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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.

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.

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

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

This paper addresses the problem of waveform design for multiple-input-multiple-output (MIMO) radar detection in the presence of range-spread clutter. Owing to the intractability of waveform design based on maximizing the detection probability, the design metric employs the relative entropy between the probability density functions of the observations under the two hypotheses (viz., the target is present/absent). We consider several practical constraints on the waveforms, including energy constraint, peak-to-average-power ratio constraint, similarity constraint, and both constant-modulus and similarity constraints. We propose a minorization-maximization approach to tackle the non-convex waveform design problem. We show that the proposed algorithm has guaranteed and fast convergence of objective values. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed algorithm.

In this letter, a monolithic missile radome for application in frequency modulated continuous wave radar (FMCWR) at missile warhead is proposed with improved radiation performance. The radome is cone-shaped and two patch antenna subarrays for transmission and reception are placed off the central line of radome. To address the issues of radiation distortion caused by radome, an improved radome with variable thickness is proposed. Theoretical analysis is conducted to reveal the working principle, which demonstrates that proper condition of radome thickness can minimize the adverse effects of field reflection and phase delay on radiation patterns. By further optimizing the radome thickness through simulation, the ripple variation of main beam is mitigated and radiation patterns become more symmetric. Simulated and measured results show that the radiation performance of the antenna with proposed radome has been significantly improved as compared with that of the antenna with uniform thickness radome (UTR).

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

A Chaotic Differential Evolution (CDE) Algorithm based method for pattern synthesis of MIMO radar is proposed. By optimizing positions and excitation amplitude of the element in receive and transmit array, better control of side lobe level and null depth can be achieved. CDE which is proposed by introducing Chaotic Optimization (CO) mechanism to Differential Evolution (DE), the risk of getting local optimal position can be reduced, thus the premature of DE can be avoid, and the performance of global optimization is improved. Simulation results validate the effectiveness of the proposed method and superiorities.

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.

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.

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.

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.

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.

Through-the-wall radar imaging (TWRI) is receiving a considerable attention recently due to its diverse applications. One of the impinging challenges is the multipath propagation from the surrounding environment and even targets themselves. Multipath propagation produces ghost targets which populate the scene and not only create confusion with genuine targets but deteriorate the performance of compressive sensing (CS) algorithms. Unlike genuine targets, ghost locations are aspect dependent. Successful exploitation of this feature is dictated by the subarray selection modality. Up to this far, random multiple subarrays selection is the practice in exploiting aspect dependence. This paper suggests new subarray configurations based on Pythagorean triple which is made of pairwise coprime numbers that can enhance ghost suppression process and improve image resolution. The sensing matrices of the proposed subarrays are developed and analyzed. The paper investigates the effectiveness of generating the images from all the elements in the array as opposed to generating the images by processing designed subarrays individually and then combining the results. This comparison is done in view of multipath ghost suppression exploiting aspect dependence feature. Results based on synthesized data and electromagnetic propagation simulator show the effectiveness of the proposed arrays.

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.

International audience; In this paper we adopt a statistical approach to optimize the ambiguity function of a radar system. By considering the codes defining the transmitted waveform as realizations of a random variable we firstly show that a suitable distribution of the random variable allows to obtain good codes. Secondly we show that using the chaotic skew tent map it is possible to generate deterministic codes having the desired statistical properties. This allows to obtain an optimized global ambiguity function of the radar system. The advantage of using chaos-based sequences is that they can be easily generated in any length and number. We further improve their performance by introducing down sampling. It appears that the proposed sequences have performance quite similar to those of the sequences of the literature computationally optimized.

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

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

In an emergency scenario, such as earthquake, victims can often be trapped in collapsed trenches or buildings. Search and rescue would be greatly simplified if first responders were equipped with a UltraWide Band (UWB) Radar sensor which has sense-through wall capability. Motivated by this challenge, we study sense-through-wall human detection based on UWB radar sensors. We observed that a Discrete Fourier Transform (DFT)-based approach could not work well in scenarios where signal attenuation is high, and the DFT-based approach has high computational load, which makes it difficult to be used in real-world. We propose a standard deviation (std)-based approach to sense-through wall and sense-through wooden door human detection, and make analysis on detection threshold selection. Our approach is very simple to be implemented, but it has high accuracy. It can achieve perfect detection (no detection error) with appropriate detection threshold. HighlightsWe study sense-through-wall human detection based on UWB radar sensors.We propose a standard deviation (std)-based approach to human detection.Our approach is very simple to be implemented, but it has high accuracy.It can achieve perfect detection (no detection error) with appropriate detection threshold.

Cosmic rays are the highest-energy observable particles in the universe. Their study opens a new frontier for scientists to better understand the nature of the universe. This paper reports our study of a bistatic radar approach that is being developed for remote sensing of cosmic-ray induced air showers. In this context, we propose a robust detection technique based on time-frequency domain for the received radar echoes. These echoes are modeled as linear-downward chirp signals, characterized by very short sweep periods, low energies, and corrupted by non-stationary and non-Gaussian background noise. In addition, the related parameters of the received echoes are variable within some expected ranges, determined by the physical parameters of the air showers. In this paper, we explore the performance of the proposed detection method through an extensive theoretical analysis. We derive formulae for probability of the correct detection, as well as false-alarm rate. Numerical simulations and experimental results that corroborate our analysis are also presented.

