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In this paper, we address the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar in the presence of spatial colored noise. By exploiting the temporal structure and the multidimensional inherent structure, a fourth-order cross-covariance tensor is formulated to eliminate the effect of spatial colored noise. The higher-order singular value decomposition is utilized for accurate signal subspace estimation. Thereafter, angles are obtained with the shift-invariance technique. The proposed method can achieve automatic pairing of the estimated angles without any virtual aperture loss, thus it has better estimation performance than existing methods. Extensive numerical experiments verify the effectiveness and improvement of our algorithm. HighlightsWe investigate tensor-based subspace approach for angle estimation in a bistatic MIMO radar with unknown spatial colored noise.A temporal cross-correlation tensor is constructed for de-noising.The proposed method brings no virtual aperture loss and achieves more accurate angle estimation performance than the existing methods.

We consider a multiple-input multiple-output (MIMO) radar equipped with a colocated nested array for both transmit and receive. A novel transmit subaperturing (TS) approach that decomposes the nested array into unequal subarrays is proposed to take the advantage of the degrees of freedom (DOF) provided by the nested array but without resorting to spatial smoothing. Phased-array and the conventional MIMO with omni-directional transmission (omni-MIMO) are also extended to the nested array scenario, which utilize spatial smoothing to improve their DOF. Simulation results show that with sufficient training data, the omni-MIMO with nested array achieves the highest output signal-to-interference-plus-noise ratio (SINR), while the TS-MIMO scheme outperforms the other methods when training is limited when the radar operates in a non-homogeneous environment.

Quadrature compressive sampling (QuadCS) is a sub-Nyquist sampling scheme for acquiring in-phase and quadrature (I/Q) components in radar. In this scheme, the received intermediate frequency (IF) signals are expressed as a linear combination of time-delayed and scaled replicas of the transmitted waveforms. For sparse IF signals on discrete grids of time-delay space, the QuadCS can efficiently reconstruct the I/Q components from sub-Nyquist samples. In practice, the signals are characterized by a set of unknown time-delay parameters in a continuous space. Then conventional sparse signal reconstruction will deteriorate the QuadCS reconstruction performance. This paper focuses on the reconstruction of the I/Q components with continuous delay parameters. A parametric spectrum-matched dictionary is defined, which sparsely describes the IF signals in the frequency domain by delay parameters and gain coefficients, and the QuadCS system is reexamined under the new dictionary. With the inherent structure of the QuadCS system, it is found that the estimation of delay parameters can be decoupled from that of sparse gain coefficients, yielding a beamspace direction-of-arrival (DOA) estimation formulation with a time-varying beamforming matrix. Then an interpolated beamspace DOA method is developed to perform the DOA estimation. An optimal interpolated array is established and sufficient conditions to guarantee the successful estimation of the delay parameters are derived. With the estimated delays, the gain coefficients can be conveniently determined by solving a linear least-squares problem. Extensive simulations demonstrate the superior performance of the proposed algorithm in reconstructing the sparse signals with continuous delay parameters., 34 pages, 11 figures

In this paper, we address the detection and motion parameter estimation problem for a maneuvering target with complex motions, where range migration (RM, including range walk and range curvature) and Doppler frequency migration (DFM) occur during the coherent observation time. A method based on time reversing transform (TRT) and special generalized Radon Fourier transform (SGRFT), i.e., TRT-SGRFT, is proposed to achieve the detection and motion parameters estimation. More specifically, the TRT is firstly applied to separate the motion parameters and reduce the order of RM and DFM. Then the SGRFT operation is employed to estimate part of the motion parameters. After that, the residual motion parameters could be achieved via the second SGRFT operation. The advantage of the TRT-SGRFT algorithm is that it could avoid the blind speed sidelobe (BSSL) effect and obtain a good tradeoff between the computational cost and estimation performance, in comparison with generalized Radon Fourier transform. Several numerical experiments are provided to demonstrate the its effectiveness. HighlightsThe detection and motion estimation problem for a maneuvering target with arbitrary parameterized motion is addressed.A method based on TRT-SGRFT is proposed to achieve the maneuvering target detection and motion parameters estimation.The TRT-SGRFT algorithm can avoid the BSSL and obtain a good tradeoff among computational cost, detection ability and parameter estimation performance.

This paper proposed a blind spot detection and warning system named BSDW for driver assistance under daytime and nighttime conditions. The proposed BSDW system includes system architecture platform, radar hardware system structure, intermediate frequency signal processing, radar target detection algorithms, blind spot area calibration method, system control strategy and system integration. Line frequency modulated continuous wave millimeter-wave radar was used to detect moving targets which come into the rear blind spot alarm area, including left, right and behind area of the subject vehicle. Based on Rayleigh clutter distribution model, a cell greatest, smallest and averaging constant false-alarm rate target detection algorithm named CGSA-CFAR was proposed to maintain higher detection rate and lower false-alarm rate by adjusting power detection threshold in time based on the noise power level, which was estimated according to the proposed target detection algorithm. The proposed system was implemented on embedded hardware platform and verified on the Chery Arrizo7 vehicle. Under urban daytime and nighttime conditions, the early warning rates of the proposed system were up to 98.20% and 98.21% respectively. The results show that the proposed system can detect the targets which come into the rear warning area of the subject vehicle and give early alarm to driver effectively in various urban road environments.

Human behaviour and occupancy accounts for a substantial proportion of variation in the energy efficiency pro le of domestic buildings. Yet while people often claim that they would like to reduce their energy bills, rhetoric frequently fails to match action due to the effort involved in understand- ing and changing deeply engrained energy consumption habits. Here, we present and, through dedicated experiments, test in-house developed soft-ware to remotely identify appliance energy usage within buildings, using energy equipment which could be placed at the electricity meter location. Furthermore, we monitor and compare the occupancy of the location under study through Ultra-Wideband (UWB) radar technology and compare the resulting data with those received from the power monitoring software, via time synchronization. These signals when mapped together can potentially provide both occupancy and speci c appliances power consumption, which could enable energy usage segregation on a yet impossible scale as well as usage attributable to occupancy behaviour. Such knowledge forms the basis for the implementation of automated energy saving actions based on a households unique energy profi le.

