Search

This paper focuses on the design of a frequency modulated continuous wave (FMCW) dual-functional radar-communication (DFRC) system with low-loss radar detection performance, aiming at achieving a balance among communication rate, symbol error rate (SER), and out-of-band leakage. For what the communication functionality concerns, the constrained frequency hopping (C–FH) sequence mapping and up/down chirp are used to embed data, limiting the out-of-band leakage. We provide explicit rules for traversal-FH sequence to adjust the number of configurable frequency points within each chip, and limit the SER at the receiver. For the radar signal processing, after coherent processing and sampling, we proceed time-domain extension and range-bin phase compensation (TE-RBPC) according to the FH value, achieving the same range profile as an unmodulated (FMCW) without any influence from the FH sequences. The simulation results show that the proposed C–FH-chirp waveform reduces the out-of-band power leakage by 10 dB if compared with a phase shift keying scheme, and provides a 5 dB improvement in the signal noise ratio under the same SER if compared with an unconstrained FH-chirp scheme. Moreover, by comparing it with a scheme without compensation, the sidelobe amplitude of radar range profile in TE-RBPC scheme is reduced by about 39 dB. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of mainlobe deceptive jammer suppression via joint transmit–receive design in a multiple-input multiple-output (MIMO) radar. At the design stage, a discrete element-pulse coding (DEPC) phase is implemented in both the slow-time pulses and transmit array antennas. After decoding and compensation of the delayed pulses, the true and false targets can be identified in the joint transmit–receive (JTR) spatial frequency domain. Furthermore, to eliminate the false targets, a data-dependent beamforming technique is adopted, where an optimization problem is constructed to maximize the output signal-to-interference-plus-noise ratio (SINR) by optimizing the DEPC coefficients and the weight vector of the receive filter in an alternating way. Specifically, two methods, including the Single Jammer Suppression (SJS)-Alternating Direction Method of Multipliers (ADMM) and Multiple Jammers Suppression (MJS)-Gradient Descent (GD) methods are developed for the cases of a single jammer and multiple jammers, respectively. A series of numerical results are utilized to verify the effectiveness of the proposed methods for mainlobe deceptive jammer suppression. • The DEPC-MIMO scheme is proposed by discretizing element pulse coding coefficients. • The true and false targets are distinguished in the transmit–receive spatial domain. • The false targets are suppressed by optimizing DEPC coefficients and weight vector. [ABSTRACT FROM AUTHOR]

Waveform design is a significant topic in multiple-input-multiple-output (MIMO) radar. In this paper, we address the problem of waveform design for colocated narrowband MIMO radar transmit beampattern synthesis. The existing methods have not comprehensively considered the transmit beampattern synthesis under multiple practical constraints, such as constant modulus constraint, Doppler tolerance, high range resolution and low auto-correlation sidelobe. We consider these practical constraints and propose a novel chirp rate diversity-based linear frequency modulated (CRD-LFM) waveform design for approximating the desired MIMO radar transmit beampattern. To tackle the problem, we improve the classical two-step approach. Firstly we design the covariance matrix using the existing closed-form solution. Then we model the problem of obtaining the waveforms from the covariance matrix as a nonlinear optimization problem, and we utilize the Sequential Quadratic Programming (SQP) algorithm to solve it. The simulations demonstrate that the waveforms obtained by this method can well approximate the desired transmit beampattern and have higher range resolution. Furthermore, we verify that the suppression of auto-correlation sidelobe can be achieved using amplitude weighting, which is easily realizable in practical engineering. • A chirp rate diversity-based waveform design method for MIMO radar is proposed. • It is verified that varying the chirp rate of waveforms can adjust the beampattern. • An improved two-step method for MIMO radar waveform optimization is proposed. [ABSTRACT FROM AUTHOR]

