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This paper investigates a countermeasure model between a multistatic multiple-input multiple-output (MIMO) radar network and multiple targets in the presence of continuous surface clutter. As a member of the multistatic MIMO radar network, the main purpose of each radar is to minimize its transmit power under a certain target detection criterion. Based on the selfishness of each radar, a strategic non-cooperative game (SNG) framework between MIMO radars is constructed. For the SNG between MIMO radars, the existence and uniqueness of the Nash equilibrium (NE) solution are proved strictly. And then, an iterative power allocation strategy for each MIMO radar is developed using optimization theory. The receive beamformer weight vectors of the multistatic MIMO radars are obtained by minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV) to suppress cross-channel interferences, respectively. Furthermore, two joint beamforming and power allocation game algorithms are proposed, which converge to the NE of the game. Finally, in order to illustrate the superiority of the proposed algorithms, we compare them with the relevant game algorithm. Numerical results are provided to show the advantages of the proposed algorithms in terms of power allocation and interference suppression.

Forward scanning SAR (FS-SAR) has recently been presented as a new mode of imaging for automotive radars, providing enhanced azimuth resolution. FS-SAR has been extended to imaging moving objects. However, it is assumed that the radar motion has been pre-compensated. Therefore, image reconstruction and focusing have to deal with the movement of the target objects only. In practice, it is not easy to compensate for, especially, complex radar motion, perfectly. Therefore, in this paper, we modify the erstwhile approach of imaging moving targets with FS-SAR to allow for radar motion without compensation. Instead of a joint, image reconstruction and image/matrix decomposition, approach, we first reconstruct the target image for every aperture position and then apply matrix decomposition to separate moving targets from clutter/stationary objects. We show, via two FS-SAR reconstruction algorithms, namely, the compressed sensing based back-projection and the modified back-projection, that the proposed approach provides the benefits of improved imaging without the need for radar motion compensation. Real-data experiments corroborate the proposed methodology.

Vehicular cooperative autonomy characteristics such as adaptive platooning and collision avoidance are enabled only through the capability to reliably exchange, at multi-Gbps speeds, an ever growing quantity of data that are being generated by light detection and ranging (LIDAR), HD video, radar, and other sensors. Due to its high bandwidth availability, the mmWave communication channel is expected to act as the required, underpinning technological enabler. In this paper, a tractable analytical model for an in-lane routing scheme that approximates the coverage, rate coverage and an adaptation of area spectral efficiency of mmWave urban Vehicle-to-Vehicle networks is proposed. The analytical model is proposed for three different path loss behaviour scenarios, namely, Line-of-Sight, Non-Line-of-Sight, and Obstructed-Line-of-Sight. Each scenario is based upon corresponding, previously reported, experimental mmWave measurements and path loss models. It is shown that Non-Line-of-Sight behaviour provides the best performance in coverage, but the lowest reliability. Moreover, the careful choice of link distances, i.e. forcing communication to be limited to the nearest vehicle, removes the sensitivity of the system to interferences from increased vehicle density, which is an important result to be considered in dense urban networks. Additionally, it is found that narrowing the beamwidth significantly improves the performance, which is the result of eliminated interferences, rather than a corresponding increase in antenna gain. The results of this research will impact both communications systems infrastructure designers and vehicle manufacturers looking to balance system performance in the investigated scenarios.

With the wide application of large-scale antenna arrays in the field of radar, sonar and wireless communication, direction of arrival (DOA) estimation based on the general asymptotic theory (GAT) has received more and more attentions recently, which can effectively conquer the inconsistent estimation problem of sampling covariance matrix (SCM) in the scenarios that the number of sensor M is large and of the same order of magnitude as the number of snapshots N . From the GAT perspective, a new DOA estimation method (termed as SEMDT-M-MUSIC) in colored noise is investigated in this paper, which mainly comprises three steps, the first step is to obtain an improved SCM through covariance matrix shrinkage estimation, the second step is to eliminate the influence of the colored noise via the matrix difference technology, and the last step is to enhance the DOA estimation via the modified spectral function, where the phase transformation result of the spike covariance matrix is employed. Numerical simulations validate the effectiveness of the proposed method.

Radar Digital Receiver (RDR) requires to perform many computationally intensive operations in real-time. Therefore, RDRs have been implemented using DSP (Digital Signal Processor) or high performance FPGA (Field Programmable Gate Array) based customized hardware and software, and optimized for heavy math computation. These designs have a few limitations like long development cycle and high implementation cost due to customized hardware and proprietary software including development tools, Board Support Package and Operating System (OS). A new design is proposed to overcome these limitations by making use of low-cost COTS (Commercially Off The Shelf) hardware containing multi-core processors that are enabled to perform complex math efficiently, open source software and ready to use Intel’s IPP (Integrated Performance Primitives) library of functions. A prototype RDR for tracking application is developed and evaluated by integrating with an S-Band Radar and tracking various targets. The details of design, implementation and performance evaluation of the prototype RDR are presented in this paper.

For high speed maneuvering platforms, multichannel synthetic aperture radar (SAR) can realize wide-swath imaging more flexibly at a high squint. In this mode, the signal reconstruction and imaging is a challenging task because the direction of the channel array vector is time-variant and inconsistent with the radar velocity vector. In this paper, the properties of space time spectrum are analyzed in detail at first. It is found that the space time spectrum of the signal is irregular, in which the space time spectral lines are nonlinear and there is a massive Doppler spectrum shift. Therefore, a range-dependent signal reconstruction method based on space time spectrum correction is proposed to obtain the unambiguous Doppler spectrum. For wide-swath data processing, an improved Omega-K approach based on time domain spectrum compression is further proposed to obtain a well-focused image. A modified Stolt mapping is used to address the range variations of range cell migration (RCM). Subsequently, a time domain spectrum compression function is used to eliminate the time domain aliasing of small-aperture data without zero-padding. Simulation results and real data processing are presented to validate the proposed algorithm.

In this paper, the problem of low angle estimation for multiple-input multiple-output (MIMO) radar is considered. Though the refined maximum likelihood (RML) algorithm can be directly applied to low angle estimation in MIMO radar, its computational burden is huge due to the large virtual aperture. Moreover, the conventional MIMO radar employs full waveform diversity, which actually sacrifices the emission gain. To reduce the computational burden and improve the transmit gain, a reduced-dimension RML method for transmit beamspace-based (TB-based) MIMO radar is developed. Firstly, the transmit beamspace (TB) matrix is designed to focus the transmit energy within the desired spatial sector. Then, to reduce the computational complexity and retain the estimation precision of the direct RML (D-RML) method for TB-based MIMO radar, a transmit-receive separate RML (TRS-RML) method is developed by applying the RML algorithm to the transmit and receive side respectively. Moreover, we propose to fuse the estimation results of the transmit and receive side together to improve the estimation precision. Theoretical analysis shows the TRS-RML method requires much lower computational burden than D-RML method. Numerical experiments show the proposed method has higher estimation accuracy compared with the D-RML method for the conventional MIMO radar in most angle region.