The range resolution of ground-penetrating radar (GPR) signal is important in thin asphalt overlay thickness estimation. In this paper, regularized deconvolution is utilized to analyze simulated GPR signals to increase their range resolution. Four types of regularization methods, including Tikhonov regularization and total variation, were applied on noisy GPR signals; and their performance was evaluated in terms of accuracy in estimating distance of close impulses. The L-curve method was used to choose the appropriate regularization parameter. The total variation regularization method and zeroth-order Tikhonov regularization outperform first-order and second-order Tikhonov regularization in terms of average asphalt layer thickness estimation error and the standard deviation of the error. An example of the field GPR data is provided to validate the proposed algorithm. The study shows that the algorithm based on regularization is a simple and effective approach to increase the GPR signal range resolution with presence of noise in the case of thin asphalt overlay thickness prediction. Deconvolution can be used to increase GPR signal resolution.Tikhonov and total variation regularization can make deconvolution robust to small reflection and noise.Simulation and field example show that accurate layer thickness can be obtained when the duration between two pulses is larger than 0.51ns.

Interferometry is one of the most advanced signal-processing and imaging techniques. It is widely used in geophysical detection and remote sensing. Application of interferometric processing significantly improves the resolution in geophysical imaging. This paper reports the application of an interferometric imaging approach to cross-hole multi-offset transmission (CHMOT) borehole radar data to generate a virtual single-hole, multi-offset reflection (SHMOR) profile with the validation of a real SHMOR data set. In subsurface material property imaging, SHMOR is an effective technique for interface and fracture detection. However, borehole radar survey in SHMOR fashion is not practical for most cases. Transforming cross-hole transmission mode radar data to virtual single-hole, multi-offset reflection data using a wave interferometric virtual source (WIVS) approach was proposed previously but not fully validated with real SHMOR data. In this study, we compare WIVS-derived virtual SHMOR to real SHMOR profiles using data sets acquired in two boreholes (SIMA1 and SIMA2) drilled into crystalline igneous bedrocks. As the calibration and validation, the reflection from borehole SIMA2 (as the known object) is clearly imaged by both the real and WIVS-derived virtual SHMOR profiles in SIMA1. The diffractions and amplitude decay from sharp stratigraphic change also registered at both the real and WIVS-derived virtual SHMOR profiles. The results of this study demonstrate the potential of the WIVS approach to improve structural imaging in bedrocks for hydrogeological, geothermal, and petroleum reservoir development applications. The wave interferometry virtual source (WIVS) method for getting the virtual single-hole, multi-offset reflection (SHMOR) profile by comparing with the real single-hole multi-offset radar data was verified.The comparison results show that WIVS derived SHMOR is imaged better, the heterogeneities between the original data.

The sea clutter modeling is critical to the radar design and assessment of relevant detection algorithms. In this paper, we investigate a family of generalized autoregressive conditional heteroscedastic (GARCH) processes to model the sea clutter as a time series, in which the current variance is dependent on historical information. The most general model (so-called the ALLGARCH model) provides more flexible variance structures to model non-Gaussian, asymmetry, and nonlinear properties of the clutter. However, after going through the usage of the ALLGARCH model, we find that it is not very suitable because the coefficients of the model, which are numerous, would be difficult to estimate in a real-time operating environment. Meanwhile, we find that some of the coefficients are negligible under almost all kinds of sea environments and weather conditions. Motivated by these observations, we propose a novel GARCH model for sea clutter modeling, which is a generalization of the nonlinear-asymmetric GARCH (NAGARCH) model. Considering the correlation between adjacent clutter returns, autoregressive terms are also introduced. By systematically analyzing practical sea clutter data under different sea environments, we demonstrate that the proposed model achieves comparable fitting effect to some commonly used statistical models. Also, we develop the corresponding generalized likelihood ratio test (GLRT) algorithm for the new model. Numerical simulations exhibit that the proposed detector achieves higher probability of detection, comparing with the AR-GARCH detector.

Multiple-input multiple-output (MIMO) radar with sparse planar arrays is expected to provide one snapshot imaging of complex motion targets at low hardware costs. In recent years, compressive sensing (CS) has been applied to MIMO radar imaging with limited antenna arrays. However, CS has to convert the matrix into a vector, which results in a large measurement matrix and a demanding amount of storage. Matrix completion (MC) is a new theory of recovering the entire data with partial observations. Compared with CS, MC is more suitable for matrix operations and avoids the basis mismatch problem. Due to the potential advantages, we introduce MC to MIMO radar imaging using a sparse planar array. Despite the success of MC in other fields, direct application in MIMO radar imaging leads to several problems, such as strict restrictions of the low-rank property and uniform random samples in MC. To solve these problems, a structured MC(StMC)-based method is proposed. With the method, MIMO radar imaging via a sparse planar array is converted to complete a low-rank structured matrix, which can be easily solved as a semi-definite program problem. Experimental results confirm the validity of the proposed method. Matrix Completion theory is introduced to MIMO radar imaging.Two types of sparse planar array are considered to reduce the hardware cost and the data volume.A structured MC imaging method is proposed to solve the problems in MIMO imaging with sparse planar array.

This study considers the problem of range tracking of a maneuvering target by an airborne digital array radar system. In the classic “current” statistical (CS) model, the maneuvering frequency α is not adaptive, and an acceleration limit has to be set. First, a new range tracking method called “adaptive CS model with velocity prediction” (ACSMVP) is proposed. This method utilizes the velocity prediction algorithm of cross product automatic frequency control with loop filter to measure the radial acceleration between the radar and the target. With the acceleration been obtained, the maneuvering frequency is derived, which is calculated in every tracking period. Second, an improved algorithm called “Sage–Husa-based ACSMVP” (SH-ACSMVP) is also proposed. In this algorithm, an online measuring noise covariance matrix is achieved by a simplified Sage–Husa estimator. Third, a Cramer-Rao lower bound of pulse-Doppler pulse train signal for range estimation is derived to assess the range tracking performance of the proposed algorithms. Simulations highlight the superior performance of our proposed algorithms over existing techniques.

We propose a new scheme to estimate the directions-of-arrival (DOAs) of mixed coherent and uncorrelated targets exploiting a collocated multiple-input multiple-output (MIMO) radar with transmit/receive coprime arrays. In the proposed scheme, the DOAs of the uncorrelated targets are first estimated using subspace-based methods, whereas those of the coherent targets are resolved using Bayesian compressive sensing. Compared with the previous works, the proposed approach achieves improved DOA estimation accuracy with a flexible coprime array configuration and may resolve more targets than the number of coarray elements. Theoretical analysis and simulation results validate the effectiveness of the proposed technique.