• The problem of Vandermonde constrained CP tensor decomposition for angle estimation in MIMO radar under spatially colored noise is addressed. • The smoothed estimate of the temporal cross-correlation matrix is presented to provide improved robustness to colored noise in the case of the limited number of pulses. • Constrained alternating least squares (ALS) based on new rectification strategy for Vandermonde factors is used to solve CP decomposition model. • With the help of temporally smoothed cross-correlation and constrained ALS, the proposed method exhibits superior angle estimation performance. We address the problem of Vandermonde constrained CANDECOMP/PARAFAC (CP) tensor decomposition in application to angle estimation for bistatic multiple-input multiple output (MIMO) radar under spatially colored noise. By exploiting the temporally uncorrelated characteristic of colored noise, a new denoising scheme based on the temporally smoothed cross-correlation approach is presented. Then, after rearranging the smoothed cross-correlation matrix into a fourth-order tensor, a Vandermonde constrained CP tensor decomposition model is formulated, which fully exploits the multidimensional structure of the array measurement and the Vandermonde structure of the factor matrices. To solve this model, an efficient constrained alternating least squares (ALS) algorithm is developed to decompose the Vandermonde factor matrices. Finally, joint estimates of direction of departure (DOD) and direction of arrival (DOA) are obtained by the generators of the Vandermonde factor matrices. Compared with the state-of-the-art approaches, the proposed method can achieve better angle estimation performance by jointly using the temporally smoothed cross-correlation denoising operation and enforcing the Vandermonde structure information in the tensor decomposition. Numerical simulation results verify the effectiveness and improvement of our algorithm. [ABSTRACT FROM AUTHOR]

Most of the previous radar automatic target recognition (RATR) methods based on high resolution range profile (HRRP) are designed under the assumption of complete target-aspects, which assumes the HRRP samples with different target-aspects are complete in training dataset or template library. Few works were concentrated on HRRP RATR with incomplete target-aspects. However, it is extremely difficult and sometimes even impossible to obtain HRRPs with complete target-aspects in real world applications. Therefore, it is required to recognize targets of unseen target-aspects with an incomplete target-aspect template library. Aiming at this problem, a scattering center neural network (SCNet) is proposed for radar HRRP target recognition with incomplete target-aspects. Based on the assumption that an HRRP sample can be represented by a linear combination of a few atoms from a scattering center dictionary, we proposed a scattering center layer (SC-layer), which encapsulates the dictionary coding into an implicit layer and treats the scattering center dictionary as its parameter that is optimized in an end-to-end manner. We further proposed a discriminative target-aspect frame dictionary by dividing HRRPs of each class into multiple target-aspect frames and associating frame label information with each atom of it to enforce discriminability in features. A multiple target-aspect prototype classifier is proposed, which is more robust to intra-class variations and thus more suitable to handle the incomplete target-aspects problem compared with softmax classifier. The classification is simply implemented by matching HRRP features with each prototype and each HRRP is assigned to the class with the nearest prototype. In order to integrate both intra-class compactness and inter-class separation, we proposed a novel discriminative prototype loss. Taking the advantage of the discriminative prototype loss, HRRPs belonging to the same class and the same target-aspect frame are pulled closer to the corresponding prototype in the feature space, while simultaneously pushing apart from other prototypes of different classes. Experiments on the aircraft electromagnetic simulation dataset and the measured dataset demonstrated the superior performance of the proposed method compared with other HRRP-based RTAR methods under the condition of incomplete target-aspects. • SCNet is based on that HRRP can be represented by a scattering center dictionary. • SC-layer is an implicit layer and treated scattering center dictionary as parameters. • Frame label is associated with atom of discriminative target-aspect frame dictionary. • Multiple target-aspect prototype classifier is more robust to intra-class variations. • Prototype loss integrated both intra-class compactness and inter-class separation. [ABSTRACT FROM AUTHOR]

• A novel space-time signal model for STAP radar based o n subarray based partly calibrated arrays (PCA) is developed. • The grid-based sparse representation of the signal model for the PCAs is presented. • We proposed a novel SR-STAP algorithm utilizing the low rank and block-sparsity promoting ℓ * , 1 , mixed-norm minimization along with an equivalent compact expression, termed as joint Low Rank and Block Sparse Recovery based STAP (LRBSR-STAP). • We proposed a gridless implementation of our LRBSR formulation for the special case of identical linear subarrays with a common baseline, referred to as GridLess joint Low Rank and Block Sparse Recovery based STAP (GL-LRBSR-STAP). • Extensive numerical experiments are conducted, and the results shows that the proposed algorithms consistently and substantially outperform all the other comparative algorithms with inter-subarray gain-phase and displacement errors under small sample conditions. Sparse recovery based space-time processing (SR-STAP) techniques are capable of achieving satisfactory clutter suppression and target detection performance, even with limited training samples. The majority of existing approaches are developed under the assumption of ideal uniform linear arrays, where no imperfections are present. In this study, we consider the robust STAP problem in the context of airborne partly calibrated array (PCA) composed of several well calibrated subarrays with unknown inter-subarray gain-phase and displacement errors. Initially, the space-time signal for airborne STAP radar based on PCAs is modeled. Subsequently, the authors propose two robust SR-STAP algorithms, considering both grid-based and gridless methods, leveraging the block-sparsity and low rank structural characteristics inherent in the signal model. Extensive numerical experiments have shown the significant performance benefits including small sample applicability and resilience to inter-subarray gain-phase and displacement errors. [ABSTRACT FROM AUTHOR]