A novel and compact reconfigurable quad-port orthogonal Multi-Input-Multi-Output (MIMO) antenna is proposed for Dedicated Short Range Communication (DSRC), WLAN, RADAR and Ku-band applications. The proposed MIMO antenna is fabricated on FR-4 substrate of dimensions 40 × 40 × 0.8 mm3. Each element of MIMO antenna has attained different Ku-band frequencies and lower frequency operating band 5.5–9.2 GHz suitable for DSRC, WLAN and RADAR applications is obtained by M-3 element of proposed MIMO antenna for diode ON condition in ground plane. Individual MIMO element is investigated with respect to S-parameters, isolation, current distribution, radiation pattern, gain, radiation efficiency, Total Active Reflection Coefficient (TARC) and diversity performance parameters like Envelope Correlation Coefficient (ECC), Diversity Gain (DG) and Mean Effective Gain (MEG). Overall gain of MIMO antenna ranges between 3.36 and 7.57 dB in operating bands and radiation efficiency ranges between 70.4 and 89.7%. Time domain characteristics are analyzed using group delay with respect to all ports and group delay is less than 1.5 ns. ECC values are below 0.05, DG is very close to 10 and MEGi

In this paper, a novel direction of arrival (DOA) estimation algorithm is proposed for monostatic multiple-input multiple-output (MIMO) radar. Due to redundancy of the received data of monostatic MIMO radar, the computational complexity of the inversion of the covariance matrix is increased. In order to reduce the large computational complexity of existing subspace-based algorithms, we perform two reduced-dimensional operations. First, the high-dimensional received data is transformed the low-dimensional received data through a special transformation matrix. Second, the low-dimensional received data is transformed into beamspace. The receive beamspace filter is designed using convex optimization, and it can flexibly control the bandwidth and limit the sidelobe level. To ensure that the converted noise is Gaussian white noise, the prewhitening process is performed. Based on the above precondition, the reduced-dimensional beamspace technique can be effectively combined with the unitary ESPRIT model. Finally, the Cramer-Rao bound (CRB) on angle estimation in element space and reduced-dimensional beamspace is calculated for performance analysis. Numerical simulations verify the effectiveness of the proposed algorithm.

Transmit sequence design plays a significant role in multiple-input multiple-output (MIMO) radar to suppress correlation function side-lobe levels. This paper investigates two practical constraints, namely, constant envelope constraint to reduce the effect of nonlinear amplifiers, and low side-lobe levels to reduce the effect of interference among various transmitting sequences. To facilitate this, new Legendre sequences (L-seq) are designed that uses Legendre orthogonal polynomial (LOP). Superiority of the proposed L-seq is proven for peak side-lobe level (PSL) and cross-correlation level (CCL) performance parameters. Further, the signal cross-interference in MIMO radar is minimized by generating orthogonal L-seq. The impact of designed L-seq on the delay-Doppler plane is observed using the ambiguity function (AF). Simulation results show that the proposed L-seq has a constant envelope and suppressed side-lobe levels compared to the existing popular methods.

The joint use of the OFDM waveform for radar as well as for communications has gained high attention recently. One problem that arises from using this waveform is the high spur level that appears on the range-velocity profile where it may eclipse an existing target of relatively small radar cross-section (RCS). This spur is due to nulling some subcarriers such as the DC-subcarrier. Another problem that results from using this waveform is the small maximum range which is far less than the unambiguous range. In our paper, we propose two diverse solutions to overcome the aforementioned problems. The first is based on using the Autoregressive (AR) model to address the problem of the high spur level. The second relies on repeating the same QAM modulated symbol through some subcarriers of the same OFDM symbol to address the problem of the relatively small maximum range. Our analysis and simulations show that applying our proposed methodology contributes to efficiently reducing the spur level from one side, and extending the maximum range to the limit of the unambiguous range from the other side. Furthermore, a trade-off is offered between the data rate of the communication system and the maximum range of the radar.

Radar system with high range-resolution can be obtained through discrete frequency-coding waveform (DFCW). In this paper, we investigate the design process of DFCW using nonlinear chirp (DFCW-NLC), with improved autocorrelation sidelobe peak (ASP) and cross-correlation peak (CCP) levels, to attain radar waveform diversity. In this context, we present a parameterized waveform design model and derive NLC functions for designing the required NLC waveform, thereby simplifying the waveform design process. We derive analytical expressions of autocorrelation and cross-correlation functions for the NLC waveform. To achieve a waveform with desired properties, we present an optimization process based on genetic algorithm, and the optimization of parameterized NLC waveforms attains improved ASP performance over existing designs. Subsequently, we propose the design of a set of DFCW-NLC, double the number as compared to the reported designs. We derive analytical expressions of ambiguity function to investigate waveform performance. We perform a joint optimization of frequency hopping sequence and associated chirp-pulse type of the DFCW-NLC. Numerical results prove that the designed DFCWs-NLC achieve significant improvement in ASP and CCP levels of ≈ 5.0 dB and ≈ 5.47 dB, respectively in comparison with the state-of-the-art designs, and makes the proposed waveforms a superior candidate for multiple-input-multiple-output (MIMO) and multiuser radar applications.

The frequency diverse array multiple-input multiple-output (FDA-MIMO) radar can detect target range by exploiting a small frequency offset across the transmit sensors, which can be utilized to jointly estimate angle and range. Nevertheless, the detection performance is basically restricted by the array aperture and signal bandwidth. In this paper, we propose a joint optimization design scheme for FDA-MIMO radar, i.e., the virtual coprime planar array with ‘unfolded’ coprime frequency offsets (VCPA-UCFO) framework, for 3D localization without ambiguity. The VCPA-UCFO framework can significantly save sensors and physical space while remarkably extend array aperture and signal bandwidth, which brings about remarkably enhanced economic benefits and estimation performance. Additionally, we construct the 3D localization problem as 3D-MUSIC spatial spectrum function and transform the 3D total spectrum search of the conventional 3D-MUSIC algorithm into 1D local spectrum search by cooperating ESPRIT algorithm and twice reduce dimension MUSIC (TRD-MUSIC) algorithm. The TRD-MUSIC algorithm can significantly relieve computational burden but with no performance degradation. The CRBs are given as performance benchmark. The analysis and simulations have verified the effectiveness and advantages of VCPA-UCFO framework and TRD-MUSIC algorithm in system cost, localization accuracy, resolution and computational complexity.

