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In the present century, digital signal processing (DSP) approaches are considered to be one of the most powerful technologies which may shape the science and technology in coming decades. From 1970 onwards, a drastic revolution took place in a wider domain of DSP which has made it popular in several studies such as radar and sonar signal processing, digital televisions, wireless communication scenarios and other multimedia setup etc. Digital filters form the backbone of this DSP architecture and in point of fact the field of digital filter design has drawn justified recognition from the researchers throughout the world for the last 50 years. In connection to this, thousands of research articles may be found from the literature which had extensively addressed on the design of such filters. In order to meet the requirement of narrow transition-band, finite impulse response (FIR) filters are commonly assumed to be of higher order and accordingly it significantly enhances computational complexity. In regard to this, construction of hardware efficient digital filters had drawn significant consideration which aims to include minimum hardware elements during its application and consequently consumes less power. This review paper illustrates various techniques for the implementation of hardware efficient narrow transition-band FIR filter and investigates a number of favourable attributes which are capable in sustaining the stringent requirements of communication standards. A good number of relevant articles are taken from the literature so as to make a robust and complete review.

This paper describes the design and verification of an ultra-wideband 6–14 GHz frequency modulated continuous wave (FMCW) primary radar system with very high range resolution. The design and measurement results of the utilized signal generator and receiver are presented. The signal generator features a 86% relative continuous tuning range and average phase noise of −106 dBc/Hz at 1 MHz offset to the carrier. The radar system utilizes a hybrid structure where the voltage controlled oscillator was fabricated using 130 nm SiGe BiCMOS technology and the lower frequency components are off-the-shelf. Free field measurements of the radar showcase an unprecedented combination of 3 cm range resolution, low phase noise and low operating frequency of 6–14 GHz for inherently higher range.

In electronic-warfare (EW) receiver applications, real-time measurement of the radar signals' carrier frequencies is mandatory and considered to be one of the most important radar parameters. Accurate frequency detection can be used to sort and deinterleave signals in a dense signal environment where multiple signals arrive simultaneously presenting severe problems unless they can be differentiated appropriately. Researchers and engineers are actively looking for new algorithms for high resolution wideband receivers with easier implementation. This paper presents an adaptive gain control and dynamic thresholding wideband digital receiver to accurately detect two simultaneous high dynamic range signals, even when their signal frequencies are very close. The proposed fast Fourier transform (FFT) based receiver examines incoming signal characteristics and provides real-time accomplishment of these tasks, by way of using an adaptive gain-control amplifier, a squarer, two analog-to-digital converters, and two FFT's. The wideband receiver offers high probability of intercept over wide instantaneous RF bandwidths, high dynamic ranges and sensitivity. Two simultaneous signal accommodations and their characteristics are provided to demonstrate the improvements over presently used receivers in this field.

This paper proposes a method for estimating the joint direction of departure (DOD) and direction of arrival (DOA) of coherent targets in multiple-input multiple-output (MIMO) radar with electromagnetic vector sensors. The method does not require any additional processing for both decorrelating the coherent targets and pairing the DOD and DOA, and exhibits real-valued operations. Compared to the existing polarization smoothing and unitary ESPRIT, the proposed method shows improved performance which is validated by the simulation results.

This paper considers the target detection problem in multiple-input multiple-output radar where the disturbance covariance matrix is unknown. Based on the Rao criterion, a persymmetric Rao test is designed by exploiting the persymmetric structure of the disturbance covariance matrix. Particularly, the proposed detector does not require training data, and exhibits strong selectivity to mismatched signals. Another contribution in this paper is to propose a two-stage detector whose second stage coincides with the proposed persymmetric Rao test. The selectivity or robustness of the two-stage detector can be tuned by selecting different threshold pairs.

Orthogonal matching pursuit (OMP) is a popular greedy algorithm because of its simplicity and low computational cost. In the application of OMP, one of the most important issue is to design a stopping condition according to some prior information, such as sparsity and noise level. In this paper, we provide a blind stopping condition for OMP in the presence of Gaussian noise. We theoretically analyze the stopping condition in terms of the mutual incoherence and the minimum component signal-to-noise ratio (SNR), with which OMP can stop with correct support recovery. Different from other alternatives, the proposed stopping condition can be explicitly determined regardless of prior information. So it is much suitable for practical applications. In the application of compressive sensing pulse-Doppler radar, we further analyze the support recovery performance of the proposed condition. By separately recovering the targets belonging to different Doppler frequencies, we remarkably reduce the requirement on the minimum component SNR of all targets, and thus our stopping condition is applicable to low SNR scenes. Numerical results illustrate that the proposed condition has comparable performance to the alternative with a priori information.

In this paper, we develop a theoretical framework for short-range millimeter (mm) wave radar imaging using a sparse array of monostatic elements, and validate it via experiments. The framework is a significant departure from classical radar, which largely focuses on long-range settings in which targets are well modeled as point scatterers. For sparse arrays, the point scatterer target model leads to grating lobes, and our central contribution is to demonstrate that a patch-based target model, suitably optimized for the sensor and scene geometry, suppresses such grating lobes. Key results include the following: (a) Characterizing the number of degrees of freedom (DoF) as a function of geometry, and showing that spatial undersampling (number of elements smaller than DoF) leads to grating lobes with the point target model; (b) showing that spatial aggregation via a patch-based dictionary suppresses grating lobes, and that patch size can be optimized based on estimation-theoretic criteria; (c) providing examples of the application, and adaptation, of patch-based dictionaries for sparse reconstruction.