Recent development of compressive sensing has greatly benefited radar imaging problems. In this paper, we investigate the problem of obtaining enhanced targets such as ships and airplanes, where targets often exhibit structured sparsity. A novel structured sparsity-driven autofocus algorithm is proposed based on sparse Bayesian framework.The structured sparse prior is imposed on the target scene in a statistical manner. Based on a statistical framework, the proposed algorithm can simultaneously cope with structured sparse recovery and phase error correction problem. The focused high-resolution radar image can be obtained by iteratively estimating scattering coefficients and phase. Due to the structured sparse constraint, the proposed algorithm can desirably preserve the target region and alleviate over-shrinkage problem, compared to previous sparsity-driven auto-focus approaches.Moreover, to accelerate convergence rate of the algorithm, we propose to adaptively eliminate noise-only range cells in estimating phase errors. The selection is conveniently conducted based on the parameters controlling sparsity degree of the signal in the proposed hierarchical model.The simulated and real data experimental results demonstrate that the proposed algorithm can obtain more concentrated images with much smaller number of iterations, particularly in low SNR and highly under-sampling scenarios. HighlightsA novel structured sparsity-driven autofocus algorithm is proposed based on sparse Bayesian framework.To accelerate convergence rate of the algorithm, we propose to adaptively eliminate noise-only range cells in phase error estimation stage.The proposed algorithm can obtain more concentrated images with much smaller number of iterations, particularly in low SNR and highly under-sampling scenarios.

The p-norm detector investigated for coherent multilook detection.Compensator used to improve its performance.Criteria established to select p-norm plus compensator detector's parameters.Enhance detector performance achieved regardless of the number of looks.Application to detection in compound Gaussian clutter with inverse Gamma texture shows excellent results. This paper introduces a new variation of the p-norm detector, which is designed for application to coherent multilook detection in compound Gaussian clutter with inverse Gamma texture. By applying what is termed a compensator, enhanced detection performance can be achieved independently of the number of looks used. This is particularly useful in the case of a fast scan rate radar where the number of looks may be quite small. Conventional coherent detectors tend to experience saturation in such scenarios, and so this new detection process complements recent advances in this area. Further validation is provided by applying this new decision rule to synthetic target detection in real X-band radar clutter.

High frequency passive bistatic radar (HFPBR) is a novel and promising technique in development. DRM broadcast exploiting orthogonal frequency division multiplexing (OFDM) is accepted as the only standard for HF band by ITU-R. DRM broadcast transmitters supply good choice for the illuminator of HFPBR. HFPBR based on DRM broadcast is a beneficial supplement to the active HF radar. HFPBR working in crowded short wave band, as the active radar, faces with radio frequency interference (RFI) problem. The traditional amplitude modulation (AM) broadcasts are ones of the main RFI resources. Unlike the active radar, RFI will affect the reference signal reconstruction quality in HFPBR with OFDM waveform, which will directly affect the performance of direct-path wave and multipath clutter rejection. Direct-path wave and multipath clutter rejection are a stumbling block for target detection of HFPBR. As the direct-path wave and multipath clutter do, RFI also will mask potential target echoes. RFI mitigation has to share the system's degree of freedom with clutter rejection. In order not to occupy the system's degree of freedom, a temporal RFI mitigation algorithm is proposed to cope with the AM interferences, after RFI model and impacts analysis on HFPBR with new radar waveform. The proposed algorithm is a closed-form approximate maximum likelihood estimator, which is easy to perform. The impacts analysis of RFI and the RFI mitigation algorithm performance are evaluated using the simulation and experimental data of DRM-based HFPBR.

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.

SAR images despeckling based on deep learning attracted more researchers’ attention because of its performance and computational efficiency. In order to embody recent improvements for SAR images despeckling models, we proposed a model base on deep learning, called Synthetic Aperture Radar (SAR) image despeckling with Convolutional Neural Networks (SID-CNN), which is based on residual learning and discriminative training. In this model, the multiplicative noise in the SAR image was removed by residual learning and discriminative training in the form of additive noise. Whilst we illustrated the capability of the proposed SID-CNN model on synthetic images and real SAR images, respectively. Finally, compared with the-state-of-art SAR image despeckling methods, extensive experiments demonstrated that the proposed model had a better capacity to remove SAR images speckle with high restoration quality and computational efficiency.

In this study, it is aimed to improve the maximum range and range resolution while detecting multiple targets’ respiration rates. An original algorithm has been proposed for this purpose, and a Stepped Frequency Continuous Wave radar has been set up for experiments. Experiments have been executed with periodically moving plates and human targets. With a resolution of 30 cm, the detected maximum ranges are 7 m and 6.3 m for moving plate and human targets, respectively. In moving plate experiments, the average accuracies of the frequency measurements are above 98% for both single and multiple-target scenarios. In human target experiments, the average accuracy of the respiration rate measurements is 96.58% for single target experiments while it is 94.44% for multiple targets. The results show that the proposed structure outperforms the state-of-the-art benchmark in terms of the capability of sensing the respiration rate in a wide range with a high resolution.

Due to inherent sparsity of ISAR images, compressive sensing theory has been used to obtain a high resolution image. However, before applying sparse recovery methods, the phase error due to the translational motion of target is compensated by autofocusing algorithms and the target rotation rate is estimated by cross-range scaling methods. In this paper, a comprehensive matrix model for a uniformly rotating target that includes the phase error and chirp-rate of the target is derived. Then by using sparsity and minimum entropy criterion, the estimation of residual phase error and the rotation rate is refined. In order to reduce the computational load, we simplify the model and by an iterative method based on adaptive dictionary, the phase error and chirp-rate are estimated separately. Actually, by exploiting a two-dimensional (2D) optimization method and the Nelder–Mead algorithm the phase adjustment is performed and the chirp-rate is estimated by solving a 1D optimization method for dominant range cells of the target. Finally, both simulation and practical data have been used to verify the validity of the proposed approach.

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

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.