Feature extraction plays an essential role in radar automatic target recognition (RATR) with high-resolution range profiles (HRRPs). Traditional feature extraction algorithms usually ignore that different regions in HRRP contain the information with different importance, resulting in their inadequacy in characterizing HRRP data. In this work, we propose a region factorized recurrent attentional network (RFRAN) for HRRP-RATR by making use of the temporal dependence through recurrent neural network (RNN) and automatically finding the informative regions by a deep clustering mechanism in HRRP samples, which reflects the distribution of scatterers in target along range dimension. Specifically, we represent the temporal RNN hidden state using a region factorized encoder whose parameters are conditioned on the HRRP region cluster centers. Moreover an attention mechanism is used to weight up the different recognition contribution of each time step’s hidden state. The aim of all the above modules is to achieve a more informative and discriminative feature. Crucially, the loss function of RFRAN is differentiable, so all components can be jointly trained with a gradient-based optimization. Compared with traditional methods, besides the competitive recognition performance, RFRAN has a promising interpretability thanks to the sequential region-specific hidden states.

There are several solutions to measure the tank level in industrial applications. However, the environmental conditions inside this tank, such as turbulence and foam, can jeopardize measurement accuracy and precision. This article proposes a methodology to identify the presence of turbulence and foam in a fermentation tank. The proposal is based on the extraction, selection, and classification of statistical features by machine learning methods. The use of machine learning strategies and statistical features guarantees the necessary robustness and generality for industrial applications. Actual data obtained from a must fermentation tank of a sugar-alcohol industrial plant were used for training and verifying one Artificial Neural Network-based and three Support Vector Machine-based classifiers. These classifiers obtained accuracy over 98% for different environmental conditions proving the effectiveness of the proposed methodology.

The multi-band signal fusion method is a practical and effective technique to improve the range resolution of the radar. This paper proposes a multi-stage approach based on the Generalized Likelihood Ratio (GLR) test for estimating the target range profile using sparse subband measurement data. The proposed subband fusion algorithm estimates, and compensates for the incoherent factors between the subbands, in the first step. In the second step, by combining the data of subbands, the model parameters and the number of scattering centers are estimated based on the Geometrical Theory of Diffraction (GTD) model. Finally, based on the initial values of the model parameters and the incoherent factors obtained in the preceding steps, the GTD model parameters, and the incoherence parameters are re-estimated simultaneously based on the GLR test to improve the parameters’ accuracy. In comparison to the conventional methods, where the incoherence parameters and the model parameters are independently estimated, the accuracy of the estimation of the parameters in the proposed method has improved with the simultaneous estimation of these two types of parameters. Also, the proposed method does not require prior information about the number of scattering centers. The validity and performance of this algorithm have been investigated with analytical and simulated data.

In this paper, we propose a cognitive radar paradigm based on frequency diverse array (FDA), which allows flexible spectrum control via element-wise transmit power allocation. As an emerging array technique, FDA differs from conventional phased array (PA) in that it imposes an additional frequency increment across the array elements. The use of frequency increment provides the FDA radar with the ability of flexible spectrum adjustment. We propose the cognitive FDA radar for target tracking in spectrally dense scenarios. Two optimization criteria, i.e., signal-to-interference-plus-noise ratio (SINR) maximization and Cramer-Rao bound (CRB) minimization, are employed to adaptively update the array weight vector for power allocation at each transmission. Numerical results show that the proposed cognitive FDA radar can adjust the signal spectrum to avoid the interfered frequencies for better output SINR. The resulting tracking errors of FDA radar with adaptive power allocation are lower than that for fixed power allocation. Moreover, the CRB criterion further improves the tracking performance.

With the wide application of unmanned aerial vehicles (UAV) in industrial production, transportation, and entertainment, it is urgent to identify UAVs in time. Traditional UAV recognition mainly depends on wireless communication, which puts forward high requirements for a communication environment and has no way to deal with non-cooperative targets. Therefore, it is urgent to explore a UAV target recognition scheme based on perception. In this paper, aiming at the time series preprocessing method, a coding-based sequence preprocessing method is proposed. This method effectively improves the effect of the Deep Learning method in the identification task. In order to verify the ability of Deep Learning in radar time series data processing and the effectiveness of the proposed method, the Deep Learning method is used to analyze the radar signal time series of the target to realize the target recognition. Finally,considering the influence of micro-motion factors on UAV targets, the neural network is used to estimate UAV’s micro-motion parameters to enhance the ability of target recognition with the help of micro-motion information.

In this paper, we propose a mutual interference alignment (IA) method for interference elimination in co-existing multiple-input multiple-output (MIMO) radar and multi-user MIMO communication systems, namely, radar-communication systems. Traditional IA methods proposed for communication systems cannot be directly adopted in the radar-communications due to failure to fulfill the requirements of radar target detection. In contrast, our proposed mutual IA method can efficiently improve the signal-to-noise ratio (SNR) needed by radar functionalities without reducing the degrees-of-freedom (DoFs) of communication system, which implies no degradation for the communication performance. The algorithm idea is first to align all interferences at every receiver to one signal space, then the interferences can be easily canceled by receive filter. It is examined by the Neyman-Pearson (NP) test for radar functionality, while the communication performance is evaluated by DoFs and bit error rate (BER) analysis. Both theory analysis and numerical results are provided to validate the effectiveness of the proposed mutual IA method for co-existing radar and communication systems.

In the field of radar automatic target recognition (RATR), the high-resolution range profile (HRRP) has received intensive attention. Bar a few exceptions, almost all HRRP-based ATR classification systems ignore the phase of the HRRP when the data is input to the classifier, relying instead only on the magnitude of the complex HRRP samples. This approach ignores the phase of the complex HRRPs, which reduces the information in the signal. In this paper, we develop a novel class factorized complex variational auto-encoder (CFCVAE) to utilize the phase of the high range resolution (HRR) radar echo for recognition. The CFCVAE is a complex-valued neural network (CV-NN) consisting of the encoding and decoding modules. In CFCVAE, the encoding module projects the observed data into the latent space, and then the latent features are fed to the decoding module, which further maps the latent features to data. In particular, the decoding module introduces the class labels to partition the whole observations into some parts, each of which is depicted by a specific class-decoder. Compared with the traditional variational auto-encoder (VAE) containing a single decoder, the CFCVAE can give a more accurate description to the whole dataset via multiple class-decoders, thus improving the characterization ability of features. In addition, based on the class labels, the CFCVAE employs the conditional prior on the latent variable to enhance the discrimination of features. Moreover, a complex backpropagation algorithm is derived for CFCVAE training, and a sample is classified to the class corresponding to the class-decoder with the minimum reconstruction error in the test stage. Experimental evaluations on the measured data indicate that the proposed method indeed achieves very promising target recognition performance.