In this paper, we propose a space-time allocation strategy for collocated multiple-input multiple-output (MIMO) radar to determine the beam directions to multiple targets, to be tracked. The transmit beams are allocated to different targets with distinct time units in one illumination. The basis of our strategy is to achieve the optimal probing through the feedback information in the tracking recursion cycle, with the objective of improving the overall tracking accuracy of multiple targets. We derive the posterior Cramer-Rao lower bound (PCRLB), used as the optimization criterion, to quantify the target state estimate accuracy. By introducing the illumination probability in each time unit, the space-time allocation problem is converted to a convex one. Therefore, a sufficient solution is provided, which aims at the real-time demand of radar system. Simulation results demonstrate the superiority of the proposed strategy to the uniform allocation, space-only allocation and time-only allocation methods.

In this paper, we study the problem of robust Stackelberg game-based power control (RSG-PC) for spectral coexisting multistatic radar and massive multiple-input multiple-output (MIMO) communication systems. Recognizing that the path propagation gains cannot be assumed to be constant, the column-wise model is employed to describe the uncertainty of the parameters. The primary objective of the RSG-PC scheme is to minimize the worst-case radiated power of each radar with uncertain path gains for a specified signal-to-interference-plus-noise ratio (SINR) requirement for target detection, while the massive MIMO communication base station (MIMO-CBS) is protected from the interference of radar transmissions. The hierarchical competition between the massive MIMO-CBS and multiple radars are modeled as a robust single-leader multi-follower Stackelberg game. In the considered game model, the massive MIMO-CBS plays as the leader to decide the prices first through the maximization of its own utility. The radars act as the followers, who compete with each other in a non-cooperative Nash game according to the released interference prices from the massive MIMO-CBS subsequently. Based on the theoretical findings on the existence and uniqueness of the robust Nash equilibrium (RNE) in the formulated game, we present an iterative power control approach that guarantees the convergence to the RNE. Finally, the numerical simulations are provided to verify that our RSG-PC scheme can provide efficient power resource utilization, guarantee a predefined target detection performance, and also protect the quality of service (QoS) of massive MIMO-CBS with uncertainties in path propagation gains.

A microwave photonic (MWP) video imaging radar with a bandwidth tuning range of 0–10 GHz is proposed and experimentally demonstrated. In the transmitting path, high-frequency radar transmission signals with varied bandwidths are generated by optical frequency multiplication of an electrical low-frequency signal with tunable bandwidth, which enables radar to image targets with proper resolution and thus optimizes the balance between the observation distance and resolution. In the receiving path, in order to realize video imaging, the high-frequency echo signals with large bandwidth, after being subjected to MWP down-conversion technology for de-chirping, can be sampled by a low-speed and high-precision analog-to-digital converter Then, a digital signal processor is employed for processing the sampled echo data to realize video imaging. In field experiments, imaging ability with varied resolution is firstly verified by imaging Boeing 737 airliner with different-bandwidth radar signals. Moreover, continuous monitoring the flight process of non-cooperative Boeing 737 airliner and unmanned aerial vehicle also successfully demonstrates the video imaging ability of the proposed MWP radar. The capturing of detail structures and the recognition of target flight posture changes all indicate a great potential of the MWP radar for observing distant-range targets of interest.

Compared to large-scale MIMO radar, coprime MIMO radar can achieve approximate estimation performance with reduced antenna number. In this paper, joint direction of arrival (DOA) estimation and array calibration for coprime multiple-input multiple-output (MIMO) radar is considered, and an iterative method for the estimations of DOA and array gain-phase errors is proposed. Based on the received data structure of coprime MIMO radar, trilinear decomposition is firstly adopted to obtain the estimations of transmit and receive direction matrices, which are perturbated by the gain-phase errors. Through equation transformation, the un-perturbated direction matrices and gain-phase errors can be iteratively updated based on Least squares (LS). Finally, the unique DOA estimation is determined from the intersection of transmit and receive direction matrices. The proposed algorithm achieves better DOA estimation and array calibration performance than other methods including estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm, multiple signal classification (MUSIC)-like algorithm and joint angle and array gain-phase error estimation (JAAGE) method, and it performs close to the method with ideal arrays. Multiple simulation results verify the algorithmic effectiveness of the proposed method.

Massive MIMO (multiple-input multiple-output) is no longer a “wild” or “promising” concept for future cellular networks—in 2018 it became a reality. Base stations (BSs) with 64 fully digital transceiver chains were commercially deployed in several countries, the key ingredients of Massive MIMO have made it into the 5G standard, the signal processing methods required to achieve unprecedented spectral efficiency have been developed, and the limitation due to pilot contamination has been resolved. Even the development of fully digital Massive MIMO arrays for mmWave frequencies—once viewed prohibitively complicated and costly—is well underway. In a few years, Massive MIMO with fully digital transceivers will be a mainstream feature at both sub-6 GHz and mmWave frequencies. In this paper, we explain how the first chapter of the Massive MIMO research saga has come to an end, while the story has just begun. The coming wide-scale deployment of BSs with massive antenna arrays opens the door to a brand new world where spatial processing capabilities are omnipresent. In addition to mobile broadband services, the antennas can be used for other communication applications, such as low-power machine-type or ultra-reliable communications, as well as non-communication applications such as radar, sensing and positioning. We outline five new Massive MIMO related research directions: Extremely large aperture arrays, Holographic Massive MIMO, Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive MIMO.