The recently developed frequency diverse array (FDA) radar applies a small frequency increment across the array elements, which provides advantages in clutter suppression, jamming suppression, and target detection for existing radar systems. In this paper, the frequency increment is enlarged to guarantee that the transmit waveforms are orthogonal in frequency domain for multiple-input multiple-output (MIMO) radar. A multi-scale moving target detection method of FDA-MIMO radar is proposed. The FDA radar with a large frequency increment suffers from the deteriorated decorrelation of scatterers within the same range bin and causes the inner-bin range dependence (IRD) property in transmit spatial domain. By taking additional space–time snapshots in transmit domain as training samples, the space–time adaptive processing (STAP) is applied in joint receive and Doppler domain for clutter suppression, which alleviates the requirement of insufficient training samples in non-stationary clutter environment. In the sequel, a set of beamformers within the whole angular domain of interest is utilized for target detection, which achieves target detection under the condition of random target locations and sizes. Numerical simulation results are provided to verify the effectiveness of the proposed method. • FDA radar of large Δ f can realize waveform orthogonality and obtain range DOFs. • FDA of large Δ f improves the range resolution to distinguish inner-bin scatterers. • The proposed STAP method alleviates the pressure on insufficient training samples. • A set of beamformers are designed to detect target with random locations and sizes. [ABSTRACT FROM AUTHOR]

• The proposed NRFRP in time dimension can accurately eliminate DFM with a onestep process of matrix multiplication, which greatly reduces the computational burden. • The introduced MSCFT in time dimension corrects RWM accurately without the Doppler ambiguity effect. Furthermore, due to the proper selection of scale factor, the DFM in space dimension can be omitted, and the search operation of the Doppler center can be avoided. • The proposed STHI algorithm can significantly improve the detection performance of maneuvering targets compared with the time dimension integration only. • The proposed algorithm can compensate for Doppler spectrum ambiguity, which improves the robustness of the algorithm effectively. Moving target detection in spaceborne distributed aperture radar (SBDAR) is a challenging task in civilian and military fields. The common issues are that the current methods suffer from the large range cell migration (RCM) and time-varying Doppler frequency modulation (DFM) problems in space-time two-dimensional (2D) domain, which is caused by distributed aperture radar detection configuration and the maneuvering characteristics of moving targets. To address these issues, a novel SBDAR moving target detection approach via space–time hybrid integration (STHI) is proposed in this paper. Compared with single or bistatic radar mode, the utilization of multiple satellites provides an improvement in moving target detection performance, together with the capabilities of localization and parameters estimation. The proposed method mainly includes the following three steps. First, the geometric and signal models of an observed maneuvering target are accurately established in a SBDAR framework, where the space-time 2D RCM and DFM are analyzed. Based on that, the second-order Keystone transform (SKT), a novel range frequency reversal process (NRFRP) and a modified scaled Fourier transform (MSCFT) are developed to correct the RCM and DFM, achieving an efficient time dimension coherent integration for maneuvering target. After that, by dividing the radar detection area, the spatial projection method is adopted to integrate the moving target energy in space dimension. Finally, the simulation results verify the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The issue of adaptive detection is considered for the colocated multi-input multi-output (MIMO) radar in this paper. Meanwhile, the background is partially homogeneous environments (PHE), where the power mismatch is present between the training data and test data. The training data in PHE are utilized to derive effective detectors on the basis of generalized likelihood ratio test, Rao, Wald, Gradient, and Durbin tests. Since the Durbin test-based detector coincides with the Rao test-based detector, the two-step design approach for the Durbin test is used for deriving the new detector. The outcomes of simulation experiments illustrate that the proposed detectors achieve superior effectiveness to existing approaches. Furthermore, the results also show that when the signal mismatch exists, the Wald and Durbin tests maintain robust characteristics. Meanwhile, the Rao test ensures selective property. [ABSTRACT FROM AUTHOR]