The ionosphere is the propagation medium for radio waves transmitted by an over-the-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, namely ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm.

A smaller number of transmit antennas can reduce the array system cost, while sparse antenna design can provide additional degrees of freedom to beampattern synthesis. It is also known that the min-max beampattern matching design criterion can synthesize beampattern with small mainlobe ripple, low peak sidelobe level, and specified null/notch. Based on this design metric, we formulate a unified sparse array design framework for beampattern synthesis. Our framework includes the colocated multiple-input multiple-output (MIMO) radar transmit beampattern synthesis, phase-only beampattern synthesis, and phased-array beampattern synthesis as special cases. In the design formulation, the antenna positions and phase-only waveforms (for MIMO radar beampattern) or array weight vector (for phase-only and traditional phased-array beampattern) are jointly determined. The resultant problem is challenging because the nonconvex nonsmooth min-max objective function, and phase-only/unimodular requirements make it become typically NP-hard, and the antenna position selection results in a difficult Boolean constraint. Nevertheless, we are able to find a local optimal solution efficiently by applying the Lawson approximation and majorization-minimization. Numerical examples show that the devised approach can achieve the desired transmit beampattern by jointly optimizing the phase-only waveforms/weight vector and antenna locations.

Generalized Radon-Fourier transform (GRFT) is a classical long-time coherent integration method for radar maneuvering target detection. GRFT, whose core is to achieve motion parameter estimation via searching, can almost reach the optimal detection performance but heavily suffers from the high computational cost. Motivated by the fact that motion parameter estimation is essentially a non-linear mapping from the radar echo to the target’s motion parameters, one can use a deep neural network (DNN), a kind of modeling tool that can learn complex mappings from training data, to directly realize this mapping, thus alleviating the heavy computational burden brought by the searching efforts. Based on this idea, a DNN-aided long-time coherent integration algorithm, which can be viewed as a fast implementation of GRFT, is proposed in this paper. More specifically, we first use a pre-trained DNN to roughly estimate the motion parameters of the target to be detected from the radar echo, and then accomplish the coherent integration of the target for detection via a fine grid search in the neighborhood of the obtained rough estimation results. Finally, simulation results demonstrate that the proposed algorithm can achieve the detection performance close to that of GRFT but with a much lower computational cost.

Mainlobe deceptive jammer suppression is a critical issue for radar systems. To address this problem, the element-pulse coding (EPC) scheme is proposed in multiple-input multiple-output (MIMO) radar. In EPC-MIMO radar, a novel phase modulation is performed in the joint transmit element and pulse domain, resulting in an equivalent spatial frequency offset corresponding to different pulses. In such a way, the true target and mainlobe deceptive jammers, which are generated after a relatively large time delay, are distinguishable because they have different equivalent spatial distributions in the joint transmit-receive spatial frequency domain. In this respect, by selecting an appropriate coding coefficient, the mainlobe deceptive jammers can be suppressed by nulling at the equivalent transmit beampattern of the true target. Moreover, a preset beampattern synthesis (PBPS) method is proposed to enhance the robustness of jammer suppression against direction mismatches by broadening nulls around the jammers and generating a flat-top mainlobe. At the analysis stage, both simulated and measured results are provided to corroborate the effectiveness of the mainlobe deceptive jammer suppression with the developed methods.

To reduce the consumption of spectrum and hardware resources, three methods are proposed to design the integrated radar and communication waveforms transmitted by a shared array. First, a minimum norm optimization (MNO) method is proposed to devise the transmit waveforms that can simultaneously synthesize desired radar and communication waveforms in different directions. The transmit beampattern formed by the designed waveforms has low sidelobe level (SLL). However, the designed waveforms may have high peak-to-average power ratio (PAPR). To reduce the PAPR, an iterative optimization (IO) method is proposed to devise the transmit waveforms with constant modulus. Then, to further reduce the SLL of the transmit beampattern formed by the designed constant modulus waveforms, the amplitude of each transmit waveform is iteratively optimized, which is referred to as iterative optimization with amplitude weighting (IO-AW) method. Finally, several numerical results show that the transmit waveforms designed by the proposed methods can simultaneously perform the radar and communication functions and can form the transmit beampattern with low SLL.

This study shows an analysis of data from measurements of natural cloud-to-ground lightning performed in Austria between 2009 and 2018. The measurement system consists of a high-speed Video and electric Field Recording System (VFRS). Over the whole period, 735 negative cloud-to-ground flashes were recorded at 33 different locations on 61 individual days. The measurement locations are scattered across the Austrian territory in Alpine and pre-Alpine terrain. Data from the Austrian Lightning Location System (LLS), ALDIS, are correlated with the collected VFRS ground truth data to complete the dataset. These datasets are used to analyze possible reasons for the detected variation of single-stroke flashes in Austria. The percentage of single-stroke flashes among all negative flashes is higher in this study (27 %) than in previous studies from different countries (12 to 24 %). A classification of thunderstorms does not show any dependency of the single-stroke flash occurrence on different thunderstorm types (based on radar data) or underlying meteorological characteristics (based on vertical wind shear computed from weather stations and radiosondes). In contrast, a possible dependency of the occurrence of single-stroke flashes on underlying terrain (Alpine versus pre-Alpine) is noted.

Achieving a fair balance between accuracy and computational complexity of limited measurement signals by algorithms for the bistatic multiple-input multiple-output (MIMO) radar under unknown noise effect has been a seemingly difficult task for most covariance methods. In this paper, the aim is to present an efficient method to achieve an improved estimation of the joint direction of departure (DOD) and direction of arrival (DOA) for the bistatic MIMO radar with an unknown ‘Toeplitz’ colored noise effect. First, by taking advantage of the static property of the noise effect, a tensor reconstruction-based imaginary Hermitian matrix is developed to eliminate the unknown noise effect. Then the 2D angle estimation problem is then reduced to a 1D sparse recovery problem where the target sparsity is exploited. Further, we formulate a reverse 1D pairwise Simultaneous Orthogonal Matching Pursuit and sparse Bayesian learning algorithms to reconstruct the sparse signal and estimate the joint DOD-DOA of the target. In contrast with the existing tensor-based methods, the proposed approach not only is robust to the influence of Toeplitz colored noise, resolves targets with limited measurement data but also ensures superior performance with lower computational complexity. Numerical simulation conducted under varying conditions verifies the effectiveness of the proposed approach.