In this paper, we tackle the problem of selecting the radar node positions to provide an estimate of the target state vector with a prescribed accuracy. The topology optimization problem is formulated as selection of a fixed number of radar node positions from a set of available ones, where the radar observations are modeled by a general non-linear model. We further propose a topology optimization framework for the simultaneous estimation of the multi-modal parameter vector. In particular, the task of joint position and velocity estimation is considered. The feasibility of the proposed approach is demonstrated for several cost functions, namely the frame potential as well as the log-determinant and maximum eigenvalue of the error covariance matrix. HighlightsA number of radar applications require knowledge of the full target state vector.Localization-driven topology optimization can outperform the location- and velocity-driven one.The log-determinant cost function allows for a lower computational complexity than the frame potential cost function.The greedy optimization of the log-determinant gives better performance than the convex optimization of the maximum eigenvalue.

Adapting the space-time adaptive processing (STAP) filter with finite number of secondary data is of particular interest for airborne phased-array radar clutter suppression. Sparse representation (SR) technique has been introduced into the STAP framework for the benefit of drastically reduced training requirement. However, most SR algorithms need the fine tuning of one or more user parameters, which affect the final results significantly. Sparse Bayesian learning (SBL) and multiple sparse Bayesian learning (M-SBL) are robust and user parameter free approaches, but they converge quite slowly. To remedy this limitation, a fast converging SBL (FCSBL) approach is proposed based on Bayesian inference along with a simple approximation term, then, it is extended to the multiple measurement vector case, and the resulting approach is termed as M-FCSBL. To improve the performance of STAP in finite secondary data situation, the M-FCSBL is utilized to estimate the clutter plus noise covariance matrix (CCM) from a limited number of secondary data, and then the resulting CCM is adopted to devise the STAP filter and suppress the clutter. Numerical experiments with both simulated and Mountain-Top data are carried out. It is shown that the proposed algorithm has superior clutter suppression performance in finite secondary data situation. We study the problem of clutter suppression in STAP with finite training samples.Fast converging sparse Bayesian learning approaches are derived.A novel STAP algorithm named as M-FCSBL-STAP is proposed.The M-FCSBL-STAP has superior performance in low training support situation.

An efficient airborne single-channel forward-looking radar (ASFLR) imaging framework to improve the azimuth resolution performance with synthetic bandwidth azimuth modulation imaging radar (SBAMIR) is proposed. During the framework, we incorporate azimuth modulation to the transmitted signal with a coded sequence in azimuth direction, and derive the SBAMIR imaging model for the first time. In SBAMIR, the synthetic bandwidth of the received echo is not only determined by the Doppler bandwidth, but also by the modulation bandwidth. Even though the Doppler bandwidth has little contribution to the synthetic bandwidth in ASFLR, the modulation bandwidth can be utilized to increase the synthetic bandwidth. Accordingly, a high azimuth resolution can be achieved after azimuth matched filtering. Achievable azimuth resolution and performance to SNR are analyzed later. Simulation results are given to verify the effectiveness of the proposed algorithm. Synthetic bandwidth azimuth modulation imaging radar (SBAMIR) is proposed.Azimuth modulation will bring in additional modulation bandwidth.Synthetic bandwidth is determined by the Doppler bandwidth and modulation bandwidth.

The accuracy of the prior knowledge of the clutter environments is critical to the clutter suppression performance of knowledge-aided space–time adaptive processing (KA-STAP) algorithms in airborne radar applications. In this paper, we propose an enhanced KA-STAP algorithm to estimate the clutter covariance matrix considering inaccurate prior knowledge of the array manifold for airborne radar systems. The core idea of this algorithm is to incorporate prior knowledge about the range of the measured platform velocity and the crab angle, and other radar parameters into the assumed clutter model to obtain increased robustness against inaccuracies of the data. It first over-samples the space–time subspace using prior knowledge about the range values of parameters and the inaccurate array manifold. By selecting the important clutter space–time steering vectors from the over-sampled candidates and computing the corresponding eigenvectors and eigenvalues of the assumed clutter model, we can obtain a more accurate clutter covariance matrix estimate than directly using the prior knowledge of the array manifold. Some extensions of the proposed algorithm with existing techniques are presented and a complexity analysis is conducted. Simulation results illustrate that the proposed algorithms can obtain good clutter suppression performance, even using just one snapshot, and outperform existing KA-STAP algorithms in presence of the errors in the prior knowledge of the array manifold.

Tracking of nonstationary narrowband signals is often accomplished using algorithms called adaptive notch filters (ANFs). Generalized adaptive notch smoothers (GANSs) extend the concepts of adaptive notch filtering in two directions. Firstly, they are designed to estimate coefficients of nonstationary quasi-periodic systems, rather than signals. Secondly, they employ noncausal processing, which greatly improves their accuracy and can be applied whenever additional delay can be tolerated. The paper develops a novel performance assessment mechanism for GANS. It allows one to evaluate tracking accuracy of the smoother without prior knowledge of the true values of the system's frequency or coefficients. The extension can be employed to build a parallel bank of filters, which automatically chooses the one which is best matched to unknown and possibly time-varying tracking conditions. HighlightsThe problem of generalized adaptive notch smoothing (GANS) is considered.A blind mechanism for assessment of GANS performance is proposed.The self-optimizing GANS is proposed.Noise radar experiment demonstrates real world application.

Availability is one of the major parameters to measure the performance of Free Space Optics (FSO) communication link. In tropical climate, rainfall acts as a dominant factor that affects the FSO link availability; whereas haze has no significant effect compare with rain. But for longer FSO link distances, the effect of haze on the overall FSO link availability becomes serious and need to be considered. In this paper, availability of an FSO link is estimated under the impact of rain, haze and the combination of the two phenomena. The availability estimations based on predicted attenuation due to rain and haze using long-term statistical measurements of rain rate (mm/hr) and visibility (km) data measured in Malaysia. The effect of individual weather phenomena was demonstrated over FSO link ranges from 1 to 5 km. The combine effect of both rain and haze also has been analyzed using the cumulative distribution function (CDF) of predicted atmospheric attenuations. In tropical regions, for short FSO link distance, the influence of haze on the availability of FSO signal is negligible compare with rain; whereas for longer FSO distances, haze attenuation will reduce the overall link availability. This effect of haze needs careful consideration for the deployment of FSO systems especially in Malaysian tropical climates.