As an important research branch in multiple-input multiple-output (MIMO) radar, orthogonal waveforms are always desirable. In practice, however, the orthogonality may not be always guaranteed. In this paper, a computationally efficient parallel factor (PARAFAC) estimator is proposed, which is suitable for direction-of-arrival (DOA) estimation in colocated MIMO radar with large antenna arrays as well as nonorthogonal waveforms. Firstly, the spatially colored noise caused by the nonorthogonal waveforms is eliminated via temporal cross-correlation of the measurement. Thereafter, a third-order PARAFAC (or called trilinear decomposition) model is established by exploiting the multidimensional structure as well as the low rank property of the cross covariance matrix. The DOA estimation problem is then linked to PARAFAC decomposition and finally obtained via least squares technique. Compared with the existing matrix completion-based algorithm, the proposed estimator is attractive from the perspective of computational complexity and estimation accuracy, especially with large antenna arrays scenario. In addition, the stochastic Cramer-Rao bound on DOA estimation with waveform imperfectly is derived. Numerical simulations are provided to verify the effectiveness of the proposed estimator.

Detection of moving targets under range-dependent clutter background is a challenging task in conventional bistatic space-time adaptive processing (STAP) radar. It is more intractable in the presence of range ambiguity, which also results in parameter estimation ambiguities. To tackle these issues, this paper proposes a novel framework of bistatic multiple-input multiple-output (MIMO) STAP radar with frequency diverse array (FDA) as the transmit array. The FDA employs a micro frequency increment across the array elements. Therefore, it is capable of providing additional degrees-of-freedom (DOFs) in range domain in the transmit end. After combining the DOFs in transmit, receive and pulse dimensions, the clutter or target of bistatic FDA-MIMO radar possesses the following properties: (i) for a particular range region, the clutter ridges of all range bins are located in a common plane of the three-dimensional (3D) space. (ii) By properly designing the frequency increment of FDA, the clutter ridges from different range regions are located in distinct 3D planes and thus are discriminable in 3D space. (iii) As long as the sum of the target radial velocities relative to the transmitter and the receiver is unequal to zero, the target will not be located in these clutter planes. By exploiting these new properties, a 3D localized space-time-range adaptive processing (STRAP) approach is presented to realize clutter suppression. It evidently reduces the computational complexity and the requirement for the amount of training data. Several numerical examples are provided to demonstrate the superiority of the proposed technique over the existing techniques.

In radar systems, prolonging the integration time helps to improve the detection and estimation performance of weak target. However, in a long integration time, target's high-order motion is non-ignorable, which causes high-order range migration (RM) and Doppler frequency migration (DFM) problems. The RM and DFM defocus the integration result and lead to detection and estimation performance loss. In this paper, a new coherent integration method is proposed for maneuvering radar target with jerk motion. First, we define a new cross correlation function basing on non-uniformly resampling technique. By using this function, we eliminate the range migration (RM) and Doppler frequency migration (DFM) caused by target's velocity and acceleration. Then, matched filtering process is performed to compensate the phase error caused by target's jerk motion. Finally, the integration is achieved by summing signal data in different range frequency cells and performing a two-dimensional fast Fourier transform (FFT). The proposed method is applicable for low signal-to-noise ratio (SNR) environment. Compared with the GRFT algorithm, the proposed method has a close integration performance and a lower computational complexity. The effectiveness is validated by simulations and real-data processing results.

Frequency diverse array (FDA) offers potential applications for joint range and angle estimation, but ambiguous estimates may be generated due to its range-angle coupling and time-variant beampattern. This problem can be addressed by jointly utilizing FDA and multiple-input multiple-output (MIMO) radar, but only multiple signal classification (MUSIC) algorithm was considered in the FDA literature. In order to avoid high computational complexity in the MUSIC algorithm due to the required 2-D peak searching, in this paper, we propose a two-stage estimating signal parameters via rotation invariance technique (ESPRIT) algorithm for FDA-MIMO radar to estimate both range and angle of targets, along with the proposed pairing method for unambiguous estimates. Moreover, closed-form expressions of the mean squared error (MSE) and Cramer-Rao lower bound (CRLB) for angle and range estimations are also derived. All proposed methods and derivations are verified by both theoretical analysis and numerical results, which show the superiority of FDA-MIMO radar over conventional phased-array radar and MIMO radar in target localization.

The high-order range migration (RM) and Doppler frequency migration (DFM) induced by the high-order motion parameters will lead to severe performance loss for long-time coherent integration of highly maneuvering target. Therefore, in this paper, a high-order RM and DFM correction method is proposed. To be specific, an auto-correlation function (ACF) is first presented to correct the RM. Then, the residual high-order and linear DFM are eliminated by the polynomial-phase transform (PPT) and Lv’s distribution (LVD), respectively. Besides, the motion parameters of the maneuvering target can also be obtained based on the peak location after LVD. The computational complexity of the proposed method is low. Meanwhile, compared with the adjacent cross correlation function (ACCF)-based methods which have comparable computational cost, the proposed method can achieve RM correction without any restriction and can obtain high motion parameter resolution. Simulations are provided to evaluate the performance of the proposed method from computational complexity, coherent integration ability and detection performance. Additionally, the real experiment with X-band radar is also conducted to verify the effectiveness.

In transmit beampattern design, it is desirable to maximize the power around locations of interest. Multiple-input multiple-output (MIMO) radar transmits orthogonal waveforms, which is hardly achievable in practical applications. Moreover, the constant modulus constraint and the computational burden limit the system performance. In this paper, we propose an effective piecewise linear frequency modulation (PLFM) waveform for coherent frequency diverse array (FDA) radar to synthesize the desired transmit beampattern. By deriving the relationship between the beam direction and the transmit signal frequency, we find that different sub-band of transmitted signal corresponds to different angular sector. With the relationship between frequency band and mainlobe of beampattern, the beampattern synthesis is transformed into the design of waveform spectrum which is realized by designing the PLFM waveform in this paper. Compared with the state-of-the-art technique, the proposed PLFM waveform in coherent FDA enjoys the advantage of the constant modulus, low sidelobe level and reduced computation complexity. In addition, the multi-dimensional ambiguity function and the transmit beampattern response are derived to examine the properties. Numerical results are presented to validate the effectiveness of the proposed method.