In this article, a novel metasurface absorber is designed for RCS reduction as well as high isolation in a 4-elements MIMO antenna. A unit cell of metasurface absorber is amalgamated by concentric circular and elliptical-shaped rings with four 300 ohms lumped resistances. Absorbance at the intended frequency band (i.e. 8.75 GHz-9.00 GHz) of metasurface absorber is more than 90.0% and its reflectivity is tending to zero. Therefore, the isolation between antennas due to this absorbance is improved by 12 dB, and the total isolation of the antenna is achieved less than 23 dB. Similarly, the Radar cross-section (RCS) of the antenna is also significantly reduced by 10dBm2. This MIMO antenna with an absorbing structure is fabricated on a richly existing FR4 substrate with dimensions of 55 × 40 × 1 mm3. The performance of the designed MIMO antenna is also judged by diversity parameters similar to Envelope correlation coefficient (ECC), Directive gain (DG), Mean effective gain (MEG), Channel capacity loss (CCL), Total Active Reflection Coefficient (TARC), and channel capacity, etc. for the proposed frequency band. The simulated and measured ECC of the proposed MIMO is less than 0.073 which exhibits that this antenna is suitable for military application in radiolocation and navigation.

Detection of unmanned aerial vehicle (UAV) is a challenging problem for passive bistatic radar (PBR) because of its low radar cross-section (RCS). Range migration (RM) will occur within one long coherent processing interval, which makes it difficult to increase coherent integration gain and improve radar detection ability. In order to eliminate the RM, a novel coherent integration method based on stretch processing and chirp-z transform (CZT) in the frequency domain is proposed. Firstly, stretch processing is applied to correct RM. Then, CZT is performed to mitigate the residual range offset and achieve coherent integration of the target's energy according to each Doppler cell. The advantage of the proposed method is that it can obtain superior coherent integration and detection performance compared to existing methods. Numerical and experimental results are presented to demonstrate the effectiveness of the proposed method.

The output signal-to-clutter-plus-noise ratio (SCNR) of space-time adaptive processing (STAP) decreases due to the dispersion of the transmit energy for traditional airborne multiple-input-multiple-output (MIMO) radar. Moreover the sufficient training samples cannot be provided to estimate the clutter covariance matrix (CCM) in the non-stationary environment. To solve these problems, a novel STAP method based on the atomic norm minimization (ANM) for transmit beamspace-based (TB-based) airborne MIMO radar is proposed. Firstly, the signal model of TB-based MIMO-STAP is established, then the optimizing principle based on the ANM is presented to design TB matrix used to focus the transmit energy in a certain spatial sector. Moreover, the beampattern corresponding to the TB matrix is close to the desired beampattern in different working modes. Meanwhile, to further reduce the number of the training samples, an accurate CCM estimation with the low-rank property is yielded by applying the ANM theory into the TB-based MIMO-STAP. Compared with the conventional MIMO-STAP with the orthogonal waveforms, simulation results show that the output SCNR of TB-based MIMO-STAP is improved. Moreover, since the CCM of the proposed method is estimated in continuous domain, the off-grid problem can be avoided. Thus, the clutter suppression performance is superior over the conventional sparse representation based STAP (SR-STAP). Meanwhile, simulation results show that the performance of the proposed method is close to that of the statistical classical MIMO-STAP and outperforms that of SR-STAP. Furthermore, it can be shown from the simulation results that the clutter suppression performance is still robust when the low-rank property of the CCM is destroyed.

Underground longwall mining activities usually induce three-dimensional (3-D) multi-level surface deformation, which could lead to serious natural hazards. Offset-tracking (OT) and differential interferometric synthetic aperture radar (DInSAR) technology (referred to as OTD-InSAR) can be used to observe the large-level and small-level mining deformations based on single InSAR pair, but the measured deformation is only along the radar line-of-sight (LOS) or the azimuth directions rather than in the 3-D directions. The conventional InSAR-based methods to resolve 3-D mining deformation have the strict restrictions and low south-north accuracy. In this paper, a novel methodology is proposed to utilize a multi-level deformation fusion model and the Lagrange multiplier method based on single OTD-InSAR pair (hereafter called MAOTD-InSAR) to estimate and optimize the 3-D multi-level surface deformation. The Daliuta coal mining area of China was selected to verify the feasibility and precision of the proposed method using high resolution TerraSAR-X (TSX) SAR images with six pairs. Results show that the root mean square errors (RMSEs) of MAOTD-InSAR against in-situ measured data are less than 0.2214 m, 0.1612 m and 0.1880 m in the east, north and vertical directions, respectively. The minimum RMSE in the horizontal direction could reach 0.0361 m with an improvement of about 82.6%, which particularly improve the accuracy in the south-north direction for the TSX satellite since its direction of azimuth deformation is approximately north direction, and this is also adequate for the other satellites in the near-polar orbit. In addition, an empirical model was generated with respect to the gradients of ground fissures on horizontal deformation, and this model enabled us to remove large errors caused by ground fissures. The verification results show that the proposed method provides higher precision and reliability for the resolved 3-D multi-level surface deformation, which presents 3-D mining deformation with more details from the edge to the center of the subsidence basin and will show some possibilities for new findings and interpretations in the process of coal mining.

With good electronic counter-countermeasures capability, random pulse repetition interval (PRI) radars receive increasing attention recently. However, the problem of maneuvering target detection in random PRI radars is rarely studied yet. In this problem, the difficulty lies in not only the range migration (RM) and Doppler frequency migration (DFM) effects but also the non-uniform sampling pulses. This paper proposes a novel algorithm for this problem. In the proposed algorithm, we first combine the non-uniform resampling operation and the keystone transform to propose the resampling-keystone transform, which can eliminate the RM and resample the non-uniform sampling pulses into uniform ones in one step. Then, the dechirp process, whose implementation can benefit from the fast Fourier transform, is employed to accomplish coherent integration for target detection by compensating the DFM. The proposed algorithm is applicable for both single-target and multi-target scenarios. Besides, the available integration time and computational complexity of the proposed algorithm are analyzed. Finally, simulation results are given to show that the proposed algorithm can approach the optimal detection performance with a much lower computational cost than the well-known generalized Radon Fourier transform.