This paper presents the design of a compact Radar for real-time detection of targets in smart mobility applications. The Radar integrates Fabry-Perot resonating antennas, X-band and configurable continuous wave transceiver, high-speed analog-digital-converter and low-power/low-cost FPGA for the baseband signal processing. The latter includes region-of-interest selection, 2D Fast Fourier Transform for range-Doppler map extraction, peak detection and alarm decision logic. The transmitted power can be configured from few mW to 1.8W. This allows for a trade-off between the maximum detection range, from few hundreds of meters up to 1.54km, and the Radar power consumption, from 2.56W to 11.66W. The measured speed is up to 40m/s. The speed and distance resolutions are 0.3m/s and 37.5cm, respectively. The configurable Radar features increased robustness vs. laser-scanners, video cameras, or induction loops detection techniques, and stands for its better trade-off in terms of covered range, size, and low-power consumption vs. state-of-the-art surveillance Radars. Design of a compact Radar module for real-time detection of targets in smart mobility applications.Real-time implementation of Radar signal processing tasks on low-cost and low-power FPGA, reducing power dissipation vs. state-of-the-art designs using GPU (Graphical Processing Units) and GPP (General Purpose Processors).Radar parametric analysis highlighting the inter-dependencies existing among Radar performance and analog and digital circuit parameters. This allows a co-design of the mixed-signal Radar transceiver with the FPGA-based digital signal processing, and a trade-off between the Radar performance and its implementation complexity.Coarse-grained and a fine-grained configurability of the Radar transmitted power, which allows for different trade-offs between the system power consumption and the maximum covered range.

Knowledge-based (KB) transmit design problem is addressed for airborne Multiple-input multiple-output (MIMO) radar target detection in the non-homogeneous clutter zone. In airborne system, due to the fact that radar platform is moving, clutter has obvious time-varying property. It is vital to acquire and update clutter information in real time for airborne MIMO radar. However, there is a contradiction between transmit beampattern optimization for clutter perception and target detection. Thus, transmit optimization for both clutter perception and target detection could hardly be done simultaneously. To remedy these problems, a transmit design method for airborne MIMO radar is proposed based on the merit of MIMO radar in waveform diversity, in which the transmit array is divided into two subarrays, i.e. clutter perception subarray and target detection subarray. Then, the transmit design problem is decomposed into two subproblems, subarray partition and transmit waveform design. Based on convex optimization, subarray partition algorithm (SPA) and detection subarray beampattern design (DSBD) algorithm are presented. Numerical results show the efficiency of the proposed method. Knowledge-based (KB) transmit design for airborne MIMO radar in the non-homogeneous clutter zone is introduced.Clutter information should be acquired end updated in real time for airborne MIMO radar.Transmit array should be divided into two subarrays for clutter perception and target detection respectively.Better performance for both clutter perception and target detection can be achieved.

Several studies indicate that vegetation attenuates and scatters radio waves. Although researchers considered vegetation as a soft obstacle in terms of total attenuation, actual measurements show that trees or shrubs could induce important attenuations. This opens a possible use when looking for radar invisibility shields to protect selected targets. Is it possible to use vegetation as an invisibility cover? If so, under which conditions this procedure would work? These are the questions this paper tries to answer, analysing the degradation in the probability of detection that a vegetation cover induces in the radar system. We present the results from large measurement campaigns on attenuation due to vegetation, performed at radar UHF and microwave frequencies; then we use them to evaluate the reduction in the radar performance when trying to detect a target protected by a vegetation cover. We demonstrate that vegetation covers lead to reductions in probability of detection to values below 0.1 considering single pulse radars, which means that the target would be undetectable. We conclude that the proposal would be reliable, even taking into account other additional effects, as pulse integration, the natural time variability of the induced attenuation, and the noise increase due to the vegetation scattering.

This study explored a novel method based on eigenvalue decomposition (EVD) and independent component analysis (ICA) to separate the multi-component radar signal in the single channel. By exploiting the generalized periodicity of the radar signal, the proposed method structures the multi-dimensional matrix from the observed signal in single-channel through EVD, then applies ICA to the matrix to determine the basic waveform of each component, and finally reconstructs the component signals. Simulation results confirmed the effectiveness of the proposed method and compared it with other methods, although the performance of proposed approach is a bit worse than some other method when processing radar signals, the most outstanding advantage of the proposed approach is that it does not require any other known conditions, and it can recover the component signals with a satisfactory level, so it can yet be regarded as an effective method.

Compressive sensing and processing (CSP) performs signal acquisition and processing at sub-Nyquist rates. This makes CSP an attractive option in radar target tracking as it reduces computational load in sequential signal acquisition and processing. However, CSP is accompanied by a reduction in signal to noise ratio (SNR) which results to a deterioration of tracking performance. In order to improve tracking performance CSP can be configured using information available on the target state which is provided by the sequential estimation process. In this work, adaptive CSP is applied to a target tracking scenario. A particle filtering implementation using adaptive CSP is developed for tracking a single target. Simulation results are provided to demonstrate the improvement in SNR and in tracking a single target by adaptive CSP over non-adaptive CSP and previously proposed adaptive CSP methods. HighlightsAdaptive compressive sensing and processing is applied to a single target tracking scenario.A particle filtering method based on likelihood sampling is proposed to sequentially estimate the target posterior.A Monte Carlo based method is provided that predicts the probability of appearance of signal elements in the measurements one time step ahead in the tracking scenario.The adaptive sensing matrix is naturally configured for each radar sensor independently based on sequentially updated information on the target state within the target tracking scenario.

Hybridization of a frequency diverse array (FDA) generated range-angle-time dependent beampattern with multiple input multiple output (MIMO) radar waveform diversity, namely, FDA-MIMO radar, provides more degrees-of-freedom to improve overall system performance. As the beampattern optimization needs an efficient method to approximate the desired beampattern and simultaneously minimize the cross-correlation sidelobes, we optimize the transmit beampattern over the beamspace matrix in this paper. That is, the transmit FDA antennas are divided into multiple overlapping subarrays and each subarray transmits an orthogonal waveform towards the target, whereas the weight vectors of all subarrays jointly form a beamspace matrix. Since the optimization problem turns to be a non-convex problem with a constant energy constraint, we propose an efficient algorithm based on alternating direction method of multipliers (ADMM) to solve it, which has a very fast convergence speed. The effectiveness and superiorities of the proposed method over existing methods are verified by extensive simulation results.