For pulse Doppler radars, targets become undetectable for Doppler frequencies in the detection-blind range occupied by the clutter Doppler spectrum. To overcome this problem, different Pulse Repetition Frequencies (PRFs) are used to search for targets with Doppler ambiguity and estimate their velocity, which is required for target tracking. Owing to the different PRFs used for velocity estimation, many beam dwells are required for illuminating the targets, Doppler compensation cannot be realized in a short time. In this paper, we propose a novel velocity estimation method that uses multicarrier signals in a single dwell. First, we obtain separated subcarrier signals from the echo signal and multiply some separated subcarrier signals by other separated subcarrier signals in a conjugate manner, obtaining Frequency-Converted Subcarrier (FCS) signals. We derive the potential ambiguous and unambiguous Doppler frequencies for the separated subcarrier and FCS signals, respectively, using a slow-time Discrete Fourier Transformation (DFT). Subsequently, the actual Doppler ambiguity number is derived from the results. Finally, the unambiguous Doppler frequency and target velocity are inferred. The effectiveness and superiority of the proposed method are validated by performing simulation studies. • Extending the unambiguous Doppler range. • Simple implementation. • Solving Doppler ambiguity at a PRF. [ABSTRACT FROM AUTHOR]

In this paper, we propose a pulse train coding and decoding matrix design based electronic counter-countermeasure (ECCM) scheme for multiple-input multiple-output (MIMO) radar against interrupted sampling repeater jamming (ISRJ). The ECCM scheme includes two steps: jamming pulse compression (PC) sidelobes suppression and jamming peaks identification. By joint design of coding and decoding matrix, the proposed ECCM scheme can simultaneously suppress PC sidelobes of target echoes and jamming signals. Compared with the existing methods, the proposed scheme can achieve the improvement in jamming suppression and target detection without signal-to-noise ratio loss, and the coding and decoding matrix design can be realized offline. Besides, the phase-only coding matrix can be directly applied to the MIMO radar with multi-beam transmit beampattern. After sidelobes suppression, an identification matrix is proposed to identify the jamming peaks. In specifical situation, the identification process does not require ISRJ parameter, and the ISRJ parameter estimation can be converted into a convex optimization problem in other situations. At last, simulated results corroborate the effectiveness of the anti-ISRJ performance with the proposed ECCM scheme. • An overall ECCM scheme is proposed, including ISRJ sidelobes suppression, ISRJ parameter estimation and ISRJ peaks identification. • The proposed ECCM scheme can be directly applied to the MIMO radar with multi-beam. • In a particular case, the proposed ECCM scheme can perform without ISRJ parameters. • The proposed ECCM scheme does not suffer SNR loss. [ABSTRACT FROM AUTHOR]

Frequency diverse array (FDA) multiple-input multiple-out (MIMO) radar has an inherent higher-dimensional data structure that can be exploited in temporal, spatial, and range-dependent dimensions for space–time-range adaptive processing (STRAP). This paper addresses the problem of tensor filters in application to space–time-range clutter suppression for FDA-MIMO radar. The characteristics of space–time-range clutter are discussed to establish the multidimensional signal models. FDA-MIMO radar can divide the secondary range cell within the range resolution cell of MIMO radar when they have the same matching filter bandwidth in the receive channel. Specifically, the target-free covariance information can be estimated from more secondary training cells for FDA-MIMO radar. According to the discussed properties of space–time-range clutter, namely secondary clutter rings, clutter degrees of freedom (DOFs), and the range-dimensional non-stationary characteristic, a tensor-based STRAP approach with range compensation is proposed to suppress clutter for FDA-MIMO radar. Numerical results verify the mentioned clutter characteristics and illustrate the importance of the samples number and range compensation operation for the proposed tensor filter. Finally, the STRAP performance benefits of FDA-MIMO radar over MIMO radar are discussed by simulations. • For the same bandwidth, FDA-MIMO radar can divide more secondary range cells within one range cell than MIMO radar. • The space–time-range clutter of FDA-MIMO radar has the range-dependent non-stationary property. • The data cube obtained in array dimension, pulse dimension and range dimension can be directly processed by tensor methods. • The tensor-based filter utilizing range compensation can effectively suppress clutter for FDA-MIMO radar. [ABSTRACT FROM AUTHOR]