The problem of adaptive target detection in compound-Gaussian clutter with unknown covariance matrix for multiple-input multiple-output (MIMO) radar is addressed in this paper. A set of secondary data for each receiver is assumed to be available, and the primary data and the secondary data own the same covariance matrix structure but different power levels (textures). Firstly, a Bayesian approach is proposed, where the structure is modeled as a random matrix with an appropriate distribution. Then, two ways are adopted to model the texture: an unknown deterministic quantity or a random variable ruled by certain distribution. In this framework, three adaptive generalized likelihood ratio tests (GLRTs) are developed using the two-step design procedure. Finally, the capabilities of the proposed detectors and their superiority with respect to some existing techniques are evaluated via numerical simulations.

The potential abilities of polarimetric multiple-radar architectures with distributed antennas in electronic counter-countermeasures (ECCMs) are analyzed based on differences between target echoes and deception jamming in scattering coefficients properties. According to the differences in generation mechanism between the target scatter and jamming, a signal model of real targets and deception jamming for two-dimensional vector sensors is established. By applying the Neyman-Pearson (NP) criterion and a hybrid NP-Maximum Posteriori (MAP) criterion, a two-stage detection/discrimination algorithm is proposed. In our proposed algorithm, the targets and jamming are detected in the first stage from a Gaussian noise background. Then, a discriminator based on the generalized likelihood ratio test is designed by exploiting the property of polarization discrimination. A theoretical analysis is also given to evaluate the performance of the second stage of the algorithm. Moreover, the discrimination performance can be improved by optimizing the transmitter polarization parameters. Simulation results indicate that the proposed ECCM strategy is effective against DRFM jamming, especially when JNR is high and transmit polarization is optimally designed.

In multiple-input multiple-output (MIMO) radar, the maximum number of targets which can be uniquely identified, also known as the parameter identifiability, is one of the most basic issues. In this paper, the parameter identifiability is extended from the space-only MIMO radar to the space-time MIMO radar. According to the parameter identifiability conditions derived, the maximum number of targets which can be identified by the space-time MIMO radar is presented. Comparing with space-only MIMO radar, the parameter identifiability of space-time MIMO radar is significantly enhanced by exploiting both the spatial and temporal characteristics of the transmitted waveforms. The results are validated by several numerical examples.

In order to obtain clearer interference pattern of interferometric radar and obtain more accurate detection target, this paper studies the influence of atmospheric turbulence on axial transmission beam. The response of random wave number and transmission distance to amplitude fluctuation and phase fluctuation is discussed respectively. The aim is to establish a phase compensation plate in the reference optical path, so that the interference optical path and phase can meet a specific range, and to promote the development of interferometric radar technology. The study shows that the random wave number caused by atmospheric turbulence has obvious influence on amplitude fluctuation and phase fluctuation, and transmission distance is relatively weak influence on them. As the change of random wave number and transmission distance, the amplitude fluctuation and phase fluctuation will also change. The phase compensation plate should be selected integrated according to the atmospheric turbulence intensity and transmission distance.

It has been an important technique to reduce sidelobes for processing nonzero Doppler target returns under Golay complementary waveforms. While the pointwise minimization processor in our early work shows an enhanced performance on the suppression of range sidelobes compared with existing approaches, it has a reduced detection probability to target with location uncertainties. In this paper, we present an alternative processing method which combines the radar returns in two separated pairs of Golay complementary waveforms through a pointwise addition processor. Our simulation result shows that this alternative method has a similar performance to that of the pointwise minimum processor for range sidelobes suppression and computational complexity but results in an improved target detection probability, with an acceptable decrease on the peak to peak-sidelobe ratio.

Significant energy savings can be achieved by operating heating, ventilation, and air conditioning control systems with indoor occupancy measurement information. This paper presents a novel plug-and-play occupancy sensing method which will enable temporal minimization of building energy consumption to meet building usage behavior without privacy concerns. The proposed wireless occupancy sensing platform is based on long-wave infra-red (LWIR) focal-plane arrays (FPAs), or thermal imagers, that detect thermal energy rather than visible light. We developed an advanced sensor package consisting of multiple thermal imagers with low-cost optical enhancements to increase field of view and increase sensitivity to occupant detection (filtering building clutter). These imagers can be coupled with radio frequency and ultrasonic-based radar to enhance data collection at key occupant zone boundaries to improve accuracy. Standard filtering and estimation techniques from the image processing and computer vision communities are introduced to overcome the accuracy issues suffered by traditional PIR based sensing, especially when occupants remain relatively still. Accurate low-level counting of individuals can be achieved with minimal impact on privacy. The proposed occupancy detection method can work as a retrofit to enable real-time understanding of the building operational state and usage behavior. Sensor data obtained from real test building zones was used to test the efficacy of the method.