In a multiple-input multiple-output (MIMO) radar, it is desired to maximize the power radiation in an angular region of interest, which, however, is hardly achievable because a set of orthogonal waveforms is employed at the engineering. Different from traditional colocated MIMO radar, space-time coding array (STCA) transmits an identical waveform with a tiny time delay circulating across array elements. It can provide a simple way to achieve the full angular coverage with a stable gain. In this paper, a new scheme referred to as chirp-STCA is proposed to synthesize the desired beampattern flexibly. With the quantitative relationship between the time delay and the span of beam spreading, the transmit beampattern synthesis issue is converted to the design of time delay value which can be readily realized based on the framework of chirp-STCA. As a result, the desired angular sector coverage of the radiation power can be realized by setting a proper time delay. The proposed chirp-STCA can provide many desired features including low range sidelobe level, low computational complexity, and constant modulus. In addition, the range-angle properties are examined by analyzing the transmit beampattern response and the ambiguity function. Numerical results are presented to demonstrate the effectiveness and simplicity of the chirp-STCA scheme for the transmit beampattern synthesis.

We present a transmit power adaptive filter-bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) waveform for joint communication and radar system. For frequency selective fading channels or frequency sensitive targets, a joint optimization problem is designed by taking both the radar detection performance and communication channel capacity into the objective function under the constraint of limited transmit power. In addition, we derive the power allocation condition where the optimal performance of radar and communication can be achieved simultaneously. Otherwise, the tradeoff between radar and communication performance is necessary by adjusting the weighting factor. The proposed algorithm can realize adaptive transmission where the parameters of the transmitting waveform can be optimally designed for the next pulse by utilizing the measured values of the current signal and the channel state information. Moreover, the feasibility and advantages of FBMC as the radar signal are analyzed from the aspects of average ambiguity function, multipath effect and Doppler shift. Simulation results verify the effectiveness of the proposed scheme. Compared with the equal power allocation, the proposed adaptive transmission power algorithm in this paper shows better performance.

Specific emitter identification (SEI) is an important problem in the field of electronic intelligence. There are two major limitations in most existing SEI methods: First, the features should be artificially extracted, which requires specialized expertise; Second, various features are not merged effectively to improve performance. In this paper, an automatic multi-feature extraction and fusion method based on deep ensemble learning is proposed for SEI. This method extracts and fuses multiple features via data-driven strategies using convolutional neural networks (CNN). No additional constraints are required on the type and number of the features to be fused. Three typical characteristics of radar pulse signals, including amplitude, phase and spectrum asymmetry, are taken as example features for method description and performance verification. Experiment results demonstrate that, the proposed method performs well in automatic feature extraction, and it can significantly improve the SEI performance via multi-feature fusion.

In this paper, a pulse compression method is proposed with the offset quadrature amplitude modulation based orthogonal frequency division multiplexing (OQAM-OFDM) signal. By establishing the received OQAM-OFDM signal as a linear model form and without inserting any cyclic prefix (CP), we propose a concise and easily implemented pulse compression method. The proposed method realizes the inter-range-cell interference (IRCI) free pulse compression perfectly, which improves the power efficiency, and the scope of observation will not be restrained by the number of subcarriers. Besides, the signal-to-noise ratio (SNR) after the pulse compression is derived. It is proved that the SNR gain for the proposed pulse compression method can reach the same as that of the method using the matched filter. Finally, numerical simulation results are provided and discussed.

This paper jointly designs the transmit and receive beamforming vectors for a multi-mission multiple-input multiple-output (MIMO) radar. The radar searches for potential targets within a prescribed region while tracking multiple targets with known locations. To improve the performance of search mission and track mission, we aim to maximize the transmit energy in the prescribed region and ensure that the received signal to interference plus noise ratio (SINR) of each target is not less than a preset threshold. To tackle the resulting NP-hard problem, we introduce an iterative algorithm based on the sequential optimization procedure. In each iteration, the receive beamforming vectors are first updated by resolving the unconstrained maximization problems. Then, a fractional quadratic constrained quadratic programming (QCQP) is developed for updating the transmit beamforming vector and is solved with semidefinite relaxation (SDR) technique. Numerical simulations verify the convergence performance of the proposed algorithm. The ability of the radar to simultaneously implement the required two tasks is also demonstrated through the synthesized transmit and receive beampatterns.

This paper considers a joint scheme of array resource allocation and transmit beamforming for radar and communication integration (RCI) capable of multiple target tracking and channel communication. The primary objective is to jointly minimize the Cramer–Rao lower bound (CRLB) of tracking filtering and Bit Error Rate (BER) of the communication subsystem. Firstly, two constrained optimization models are constructed and combined to obtain the RCI. Next, Majorization minimization (MM) is employed to convert the resulting optimization into a surrogate simpler problem, which is then solved by projected gradient descent (PGD). In addition, a transmit beampattern design problem is considered through optimizing the waveform covariance matrix under the shared and separated deployment. An appropriate beampattern is obtained based on array resource constraint, employing the PGD, while the peak power of the transmit beam is guaranteed for each target. Furthermore, iterative shrinkage-thresholding is applied as acceleration to the PGD in each algorithm. Numerical experiments are provided to illustrate the efficient performance of the joint array allocation and transmit beamforming for the RCI system, where the convergence of the algorithms is also confirmed.

With the advances in electronic countermeasures (ECMS), especially the emergence and development of active jammers, there is an urgent demand for anti-jamming techniques. In this paper, we proposed a cognitive active anti-jamming method based on frequency diverse array (FDA) radar phase center. For the uniform linear FDA (ULFDA) radar, we derive the closed form of phase center, based on which the regulation effect of frequency increments is explored through Monte Carlo test. Based on the closed form of phase center, an optimization model considering the frequency increments regulation at the fixed time is established and solved by the improved swarm-immune optimization (PSO-IMMU) algorithm to realize active anti-jamming. Finally, for the jammers that implement jamming by determining the position of target, we propose a cognitive active anti-jamming method making the radar difficult for a jammer to detect or locate during the normal operation, and for the case of moving target and fixed jamming source, the Bayesian filter is applied to realize cognitive beamforming, while for the case of fixed target and moving jamming source, the auxiliary radar is applied to predict and estimate jamming source state along with the Bayesian filter. All proposed methods are verified by numerical simulation results.