Statistical performance of the Bernoulli Track-Before-Detect (TBD) algorithm for maritime radar is superior to that of the Bernoulli tracking filter which processes, in a conventional manner, the point measurements (PM) produced by a detector. However, the Bernoulli TBD is orders of magnitude computationally more intensive. In this paper we develop a hybrid algorithm that takes advantage of the amplitude information (exploited by the TBD algorithm), but operates in the framework of conventional point-measurement Bayesian tracking. The resulting filter is formulated using the Rao-Blackwell decomposition: a particle filter estimates the target amplitude, while the target position and velocity, given the amplitude, are estimated using a Gaussian mixture filter. Numerical results show that the statistical and computational efficiency of the new hybrid filter is between the two extremes, namely, the Bernoulli TBD and the Bernoulli PM tracker.

Controlling electronic devices by touchless methods is a promising research field due to its application in healthcare, lifestyle monitoring, and human-computer interaction, providing users with more convenience and public safety. However, most of the functions are realized by capturing visual information using camera or radar sensors. Here, we demonstrate a flexible and relatively transparent touchless sensor achieving non-contact controlling on a single sensor pixel based on the surface charge redistributions under hand motions. Moreover, the optimized surface design method is analyzed and the output signals are increased by more than 200% after hierarchically nano-micro textures are fabricated on the sensing layer, providing the device with higher sensitivity. We further present the practical application of such sensors to skip or zoom a picture without laying fingers on the screen of computer, even under though-obstacle situations. This not only suggests promising applications of non-contact controlling methods but also presents a strategy for signal enhancement of near-field sensing technology.

A decision-support system for combating abalone poaching is proposed. A dynamic Bayesian network (DBN) is used to model context-based behaviour of vessels in a maritime abalone poaching situation. The context and behaviour is informed by Expert knowledge. The model is utilised for both data generation and behaviour classification. Data generation is performed by sampling in the DBN. The result is that a set of vessels are simulated in an abalone poaching situation. Several vessel classes including poaching, patrol, fishing, tourist, and recreational vessels are modelled. The generated data is intended to model surveillance data that may have been produced by sensors such as optical, infrared or radar sensors. Classification is performed using the filtering and smoothing inference methods on the DBN. A vessel class is inferred given tracked vessel data and contextual information. The purpose is to identify vessels that exhibit poaching behaviour. The novelty of this work includes a derivation of the generalised pseudo Bayes smoothing algorithm for the classification model. This smoothing algorithm is demonstrated to provide more accurate classification results than the previously proposed filtering method.

The proposed unitary parallel factor (U-PARAFAC) algorithm is based on tensor decomposition.The real-valued tensor signal model still follows a PARAFAC model.Traditional unitary ESPRIT method is firstly extended to the real-valued PARAFAC model.The proposed U-PARAFAC algorithm directly operates the real-valued loading factors instead of estimating the signal subspace. In this paper, a novel unitary parallel factor (U-PARAFAC) algorithm of estimating direction-of-departure (DOD) and direction-of-arrival (DOA) in bistatic multiple-input multiple-output (MIMO) radar is proposed. A real-valued tensor signal model is constructed by applying the traditional forward-backward averaging technique. Subsequently, the fact that the real-valued tensor follows a PARAFAC model is proved, thus the subspace-based high-order singular value decomposition (HOSVD) method can be avoided in the subsequent solving process. Furthermore, directly operating the real-valued loading factors instead of the signal subspace, traditional unitary ESPRIT (U-ESPRIT) method is firstly extended to the real-valued PARAFAC model. The new algorithm, which exploits the multidimensional structure and does not require the estimation of signal subspace, having good performance especially at low signal-to-noise ratio (SNR). More attractively, compared with classical tensor methods such as the PARAFAC algorithm and the unitary tensor-ESPRIT algorithm, the U-PARAFAC algorithm still performs well without sacrificing array aperture when targets are highly correlated or closely spaced. Additional angle pair-matching is not required. Simulation results verify the effectiveness of the proposed algorithm.

This paper deals with the forward-looking imaging problem under high-speed platform for the airborne single-channel forward-looking radar (ASFLR). Biphase-based synthetic bandwidth azimuth modulation imaging radar (Bi-SBAMIR) can be used to increase the azimuth resolution. However, the performance degrades significantly when the velocity of the platform is high. To solve this problem, a more generalized SBAMIR framework based on linear frequency modulation (LFM) waveform (LFM-SBAMIR) is proposed. The LFM waveform is more Doppler tolerant than the biphase waveform, which makes it a good choice for high speed platform. In LFM-SBAMIR, the synthetic bandwidth of the received signal has two parts: the modulation bandwidth and the Doppler bandwidth. Therefore, large synthetic bandwidth can be achieved by increasing the modulation bandwidth even in the case of small Doppler bandwidth, and high azimuth resolution can be achieved after matched filtering. Moreover, it is firstly proved that the azimuth resolution is proportional to the time bandwidth product (TBP) of the modulated LFM waveform. Finally, the azimuth resolution performance under different TBPs, SNRs and velocities are analyzed. The performance assessment conducted by resorting to simulated data, also in comparison to the previously proposed Bi-SBAMIR method, has confirmed the effectiveness of the newly proposed algorithm in high-speed platform scenarios.

Recent investigations have shown that the Pareto class of models provide a valid approximation for the statistical structure of the backscattering from the sea, for high resolution X-band maritime surveillance radar. This has stimulated the research and development of non-coherent radar detection processes for operation in such a clutter model environment. Using data from Defence Science and Technology Group's X-band radar the application of a Pareto Type I clutter model has been justified, which has facilitated the development of sliding window decision processes. However it has been found that when these detectors are applied to synthetic target detection in real data there are some issues resulting in substantial detection losses. In order to rectify this it is necessary to investigate the development of radar detection schemes under a Pareto Type II clutter model assumption. Using a transformation approach for radar detector design it will be shown that it is possible to construct detection processes that achieve the constant false alarm rate property with respect to the Pareto shape parameter, as in the Pareto Type I case, while requiring a priori knowledge of the Pareto scale parameter. Performance analysis includes application to real X-band clutter returns with synthetic target models.