This paper design waveform for high frequency radar to optimize its performances in co-channel interference/colored noise environment. Considering that radar applies the optimal filter for range processing, a novel objective function accounting for both Signal to Interference plus Noise Ratio (SINR) and Integrated Sidelobes Level (ISL) is introduced and the constant modulus constraint on the probing waveform is forced. To resolve the resulting non-convex quartic optimization problem, an efficient iterative algorithm based on Majorization-Minimization (MM) and Pattern Search (PS) is proposed. In each iteration, the proposed algorithm produces a multi-dimension quadratic majorized problem and then splits it into multiple one-dimension problems with closed-form solutions. Simulation results show that the SINR and ISL performances of designed waveform can be flexibly adjusted. Besides, the convergence speed and stability of the proposed algorithm are more satisfactory than the other methods.

In this paper, we utilize a space-time coding (STC) scheme to synthesize the multiple-input multiple-output (MIMO) radar transmit beampattern and meanwhile consider the matched filtering loss (MFL) of equivalent signal in waveform design. To solve the MFL optimization problem, we propose a simulated annealing based (SA-based) algorithm to effectively find the optimal solution of STC waveform matrix. Our STC method for waveform design has suppressed the fluctuation of equivalent signal so that has the advantage of achieving the minimum MFL nearly, compared to the conventional methods for constant-envelope constraint waveform design (e.g. cyclic algorithm (CA) method). Simulation results demonstrate the effectiveness of the proposed algorithm.

This paper investigates the design of constant modulus waveforms with low auto- and cross-correlation sidelobes for multiple-input multiple-output (MIMO) radar. The resulting weighted integrated sidelobe level (WISL) minimization problem involves a fourth-order objective function and nonconvex constant modulus constraint and, hence, is NP-hard. Unlike existing methods which indirectly solve an “almost equivalent” problem to the original one, we equivalently reformulate the nonconvex WISL minimization problem appropriately and propose a new method to directly deal with the problem. Specifically, in the proposed algorithm, the fourth-order problem is first converted into a bi-quadratic problem via introducing an auxiliary primal variable, and then a primal-dual framework, named as “nonlinear alternating direction method of multipliers (nonlinear-ADMM)”, is derived to handle the modified problem. More importantly, both the convergence and termination conditions of this algorithm are discussed. Numerical simulation are carried out to assess the performance of the proposed algorithm in terms of the WISL metric, auto- and cross- correlation properties and computational complexity.

This paper revisits the problem of multiple parameters estimation for coherent targets in bistatic EMVS-MIMO radar. By extending the spatial smoothing mechanism to the EMVS-MIMO radar, three generalized spatial smoothing estimators, named the TS approach, the RS approach and the TRS approach, have been proposed. The killer idea of the proposed methodologies is to recover the rank of the covariance matrix via averaging the array measurement in spatial domain, and then estimate the parameters from the cooperation of the normalized vector cross-product technique and the LS method. Unlike the state-of-the-art polarization smoothing methods, the proposed methods would not sacrifice the polarization information, so that TS, RS and TRS are capable of providing polarization information of the coherent targets. The proposed estimators do not require any constrain on the geometries of the Tx/Rx EMVS arrays. They are computationally friendly yet retain robustness to the sensor position error. The proposed estimators are analyzed in detail, and numerical simulations are provided to verify their theoretical advantages.

This article develops a collaborative radar node selection and transmitter resource allocation (CRNS-TRA) algorithm for target tracking applications in multiple radar architectures. The key mechanism of the CRNS-TRA strategy is to coordinate the radar node selection, transmit power, and dwell time of multiple radar architectures to enhance the target tracking accuracy under the constraints of several transmitter resource budgets. The analytical expression for the Bayesian Cramer-Rao lower bound (BCRLB) is derived and employed as the objective function to quantify the target tracking performance. It is shown that the CRNS-TRA problem is a mixed-integer, non-convex, and non-linear optimization problem, where the three involved parameters, that is, the radar node selection, transmit power and dwell time allocation, are all coupled in the objective function and the constraints. Subsequently, a fast and effective two-stage-based solution methodology is proposed for solving the resulting problem, which incorporates the interior point method and cyclic minimization framework. Extensive simulation results are provided to verify that the presented CRNS-TRA scheme is capable of effectively improving the target tracking performance with much lower operation time consumption when compared to other state-of-the-art resource allocation benchmarks.

Elliptically-contoured distributions (ECD) play a significant role, in computer vision, image processing, radar, and biomedical signal processing. Maximum likelihood estimation (MLE) of ECD leads to a system of non-linear equations, most-often addressed using fixed-point (FP) methods. Unfortunately, the computation time required for these methods is unacceptably long, for large-scale or high-dimensional datasets. To overcome this difficulty, the present work introduces a Riemannian optimisation method, the information stochastic gradient (ISG). The ISG is an online (recursive) method, which achieves the same performance as MLE, for large-scale datasets, while requiring modest memory and time resources. To develop the ISG method, the Riemannian information gradient is derived taking into account the product manifold associated to the underlying parameter space of the ECD. From this information gradient definition, we define also, the information deterministic gradient (IDG), an offline (batch) method, which is an alternative, for moderate-sized datasets. The present work formulates these two methods, and demonstrates their performance through numerical simulations. Two applications, to image re-colorization, and to texture classification, are also worked out.

A great deal of interest has been paid to enhance the radar range resolution for the last decades. One of the techniques is to construct a waveform combining inter-pulse and intra-pulse modulations. In this paper, stepped frequency (SF) waveform is used to represent the former, whereas phase coding (PC) is considered for the latter. To obtain the high-resolution range profile (HRRP) of a target induced by an SF waveform at the receiver, one of the approaches is the frequency domain (FD) algorithm. It has been successfully applied with SF linear frequency modulation waveforms. However, based on our investigations, processing the SFPC waveform with the FD algorithm does not lead to the performance, in terms of peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR), of the single-carrier phase coding (SCPC) waveform processed with a matched filter (MF). To overcome these drawbacks, we propose to split the spectrum of a phase coded pulse into a predetermined number of portions, and then to successively transmit the time-domain transformed versions of these various portions. The received echoes are then processed with a modified version of the FD algorithm. Our analysis and simulations show that the proposed waveform can be characterized by a PSLR and an ISLR close to those of the SCPC processed with MF in some scenarios.