In this paper, the five fundamental issues of target extension, target maneuvering, closely spaced targets, weak targets, and dense clutter are tackled simultaneously in the framework of a new tracking approach that is capable of effectively detecting multiple closely-spaced maneuvering weak and extended targets in densely cluttered environment. The proposed technique finds its foundation in the track-before-detect (TBD) paradigm, proven to be effective in tracking weak targets in presence of dense clutter. The approach consists of two stages. In the first stage, a 3-dimensional projection based TBD algorithm allows addressing the problem of target extension, weak targets, and dense clutter. Straight line tracklets are obtained at the end of the first stage. In the second stage, tracklets association is performed to obtain the target trajectories. The issue of closely spaced targets and target maneuvering is addressed in this second stage. To illustrate the effectiveness of the proposed approach, synthetic data, real data from a netted radar system, and real data from a high resolution radar are processed both with the proposed algorithm and with several existing algorithms. The obtained results reveal that the proposed method can outperform the competing approaches in various scenarios.

This paper proposes a mainlobe clutter suppression approach for frequency diverse array (FDA) radar blind-Doppler target detection, by exploiting the Doppler-spreading (DS) effect. As an emerging array processing technique, FDA differs from conventional phased-array in that it employs a frequency increment across the array elements. When a large frequency increment is used, the FDA radar echo signal from a moving target will be spectrally spread in Doppler domain. Inspired by this phenomenon, we establish a joint range-angle-Doppler processing model for FDA radar with DS consideration. Using resolution capability analysis, we show that that this DS effect provides potentials in resolving Doppler ambiguity and meanwhile suppressing mainlobe clutters. As an application example, the proposed FDA radar model with DS consideration is used for blind-Doppler target detection in mainlobe clutters. Analytical expressions for the detection probability and signal-to-clutter-plus-noise ratio (SCNR) are derived for the proposed FDA-based target detection. Numerical results show that the proposed approach outperforms the counterparts for the FDA radar without DS consideration and conventional radars.

In this paper, a new technique is proposed for designing stealthy radome, especially for normal incoming wave stealth. The proposed structure is composed of a band-pass FSS and one layer of polarization conversion arrays which are separated by a foam layer. An FSS-backed polarization conversion meta-surface (PCM) can be obtained. The FSS-backed PCM is capable of providing a second-order transmission window and a wide stealthy band. In the transmission window (4.2∼4.6 GHz), the self-excited communicate electromagnetic wave can pass through freely with its same polarization state. In its stealthy band (7.0∼17.6 GHz), the external incoming wave can be efficiently reflected with its cross-polarized state. Thus, the radome is invisible for a linearly-polarized radar system even the detecting wave is reflected along its incoming direction. The proposed PCM is simulated and experimented. The measured results are in good agreement with the simulated ones. This stealth technique will be significance in the area of naval vessel radomes.

The fraction-of-time (FOT) framework, a pseudo-probability framework, is proposed to promote the stochastic signal processing theory into practical applications. Technically, the ensemble averaging that requires infinite realizations is replaced by the time averaging of a single realization. This skill is further generalized for cyclostationary signals and accordingly the periodic FOT framework is obtained. However, neither the FOT nor the periodic FOT is proper for the chirp cyclostationary (CCS) signals which are utilized to model most of the nonstationary signals in mobile communications, radar/sonar systems. In this paper, a novel general FOT framework is proposed, which is obtained by constructing an isomorphic map associated with fractional Fourier transform (FrFT). Specifically, a CCS signal and its fractional shift form a Hilbert space, which is isomorphic to the Hilbert space formed by the CCS stochastic processes. The chirp-period FOT framework for CCS signals with a single chirp period is obtained directly, which is further extended for CCS signals with incommensurate chirp periods. Then the statistics of the CCS signals are defined and their properties are introduced in this framework. Finally, their estimators are investigated.

This article presents research on assessing the accuracy of ship position determination based on observations of radar echoes of navigation marks identified in an Electronic Navigational Chart (ENC). The accuracy analysis of the simulated radar positioning in relation to navigation marks the validity of the research. Position accuracy on the approach fairways was better than 6.5 m (RMS). A specially prepared measuring system made it possible to conduct tests based on radar images. In subsequent stages of the research, the association of extracted characteristic radar echoes with navigation marks found in an ENC was made. Then, in order to eliminate significant values of measurement errors and erroneous identification of navigation marks, geodetic adjustment was applied. The obtained position accuracy equal to 34.1 m (RMS) provides a premise to state that the method can be adopted for autonomous navigation systems which are alternatives to Global Navigation Satellite System (GNSS).

Different transmit antenna elements in a frequency diverse array (FDA) radar transmit different frequencies. This makes the transmitted beam to continuously change its orientation, and enables an FDA to scan the space around itself in a simple manner. Furthermore, an FDA can find the range as well as the direction of each target simultaneously. The achievable resolution of direction estimation depends on the receive array geometry. The range resolution can be enhanced by decreasing the width of transmit beam, which depends on the transmit array geometry. The later property makes FDA especially attractive because it is no longer necessary to produce sharp, wideband, and high power pulses to achieve high range resolution. FDA has been studied mostly for collocated transmit and receive array, and a very little has been done on its application in bistatic radar. In this paper we propose a new signal transmission scheme for bistatic FDA radar, and demonstrate how one can detect and localize the targets accurately using a computationally efficient algorithm. The proposed signal transmission scheme is designed to produce the narrowest transmit beam for a given number of transmit antennas. This achieves significantly better detection and localization performance compared to the existing methods. Our estimation approach uses a fast algorithm to produce some initial estimates that can be used to initialize a numerical algorithm for computing the maximum likelihood estimates. We also derive the Cramer Rao bound (CRB) associated with the estimation problem, and demonstrate that the variances of the estimates produced by the proposed method are close to corresponding CRBs.

With the increasing air pollution each day, it is very important to keep a check at the air quality we are breathing in. This can be made possible by designing an environmental sensor that is low-cost, robust, readily available, and easy to manufacture. In this research work, we propose a 12-bit smart chipless radio frequency identification tag having a radius of 7.4 mm. The miniaturized chipless RFID multi-sensor is based on humidity sensitive Kapton®HN substrate with Copper as a radiator. These materials selection enable the vast adaptability of this sensor in such areas where the use of a commercial environmental sensor is not economically supportive. The frequency response of the tag contains twelve uniquely identifiable frequency resonances in a 4–18 GHz frequency band. The gas sensing characteristic is incorporated by deploying multi-wall carbon nanotubes over the center of the tag design. The changes in the frequency response of the central slot due to CO2 concentrations are demonstrated in a radar cross-section graph. The proposed tag shows a reliable performance under changing atmospheric conditions. © 2001 Elsevier Science. All rights reserved.