Hypersonic vehicles (HSVs) offer great advantages over airplanes and satellites, owing to their hypersonic speeds and flexible trajectories. However, HSV-borne radar may suffer from performance degradation if traditional target integration methods with stop-and-go (SAG) approximation are applied; this is especially true for high resolution radar and applications that utilize long-time coherent integration. In this paper, we consider the effects of moving-in-pulse duration (MPD) to derive an HSV-borne radar signal model without SAG approximation by specifically characterizing platform motion during the transmission and reception of a pulse. An explicit formula for wavenumber domain echo is derived using the 2-D joint stationary phase method. To mitigate the pulse-dependent echo distortion induced by the MPD effect, a target integration method based on the omega-K algorithm is proposed; this method employs revised filters for bulk focusing and revised Stolt interpolation for differential focusing to improve the overall focusing quality. The paper discusses the ambiguity functions of in-range MPD echo models, and describes the performance metrics for the integration results in both dimensions, including impulse response width (IRW), peak sidelobe ratio (PSLR), and integrated sidelobe ratio (ISLR). Simulation results have verified the effectiveness of the proposed method in MPD circumstances.

Aiming at the task scheduling problem in the DAR (digital array radar), an online task interleaving scheduling algorithm is proposed. The full structure of the DAR task is explicitly considered in a way that the waiting duration can be utilized to transmit or receive subtasks, which is called the task interleaving, as well as the receiving durations of different tasks can be overlapped. The algorithm decomposes the task interleaving analysis into the time resource constraint analysis and the energy resource constraint analysis, and online schedules all kinds of tasks that can be interleaved. Thereby the waiting durations and receiving durations can be fully utilized. The simulation results demonstrate that the proposed algorithm improves the successfully scheduling ratio by 73%, the high value ratio by 86% and the time utilization ratio by 55% compared with the HPEDF (highest priority and earliest deadline first) algorithm.

The DOA estimation problem for monostatic MIMO radar is considered.A reweighted smoothed l0-norm minimization framework with a reweighted continuous function is designed for DOA estimation.The proposed method is about two orders of magnitude faster than conventional l1-norm minimization based DOA algorithms.The proposed method provides better angle estimation performance than l1-SVD, reweighted l1-SVD, RV l1-SRACV, RD-Capon and RD-ESPRIT algorithms. In this paper, a reweighted smoothed l0-norm algorithm is proposed for direction-of-arrival (DOA) estimation in monostatic multiple-input multiple-output (MIMO) radar. The proposed method firstly performs the vectorization operation on the covariance matrix, which is calculated from the latest received data matrix obtained by a reduced dimensional transformation. Then a weighted matrix is introduced to transform the covariance estimation errors into a Gaussian white vector, and the proposed method further constructs the other reweighted vector to enhance sparse solution. Finally, a reweighted smoothed l0-norm minimization framework with a reweighted continuous function is designed, based on which the sparse solution is obtained by using a decreasing parameter sequence and the steepest ascent algorithm. Consequently, DOA estimation is accomplished by searching the spectrum of the solution. Compared with the conventional l1-norm minimization based methods, the proposed reweighted smoothed l0-norm algorithm significantly reduces the computation time of DOA estimation. The proposed method is about two orders of magnitude faster than the l1-SVD, reweighted l1-SVD and RV l1-SRACV algorithms. Meanwhile, it provides higher spatial angular resolution and better angle estimation performance. Simulation results are used to verify the effectiveness and advantages of the proposed method.

Characteristics of Radar Cross Sections (RCS) in the presence of ferrite coating layer on perfect electromagnetic conductor sphere have been studied theoretically and analytically. Spherical wave vector functions were used to expand the incident, scattered and transmitted electromagnetic field components. Co and Cross polarized scattered field components have been evaluated by applying PEMC boundary conditions at both interfaces, i.e., free space- ferrite layer and at ferrite layer-PEMC sphere core. By using obtained values of the Co and Cross polarized scattering coefficients, the forward and backscattered Radar Cross Section were calculated and compared with already published literature. The effects of applied dc bias magnetic field, saturation magnetization, incident frequency, sphere radius and relative permittivity on forward and backscattered radar cross section were analyzed in graphical form.

In this paper, we address the problem of detecting a point-like target embedded in clutter characterized by a symmetrically structured power spectral density and persymmetric covariance matrix. In particular, we consider the so-called partially homogeneous environment, where the cell under test and the training samples share the same covariance matrix up to an unknown power scaling factor. At the design stage, we jointly exploit the spectral properties of the clutter and the persymmetric structure of the clutter covariance matrix to reformulate the decision problem in terms of real variables with an increased number of training samples. Then, we derive two adaptive detectors relying on the Rao test and a suitable modification of the generalized likelihood ratio test (GLRT). The performance analysis, conducted on both simulated and real radar data, confirms the superiority of the newly proposed receivers over the traditional state-of-the-art counterparts which ignore either the persymmetry or the symmetric spectrum.

This paper proposes an efficient parameter estimation method for maneuvering targets based on the discrete randomly-modulated (DRM) radar. To correct the range cell migration, the range frequency spectrum of the received echo is evenly divided into two parts, and a synthetic aligned signal is constructed by multiplying one with the conjugate of the other. Then, the aligned azimuth signal is modeled as a nonuniform linear frequency modulated (Nu-LFM) signal and a two-dimensional nonuniform symmetric correlation distribution (2D-NUSCD) is applied to uniquely present the Nu-LFM signal in the 2D frequency domain. The 2D-NUSCD is simple and only requires complex multiplication and nonuniform fast Fourier transform (NUFFT). Owing to the application of NUFFT, the searching processing is unnecessary for the nonuniformly spaced signal and the computational cost is reduced greatly. Furthermore, theoretical analysis illustrates that the proposed method is appropriate for multitargets and is an asymptotically efficient approach with good anti-noise performance. Compared to other representative methods, the proposed method significantly improves the computational efficiency on the premise of retaining the estimation performance. It is suitable for both DRM and determinate radar systems, providing broader application scope. Simulation results verify the effectiveness of the proposed method.

Polarization diverse design of the transmit waveforms based on the property of the target scattering matrix provides better performance than transmitting waveforms with fixed polarizations, especially for mainlobe interference suppression applications. In this work, we propose a radar system combines the advantages of multiple-inputmultiple-output (MIMO) systems with the advantages offered by optimally choosing the transmitter polarizations, in order to achieve a better mainlobe interference suppression performance. The polarization diversity is employed in the transmit array, while the receive array adopts 2-D vector sensors to measure the horizontal and vertical components of the received signal. The optimal transmitter polarizations can be obtained by maximizing the output signal-to-interference-plus-noise ratio (SINR) in each coherent processing interval (CPI). In addition, the discrimination method for the target and the interference is studied in this paper, along with the target scattering matrix estimation method. Simulation results demonstrate that interference can be effectively discriminated and suppressed with the radar configuration, and better interference suppression performance is achieved with the optimal transmitter polarizations.