Focusing on the signal to interference-plus-noise ratio (SINR) maximization in colocated multiple-input multiple-output (MIMO) radars, using the transmit covariance matrix (TCM) design of transmit waveforms, we have proposed a TCM Rpm with the form of symmetrical Toeplitz matrix, where m is the control parameter to generate different TCM. The main features include (a) full rank, to exploit the waveform diversity advantage of MIMO radar and to further suppress the maximum number of interfering sources, (b) positive semi-definition of sin ((π/2)Rpm) to guarantee that Rpm can be synthesized with binary phase shift keying (BPSK) waveforms in closed form, (c) higher output SINR level using the Rpm with smaller m in the receiver, with the prior knowledge of target and interference locations, (d) lower receive sidelobe levels (SLLs) using the Rpm with certain larger m (e.g. m = 1.5 or 2) for the unwanted and unknown sidelobe interference suppression. Simulation results validate the better performance of our proposed TCM compared to the phased-array, omnidirectional MIMO radar, phased-MIMO radar and the recently proposed TCMs.

Extended target probability hypothesis density (ET-PHD) filters have recently become popular owing to their relatively simple recursion processes, which makes them suitable for use in applications requiring real-time results, such as radar multi-target tracking. However, in the classic ET-PHD filters, the measurements of different targets are generally considered to be generated at the end of each scan. With this assumption, the measurement time diversity in radar applications cannot be modeled. To address this shortcoming, a novel time-matching ET-PHD filter in which a multi-prediction filtering framework is applied, and the true measurement times are used in the PHD propagation of each cell, i.e., prediction and correction, is proposed in this paper. In addition, a pre-partitioning strategy is employed to reduce the computational complexity of the proposed filter. The results of simulations conducted using the gamma Gaussian inverse Wishart PHD filter indicate that the proposed pre-partitioning-based time-matching ET-PHD filter is superior to standard filters in terms of both estimation accuracy and real-time performance.

Low signal-to-noise ratio (SNR) in wideband radar reconnaissance degrades performance of existing signal type recognition methods. To achieve higher correct recognition rate in low SNR levels, this paper proposes a recognition approach containing feature extraction and automatic estimation for signal type number. First, feature vectors of radar pulses are constructed from maximum energy angle (MEA) and maximum energy slice (MES) of ambiguity function (AF). To improve applicability in practical implementing, AF grids method is developed to obtain MEA and MES. A novel algorithm for denoising MES with geometric shape constraint is also proposed to improve validity of features with low SNR. Second, for realistic reconnaissance lacking prior knowledge, a type number estimation method is developed that costs little extra processing burden. Simulation results indicate that the proposed denoising algorithm brings high stability and robustness to extracted features in low SNR levels. The recognition approach in this paper is applicable to more realistic scenarios and achieves higher correct recognition rate with respect to low SNR compared with existing methods.

Accuracy and speed of parameters estimation for radar signal is very important in optical wireless communications. In order to implement high accuracy and fast speed of parameters estimation, a new estimation algorithm is presented in the paper. The method is based on quadratic phase function (QPF) and fractional Fourier transform (FRFT), which is used to estimate parameters for the chirp signal. In order to avoid two-dimensional searching during FRFT, chirp rate is directly estimated by QPF method, and then the optimum transform order of FRFT is calculated by estimating the chirp rate. Finally, center frequency and amplitude of the chirp signal are estimated by FRFT. Simulation results show that the proposed QPF-FRFT method is faster than the FRFT method and two step maximum modulus searching method. Otherwise, when the SNR is higher than -4dB, the center frequency, chirp rate and amplitude of chirp signal can be estimated by QPF-FRFT method more accurately than FRFT method. In conclusion, results on experimental of chirp signal illustrate the efficient performance of the algorithm.

A photonic approach to wideband Doppler frequency shift (DFS) measurement and direction discrimination is proposed and demonstrated. In the proposed scheme, a dual-parallel Mach-Zehnder modulator is used to generate carrier suppressed double sidebands signals, which are orthogonally polarized in a Sagnac loop incorporating a fiber Bragg grating. The DFS is transformed into a low-frequency electrical signal. The value of the DFS is obtained by analyzing the spectrum of the output current. The orientation of the DFS is estimated by a 90° hybrid coupler. The entire DFS measuring process is independent of the radar carrier frequency and suitable for wideband spectrum measurement range. Simulations are performed to verify the proposed scheme, the value and the orientation of the DFS can be estimated simultaneously. As the target moves toward the radar or moves away from the radar, there are, respectively, 36.8 dB and 39.1 dB power difference between the two branches of the hybrid coupler. In addition, the performance of the proposed approach in term of the signal-to-noise ratio and stability are discussed.

We develop a novel radar-based human motion recognition technique that exploits the temporal sequentiality of human motions. The stacked recurrent neural network (RNN) with long short-term memory (LSTM) units is employed to extract sequential features for automatic motion classification. The spectrogram of raw radar data is used as the network input to utilize the time-varying Doppler and micro-Doppler signatures for human motion characterization. Based on experimental data, we verified that a stacked RNN with two 36-cell LSTM layers successfully classifies six different types of human motions.