This paper has described the development of the spread spectrum radar system and radar cross-section (RCS) measurements in the frequency range of 1–3 GHz, using a LabVIEW controlled fully digital radar setup. In this paper, the performance of a polyphase coded spread spectrum radar is illustrated and compared with the theoretical calculation for bistatic RCS measurements. For the performance calibration, different (sizes) flat plates are used as reference targets and tested over a wide frequency range. Here in addition to the controlling LabView platform is used for radar signal processing. MATLAB platform is used to compute the theoretical RCS pattern for radar performance validation.

Compared with the traditional subspace methods, the application of compressed sensing (CS) to the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation in bistatic multiple-input multiple-output (MIMO) radar can achieve robustness to coherent targets and high localization accuracy with a limited number of snapshots. However, the performance of grid-based CS reconstruction methods degrades since there is an unavoidable basis mismatch between the actual DOD/DOA and the assumed basis. As a gridless CS method, the atomic norm minimization (ANM) has attracted much attention. Specifically, the use of multiple snapshots in ANM has improved the parameter estimation accuracy in contrast with a single snapshot. In this paper, we address the problem of gridless DOD and DOA estimation in bistatic MIMO radar, and develop a multiple-snapshot 2D-ANM algorithm and its two low complexity versions. We start with a rigorous derivation on how to convert the multiple-snapshot 2D-ANM into a semi-definite programming problem, and then explore its dual problem. To overcome the heavy computational burden of the 2D-ANM when the numbers of snapshots and array elements increase, we further propose a 2D-ANM algorithm with snapshot reduction (2D-ANM-SR), as well as an improved 2D-ANM-SR algorithm based on alternating direction method of multipliers (2D-ANM-SR-ADMM). Numerical examples show that with similar accuracy in comparison to 2D-ANM, much lower computational complexity can be achieved via 2D-ANM-SR and 2D-ANM-SR-ADMM.

Conventionally adaptive detection for frequency diverse array (FDA) radar requires extra training data to estimate the range-dependent interference covariance matrix (ICM). In this paper, we propose an adaptive moving target detection approach for FDA radar without training data, where the ICM is directly estimated by applying a structured generalized likelihood ratio test (SGLRT) to the target-present samples. In order to reduce the three-dimensional (3D) search complexity of the SGLRT, we then propose an efficient unstructured GLRT (UGLRT) approach, which transforms the 3D search into a 1D Doppler-only search and a joint range-angle search. Moreover, we design a low-complexity two-stage implementation of the UGLRT for practical application. The first stage is to test multiple discrete frequency bins for target detection, while the second stage is to precisely estimate the frequency through locally optimizing the estimator function around the coarse estimate provided by the first-stage detection. As the UGLRT approach avoids the 3D search, it significantly reduces the computational complexity, compared to the SGLRT counterpart. Numerical results show that the UGLRT approach achieves almost the same detection and estimation performance as the SGLRT one.

A large-range autofocus microwave photonic radar based on adaptive spatial filtering along the range direction is proposed to achieve high-resolution imaging of the target at any distance. By firstly estimating the target distance and then adaptively adjusting the coverage of the spatial filter, the targets at any distance can be filtered out and their high-resolution images can be achieved. Besides, the sampling rate of the back-end ADC can be as low as hundreds of megahertz, leading to low computation load. Comparing to the present microwave photonic radars, new radar structure and work flow are proposed to achieve high-resolution imaging of the targets at any distance. The range window is not limited and the computation load is low, which is hard to realize by the present microwave photonic radars. Experimentally, a K-band (18–26 GHz) autofocus radar is established, the large-range autofocus ability is verified by autofocusing and high-resolution imaging for a static metallic plane at the equivalent distances of 790 m, 3.97 km and 8.11 km, respectively. Imaging for a flying UAV also indicates the system’s high-resolution imaging ability for the moving target. The proposed system enhances the capabilities of microwave photonic radars and has the potential to be applied in non-cooperative target identification.

Landslide disasters are the second largest geological disaster activity in addition to earthquake disasters. They cause economic losses of up to several billion yuan each year, resulting in thousands of deaths and injuries. Therefore, it is important to monitor and alert landslide disasters. Significance. In addition, after the occurrence of landslides, a large amount of deposits will be created, which will further breed mudslides. The ability to qualitatively and quantitatively extract landslide information at the first time is particularly important for disaster prevention, mitigation, and disaster management. Therefore, the extraction of landslide information is a significant measure. At the same time, due to the limitations of sensors, how to organically fuse data from multiple sensors to achieve tracking performance that cannot be achieved by a single sensor has become the focus of research and attention on multi-sensor data fusion technology in the field of target tracking. Based on the above background, the research content of this paper is the application of multi-sensor target tracking fusion technology to achieve comprehensive warning information extraction of landslide multi-point monitoring data. This paper proposes a spatiotemporal registration method and Ensemble Kalman Filter (EnKF) for target tracking Algorithm, and the prediction model is established through optimization problem and target tracking. Finally, the test simulation is carried out by selecting 30 monitoring data of a monitoring point of a new construction project as the detection value. The experimental results show that when the moving target model is unknown in actual work, the polynomial. The order of the fitting and the boundary conditions of the spline fitting are not easy to give, but you can optionally use the sensor measurement data in the middle part and then use the spline fitting algorithm to obtain a better time registration effect; In terms of spatial registration, in the Z-axis direction, the measurement error of sensor A and sensor B is large, and the maximum value exceeds 2000 m. The maximum value of sensor A error between the measured values and theoretical values of radar A and B after registration is about 1000 m, which is the Z axis direction, and the maximum error of sensor B is about 1000 m, which is also the Z axis direction. The error has been significantly reduced. EnKF is better than the UKF algorithm under non-Gaussian noise conditions. The PF algorithm itself is suitable for nonlinear and non-Gaussian systems and has the best tracking performance.