In this paper, a PARAFAC decomposition-based algorithm is developed for joint direction-of-departure and direction-of-arrival estimation in the presence of unknown mutual coupling for bistatic multiple-input multiple-output radar. A three-order tensor is formulated which links the estimations of coupled direction matrices to the PARAFAC model. The coupling effects of the direction matrices are compensated by two selective matrices, and the angles are obtained from the estimated direction matrices. Then the mutual coupling coefficients of the transmitter and the receiver are estimated using the subspace method. Unlike existing algorithms, PARAFAC decomposition before decoupling operation results in more accurate angle estimation, which brings better mutual coupling coefficients estimation than the ESPRIT-Like and unitary HOSVD methods. The proposed algorithm does not require spectral peak searching or eigenvalue decomposition of the received signal covariance matrix, and it can achieve automatic pairing of the estimated angles. The identifiability and computation complexity of the presented algorithm are analysed and CramerRao bounds of joint angle and mutual coupling estimation are derived. Numerical experiments verify the effectiveness and improvement of our algorithm. A new method is developed for MIMO radar angle estimation with mutual coupling.The proposed method brings no virtual aperture loss.It achieves more accurate angle estimation performance than the existing methods.The proposed method is more efficient with massive antennas.

In this paper, we consider the range distributed target detection in partially homogeneous (PH) clutter which displays different statistical properties in adjacent range cells. We propose a group method that adjacent cells with slightly varying statistics are divided into a group. Given the cells group effects on deducing the generalized likelihood ratio test (GLRT), three detectors: one-step group GLRT (1S-G-GLRT), maximum a posteriori estimation group GLRT (MAP-G-GLRT) and two-step group GLRT (2S-G-GLRT) are developed. It is verified that the 1S-G-GLRT and 2S-G-GLRT are constant false alarm rate (CFAR) with respect to the scale parameter of texture and the estimated speckle covariance matrix. The experiments show that, in the simulated clutter, the three proposed detectors behave approximately similarly, all of them outperforming three existing detectors remarkably despite the effects of target models and group strategies. In the real clutter, the 1S-G-GLRT and MAP-G-GLRT have advantages over the detectors without grouping in PH real clutter. We propose a group strategy for clutter under test because the textures of compound Gaussian clutter behave statistically different in adjacent cells.Given the cells group effects on deducing the GLRT, we propose three detectors: 1S-G-GLRT, MAP-G-GLRT and 2S-G-GLRT, and verify their CFARnesses to the scale parameter of texture and to the estimated speckle covariance matrix.The three proposed detectors outperform three existing detectors remarkably in the simulated clutter, and the 1S-G-GLRT and MAP-G-GLRT behave best in the real clutter.

Driving environment perception technology is a key issue for driving assistance systems and autonomous vehicles. Driving environment information collection and information fusion and representation are two important contents of this technology. On-board sensors such as camera, radar, lidar, etc., are popularly used to collect relevant information. However, when using these on-board sensors, uncertainty arises from ignorance and errors, where ignorance is usually caused by vehicle occlusion, and errors is caused by imprecise pose estimation and noisy measurements. Increasing sensor number would decrease such uncertainty, but complexity and cost is also increased in this way. In this paper, we present a V2I (vehicle-infrastructure) based urban driving environment collection method achieved only by two sensors, i.e., a Road Side Unit camera (RSU camera) and vehicle GPS. RSU camera is used to extract lane marking information, including position and type, and vehicle position information. Vehicle GPS provide position information of vehicle. A credibility map is proposed as the compact representation of urban environment, and accept those information from RSU camera and vehicle GPS. In order to hand uncertainty caused by sensors, Dempster-Shafer theory is employed to estimate the credibility of each cell, fuse the credibility root in the information provided by RSU camera and vehicle GPS, and map onto the credibility map. The performance of this method has been validated with real-world urban traffic data.

In this paper, the direction of arrival (DOA) estimation for noncircular sources in multiple-input multiple-output (MIMO) radar is dealt with by a novel nuclear norm minimization (NNM) framework. The proposed method exploits the noncircular property of signals to extend the data model for doubling the array aperture. Then a block sparse model of the extended data is formulated without the influence of the unknown noncircularity phase, and a novel signal reconstruction algorithm based on nuclear norm minimization is proposed to recover the block-sparse matrix. In addition, a weight matrix based on the reduced dimensional noncircular Capon (RD NC-Capon) spectrum is designed to reweight the nuclear norm minimization for enhancing the sparsity of solution. Finally, the DOA is estimated from the non-zero blocks of the reconstructed matrix. Due to exploiting the extended array aperture and block-sparse information, the proposed method provides superior DOA estimation performance and higher angular resolution. Furthermore, the proposed method has a low sensitivity to the priori information on the number of sources. Simulation results are presented to verify the effectiveness and advantages of the proposed method. HighlightsThe DOA estimation problem for noncircular sources in colocated MIMO radar is considered.A RD NC-Capon spectrum is formulation to design the weight matrix via solving a quadratic optimization problem.A reweighted nuclear norm minimization framework is proposed for DOA estimation.The proposed method provides superior DOA estimation performance and higher angular resolution due to exploiting the extended array aperture and block-sparse information.The proposed method has a low sensitivity to the priori information of the number of sources.

Compressed sensing exploits the sparsity of the signal to reduce the sampling rate while keeping the resolution fixed, and has been widely used. In this paper we propose a new algorithm called adaptive p-CAMP and show its application in the sparse stepped frequency radar signal processing. Our algorithm is inspired by the complex approximate message passing algorithm (CAMP) that solves complex-valued LASSO. The following properties of the proposed algorithm make it superior to existing algorithms: (1) All the parameters of the algorithm are tuned dynamically and optimally. The algorithm does not require any information about the signal and is still capable of tuning the parameters as well as an oracle that has all the signal information. (2) Adaptive p-CAMP is designed to solve the complex-valued p-regularized least squares for 0p1. Hence, it can outperform CAMP. The performance of the proposed algorithm is verified by simulations and the data collected by a real radar system. HighlightsA new compressed sensing algorithm called adaptive lp-CAMP is proposed.The proposed algorithm can be applied to the sparse stepped frequency radar signal processing.The performance of the proposed algorithm is verified by simulations and real data.