In this paper, we propose a novel angle estimation method in bistatic multiple-input multiple-output (MIMO) radar, which combines the transmit beamspace (TB) technique with the unitary ESPRIT (U-ESPRIT) model. The TB matrix is designed to focus the transmitted energy within the desired spatial sector, thus achieving the transmit coherent gain. Due to the special data structure of the TB-based bistatic MIMO radar, a corresponding method is proposed to construct the real-valued model. Unfortunately, the existed interpolation error inevitably degrades the direction-of-departure (DOD) estimation performance. To compensate the DOD estimation error, a look-up table is created, which establishes a one-to-one mapping relationship for DOD. The Cramer-Rao bound (CRB) on angle estimation in TB-based bistatic MIMO radar is also derived for performance analysis. Compared with existing methods, the proposed algorithm has better estimation performance due to the improved signal-to-noise ratio (SNR) gain and requires less computational complexity. Numerical simulations are presented to demonstrate the estimation performance of the proposed algorithm.

In recent times, time delay estimation (TDE) has received significant practical importance in sonar, radar, GPS, and various other fields. In a passive sonar system, estimation of time delay for low frequency and low signal to noise (SNR) acoustic source is a difficult task. If the source and receivers are kept inside the reverberation environment, time delay estimation becomes more difficult because of sound source echo’s. This research work proposes a new TDE approach named empirical mode decomposition maximum likelihood time delay estimation (EMD ML TDE) method, for the low-frequency and low SNR underwater machinery acoustic signal in a reverberant environment. EMD ML TDE method is based on maximum likelihood (ML) method, and it is using empirical mode decomposition (EMD) denoising technique to estimate acoustic sound signal and noise from the noisy reverberant signal. The experimental results are provided that this new approach is better to estimate time delay in reverberant environments.

In this paper, an efficient technique proposed for surface wave filtering in radar array antennas. The filtering reduced the correlation between high directive TX/RX array antennas. The filtering has been done using a small defected ground filter that achieves more than 20 dB isolation between the two antennas with a very close separation between the two antennas (less than 0.06 λ0). The presented results confirm the advantage of the applied technique in reducing the correlation coefficient between the two antennas to less than 0.005. The results demonstrate that 99.68% of coupled power can be reduced. The integrated isolated radar array antennas have been studied theoretically, simulated using electromagnetic simulation, and measured experimentally.

The simultaneous orthogonal multi-beam transmitted by a collocated multiple-input multiple-output (MIMO) radar is very effective in multi-target tracking. Since this working mode can meet the requirement of low possibility of interception; meanwhile, gain higher sensitivity and resolution. Aiming at the resource management in such a scenario, a joint beam, power and waveform selection strategy is put forward. The optimization model is established subject to a certain number of beams, power budget and waveform parameters. The criterion, which is predicted by the Kalman recursive equation, is minimizing the posterior estimate errors of multiple targets in worst cases simultaneously. Thereby, the suitable number of utilized beams, the allocated power as well as the waveform parameter in each beam for the current time epoch can be adaptively turned according to the estimate errors from the previous time epoch. We then fully demonstrate such a non-convex problem can be transformed into several convex problems. As such, the solution can be provided efficiently to meet the real-time demand. Furthermore, through obtaining the posterior estimate error by the square-root cubature Kalman filter, prediction and then adapts the three resources for next time epoch, a closed loop feedback allocation scheme is established. The simulation results show that the proposed algorithm can significantly improve the tracking performance compared with the uniform and random resource allocation strategies.

Classification of ground vehicles from the unmanned aerial vehicles (UAVs) is the basis for local precision strikes. In this paper, a method based on micro-Doppler effect which provides unique information of targets is proposed for ground vehicles classification. Firstly, models describing the air-to-ground relationship between ground vehicles and the UAV are built to derive mathematical expressions of radar echo signals. Secondly, Singular Value Decomposition (SVD) is utilized to analyze micro-Doppler components of the ground wheeled vehicle and the ground tracked vehicle respectively. Thirdly, according to classify requirements, specific signal components are restored by Singular Value Reconstruction (SVR). Seven micro-Doppler features are extracted from reconstructed signals. At last, these features are sent to Support Vector Machine (SVM) classifier. Compared with current related methods such as Empirical Mode Decomposition (EMD) and multi-level wavelet decomposition (MWD), experimental results in different cases illustrate the effectiveness of this method. Discrimination performance under various signal-to-noise ratios (SNRs) also proves the robustness of proposed method.

In this paper, a novel clutter suppression and ground moving target imaging approach is proposed for hypersonic vehicle (HSV) borne multichannel (MC) synthetic aperture radar (SAR) system. Compared with the traditional MC SAR ground moving target indication (GMTI) methods, the proposed algorithm can suppress clutter and decrease the moving target energy loss based on the improved MC clutter suppression method in the chirp Fourier transform (CFT) domain. Moreover, the moving target can be accurately focused by compensating the range curvature and the third order phase item. In addition, the motion parameters can be estimated precisely and the moving target can be focused accurately. Finally, the effectiveness of the proposed method is validated by the simulation examples.