In practical application, the time when the maneuvering targets enter and leave the radar coverage observation is usually unknown, which will seriously deteriorate the performance of coherent integration and target detection. To solve this problem, we propose a method based on the symmetric autocorrelation function and the proposed window Lv's transform (SAF-WLVT) for coherent integration and motion parameters estimation of maneuvering target, involving the range migration (RM) and Doppler frequency migration (DFM). Firstly, the SAF is utilized to correct the linear RM induced by target's radial velocity and range curvature caused by target's radial acceleration simultaneously. Then, the proposed WLVT is applied to eliminate the residual DFM caused by the target's radial acceleration, accumulate the target energy without introducing excess noise energy, and obtain the baseband velocity and acceleration estimations. Finally, the Doppler ambiguity number is estimated by matched filtering and 1-D parameter searching, and then the target's radial velocity is obtained. The proposed method has low computational complexity, and possesses favorable performance of target detection and parameter estimation in the high signal to noise (SNR) environment. The mathematical analysis and simulation results demonstrate the effectiveness of the proposed method.

Signal parameter estimation like direction of arrival (DOA), polarization and Doppler frequency for conformal arrays with a reasonable computational effort is cumbersome, because conformal arrays have complex steering vectors without Vandermonde structure but involves distinct antenna patterns. To this end, we propose a fast estimation algorithm which enjoys computational benefit stemming from processing in fractal dimension and the Hough transform. Firstly, we obtain rough DOA estimations via the Hough transform using the covariance matrix phases, on the basis of a devised systematic scheme of ambiguity resolving. Subsequently, polarization is obtained through a rank reduced problem with DOA substituted. Precision improves by iteration and alternative optimization between DOA and polarization. Thirdly, we calculate the Doppler frequency with the temporal covariance matrix. Simulations validate that the proposed algorithm outperforms existing algorithms for slightly lower computation complexity and sufficient precision.

Due to their nice properties, unimodular sequences possessing low autocorrelation sidelobes have already been employed as transmit waveforms in numerous radar systems. It could also be desired to have a transmit waveform which avoids certain frequency bands allocated for specific communication applications. Hence, adaptation and specific design of the waveform spectrum might also be essential. Accordingly, in this manuscript, we propose two alternative algorithms for designing radar transmit sequences satisfying simultaneous temporal integrated sidelobe level (ISL) and spectral stopband constraints. The proposed algorithms utilize the majorization-minimization (MM) method for directly minimizing ISL by first proposing a majorizing function in place of the objective function of the minimization problem. Then, solution of the minimization problem is obtained in an iterative manner. Accelerated versions of the newly proposed algorithms are also derived to facilitate their fast convergence. We also present numerical examples for comparing performances of our newly proposed methods against some existing methods from the literature in terms of computation time, number of iterations, and level of suppression in stopbands. (C) 2020 Elsevier Inc. All rights reserved.

The ordered statistics (OS) scheme is an effective constant false alarm rate (CFAR) technique deployed in many radar systems. It is widely deployed because of its simplicity and effectiveness under conditions of both homogeneous and non-homogeneous radar returns. However, the problem of inaccurate censoring typically degrades its performance since it is often difficult to accurately determine the actual number of interfering targets and clutter edges in the reference window per time. In this article, we address this problem based on the principle of discriminant analysis (DA) towards automatically and effectively estimating the k th rank ordered element of the OS scheme. Our scheme, termed the DA-OS scheme, works without requiring a priori knowledge about the statistical characteristics of the input radar returns. The results obtained via Monte Carlo simulation indicate that the DA-OS scheme achieves a small CFAR loss of about 0.392 dB relative to the cell averaging (CA) scheme under conditions of homogeneous radar returns at a probability of detection of 0.5. It outperforms other notable traditional schemes, including the CA, smallest-of CA, greatest-of CA, and the fixed OS schemes under conditions of non-homogeneous radar returns. Finally, it provides a number of desirable qualitative characteristics as against other existing censoring techniques.

For a statistical multiple-input multiple-output (MIMO) radar, we investigate the maximum achievable diversity gain and the minimum possible probability of miss detection and the conditions of achieving those at sufficiently high signal-to-noise ratio (SNR) when the matched subspace detector is employed in the presence of signal-dependent interference. In addition to linear dependence between target returns at each receiver, we show that linear dependence between target and interference subspaces reduces the diversity gain when the matched subspace detector is employed. We derive the approximated form of the probability of miss and introduce the signaling loss concept based on it. We show that the probability of miss is minimized at high SNR when not only is the diversity gain maximized, but the signaling loss is also minimized. Then, we use the signaling loss corresponding to the maximum diversity gain as a novel design metric for designing signals and minimizing the probability of miss at sufficiently high SNR in statistical MIMO radar. Due to the fact that designing the optimal signals is challenging, we derive analytically the detection performance loss of quasi-optimal signals by employing the signaling loss concept.

Pulse streams with Doppler shift exists widely in radar, communications, and many other time-variant linear systems. One central task in identifying these systems is to recover the delay-Doppler pairs. However, the classic methods require high sampling rate for wideband pulse. In this paper, a multicycle sub-Nyquist sampling system to recover delay-Doppler pairs symmetrically is proposed. Using finite rate of innovation (FRI) theory, the unknown parameters can be estimated from the directly sampled Fourier coefficients, where FRI refers to that the sampled signals have finite degrees of freedom per unit time. Based on the sparse common support (SCS) model and symmetry of the delay-Doppler pairs, the samples from each pulse repeat interval (PRI) can be jointly used to estimate parameters, where PRI is the time interval of the transmitted pulses, and SCS refers to that each PRI of the received signal has common non-zero supports but different weights. The number of PRIs and samples required for signals with both different and identical parameters are demonstrated mathematically. When the SNR is 10 dB, the NMSEs of the estimated delay-Doppler parameters are -10 dB and -5 dB or more higher than several advanced methods respectively, which demonstrate the effectiveness of the proposed method.

In a low angle scenario, the radar performance is significantly degraded because of the multipath. Time reversal is an efficient technique to improve the target detectability by focusing the energy from different paths. For multiple targets and rich multipath scattering case, the number of echoes is large and the distance estimation is complex. In this paper, a distance estimation method of multiple targets and multipath based on radar time reversal is presented. Utilizing Time reversal transmitting signal as the referring signal, the distance of different paths for different targets can be captured after matched filtering. Then the distance paring method is proposed to group the distance measurements from the same target. Simulations verify the performance of proposed methods.