The increasing interest on spectrum resources causes various efforts on developing smart and compact solutions as joint radar-communication (JRC) systems. A JRC system can offer cost-effective solution with concurrent operation, as target sensing via radar processing and establishing communication links. JRC capability has been proposed over the years for different types of MIMO radars. However, a JRC capable monostatic coherent MIMO radar system is yet to be developed. These radars offer several advantages as fully coherent signal processing and coherent transmit beamforming which provides beampatterns to minimize probability of intercept. In this paper, two new waveform generation techniques suitable for JRC operation without disturbing transmit beamforming requirements and waveform orthogonality condition in space and time domain are proposed for monostatic coherent MIMO radars. Then, new communication methods are introduced for phase coded monostatic coherent MIMO radars. First method uses chirp-wise information encoding inside the radar pulse as intra-pulse communications. Second rotates the phase of a specific waveform on radiated symbols to a specific direction and the last method applies a small amount of progressive phase shift to the radar waveforms emitted from the antennas to create relative phase modulation between selected radar waveforms. Then, the performance of the proposed communication techniques are investigated in terms of bit error rate (BER) and generated waveforms are examined according to the orthogonality and transmit beamforming requirements., NA

In this paper, we develop a Target-Aware Recurrent Attentional Network (TARAN) for Radar Automatic Target Recognition (RATR) based on High-Resolution Range Profile (HRRP) to make use of the temporal dependence and find the informative areas in HRRP, since it reflects the distribution of scatterers in target along the range dimension. Specifically, we utilize RNN to explore the sequential relationship between the range cells within a HRRP sample and employ the attention mechanism to weight up each timestep in the hidden state so as to discover the target area, which is more discriminative and informative. Effectiveness and efficiency are evaluated on the measured data. Compared with traditional methods, besides the competitive recognition performance, TARAN is also more robust to time-shift sensitivity thanks to the memory function of RNN and attention mechanism. Furthermore, detailed analysis of TARAN model are provided based on time domain and spectrogram features.

A robust cardinalized probability hypothesis density based on interacting multiple model (RCPHD-IMM) is proposed for multiple maneuvering targets tracking under the Doppler blind zone of airborne pulse Doppler radar. This tracking algorithm integrates the advantages of unbiased converted measurements (UCM), interacting multiple model (IMM), and cardinalized probability hypothesis density (CPHD). Under the architecture of the proposed algorithm, an IMM deals with the model switching, and the UCM accounts for nonlinearity in the dynamic system models, whereas the CPHD estimates the number and state of the targets. The gain adjustment matrix is designed to improve the robustness of the proposed algorithm in the blind zone. Furthermore, an adaptive gating strategy is proposed to remove the irrelevant measurements and guarantee the detection of new targets, thereby effectively reduce the computational complexity of the proposed algorithm. Simulation results demonstrate the validity and feasibility of the proposed algorithm. The number and state of the targets in the loss of measurements can be estimated more accurately, and the amount of calculation needed with the RCPHD-IMM algorithm is reduced by 16.03% compared with the traditional CPHD algorithm.

To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of radars, including consideration of waveform design, path planning, and radar selection, which are separately but not jointly taken into account in existing work. The tracking procedure, built on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), is formulated as two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, an illustrative example introduces a specific scenario in 2-D space. Simulation results based on the scenario demonstrate that the proposed framework can accurately track the target under the management of the network of radars.

This paper considers the detection problem of radar maneuvering target with the range migration (RM) and Doppler frequency migration (DFM) and proposes an coherent integration method based on two steps scaling and fractional Fourier transform, i.e., TSS-FrFT. To be specific, this method corrects the quadratic RM (QRM) and DFM based on FrFT and a scaling processing of the rotation angle firstly. Then, it eliminates the coupling between the range frequency variable and FrFT domain (FrFD) variable via a second scaling processing. In the end, the coherent integration is achieved by the inverse Fourier transform (IFT) with respect to the range frequency variable. Compared with the existing methods, the proposed TSS-FrFT method has superior detection performance and low computational complexity. Moreover, considering the sudden change of the target acceleration within the coherent integration interval, this paper also presents an acceleration matched filter (AMF) to remove the Doppler frequency rate change (DFRC) caused by this acceleration change. Computer simulation results are also given to demonstrate the effectiveness of the proposed method from different aspects, i.e., computational complexity, coherent integration for a single target and multiple targets and detection performance for a uniform acceleration and non-uniform accelerations.

In the present work, linear array of four Cylindrical Dielectric Resonator Antenna having beam steering capability has been designed, investigated and validated experimentally. In order to achieve a specific progressive phase distribution, the phase coupled to each array element is controlled by changing the position of power junction feed point in the feeding network. Progressive phase shift between the elements have been achieved by the feeding network itself without the need of any phase shifter and biasing network hence making the array design simple and less costly. The proposed design with impedance bandwidth of 500 MHz at 7.5 GHz frequency along with the gain of nearly 10dBi could be used in radar communication and other wideband wireless applications operating in X-band for moving targets.

Range dependent clutter suppression is a challenging problem in non-side-looking airborne radar, especially in the presence of range ambiguity. To deal with this problem, this paper proposes a two-stage adaptive clutter suppression method. It utilizes the degrees of freedom in range, angle and time domains provided by a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. In the first stage, a novel multi-waveform based adaptive beamforming in joint transmit-receive (Tx–Rx) domain is proposed for range-ambiguous clutter suppression. Analysis on second-order statistic property shows that the clutter data obtained from multiple waveforms with disjoint frequency bands are (approximately) independent and identically distributed (IID) in joint Tx–Rx spatial domain. By exploiting this characteristic, a target-free Tx–Rx covariance matrix is estimated by the IID data, which are extracted from carefully designed secondary waveforms. Therefore, a near-optimal Tx–Rx response is achieved, and range-ambiguous clutter can be suppressed effectively. In the second stage, a new auxiliary channel based space-time adaptive processing approach incorporated with range dependence compensation is devised for residual clutter suppression. It takes full advantage of the prior knowledge of clutter spectrum. Thus, excellent clutter suppression performance can be obtained by the proposed approach. Simulation results demonstrate the effectiveness of the proposed method.