The detection of multiple targets in an enclosed scene, from its outside, is a challenging topic of research addressed by Through-the-Wall Radar Imaging (TWRI). Traditionally, TWRI methods operate in two steps: first the removal of wall clutter then followed by the recovery of targets positions. Recent approaches manage in parallel the processing of the wall and targets via low rank plus sparse matrix decomposition and obtain better performances. In this paper, we reformulate this precisely via a RPCA-type problem, where the sparse vector appears in a Kronecker product. We extend this approach by adding a robust distance with flexible structure to handle heterogeneous noise and outliers, which may appear in TWRI measurements. The resolution is achieved via the Alternating Direction Method of Multipliers (ADMM) and variable splitting to decouple the constraints. The removal of the front wall is achieved via a closed-form proximal evaluation and the recovery of targets is possible via a tailored Majorization–Minimization (MM) step. The analysis and validation of our method is carried out using Finite-Difference Time-Domain (FDTD) simulated data, which show the advantage of our method in detection performance over complex scenarios. • Reformulation of TWRI detection via RPCA. • Addition of a structured robust distance for heterogeneous noises. • Resolution via ADMM coupled with variable splitting. • Analysis of the proposed the method on FDTD simulations. [ABSTRACT FROM AUTHOR]

• We propose the strategy of "Separation + Recognition" for solving the problem of the number limit of compound types. • We separate the observed compound jamming signals from multiple receiving channels. • A fast recovery algorithm to recover and identify the separated signals. To deal with various jamming signals based on digital radio frequency memories, a compound jamming signal recognition method based on source signal separation is proposed. In order to overcome label limitation of the supervised learning method in the recognition process, this paper puts forward "Separation + Recognition" strategy. Firstly, the received signals of multiple channels are preprocessed, and the number of signal sources is analyzed through the single-source detection algorithm. Then the received compound jamming signals are processed by source separation, and the independent single jamming signals can be obtained. On this basis, a fast signal compensation algorithm is added to compensate different source signals at overlapping time-frequency points, which effectively increases the integrity of the separated signals. The separated jamming signals are then put into the convolutional neural network for recognition, and the specific jamming types in the compound jamming signals can be obtained. It has been proved that the recognition accuracy of five kinds of compound jamming exceeds 90% when the jamming-to-noise ratio is 0 dB. [ABSTRACT FROM AUTHOR]

Range ambiguity is a significant challenge in conventional pulse-Doppler radar systems with medium or high pulse repetition frequencies, greatly impacting radar detection and imaging capabilities. The classic approaches such as waveform diversity and compressed sensing are based on the isolation between pulses. However, these methods encounter issues like residual ambiguity and grid mismatch, resulting in poor range ambiguity suppression performance. In this paper, the multiple signal classification-alternating projection (MUSIC-AP) method is proposed to address these challenges and achieve high-resolution estimation. The proposed method converts the range-velocity estimate into a spectrum estimate, enabling the utilization of the MUSIC algorithm to solve the grid mismatch problem and improve echo reconstruction. Then, to mitigate the effect of ambiguous energy on reconstruction, the MUSIC reconstruction subprocess is embedded into the AP framework. Through iterative approximation, the method gradually suppresses ambiguous and noise energy, thereby further enhancing the echo reconstruction accuracy and range ambiguity suppression performance. Simulations demonstrate the effectiveness of the proposed method. Moreover, algorithm analysis is conducted, considering aspects such as resolution, robustness, and sparsity, which provides a basis for parameter selection and applicability conditions. [Display omitted] • The range-velocity estimation is transformed into spectrum estimation problem. • Unambiguous range-velocity high-resolution estimation via the MUSIC algorithm. • The MUSIC is embedded into AP outer frame to gradually suppress ambiguity and noise. [ABSTRACT FROM AUTHOR]

• The article proposes a learnable method named TEFISTA-Net for high-resolution range profile reconstruction (HRRP) with low-frequency ultra-wideband radar, which is an unrolling network with target enhancement capability. • The method achieves lower computational complexity and reduced difficulties in algorithm parameter tuning, compared with existing competitors based on compressed sensing. • The proposed network structure can effectively reduce background artifacts and improve the robustness of HRRP reconstruction. • Simulated and measured radar data demonstrate the performance advantages of the proposed method, in terms of the reconstruction accuracy and target saliency of HRRP. In low-frequency ultra-wideband (LFW) radar, the reconstruction of high-resolution range profile (HRRP) is an important task. The state-of-the-art compressive sensing (CS) method for HRRP reconstruction based on geometrical theory of diffraction (GTD) requires manual algorithm parameter tuning and is computationally expensive. In this paper, we propose a deep learning-based CS method for LFW radar HRRP reconstruction. We design a neural network architecture, i.e., target enhancement-based FISTA-Net (TEFISTA-Net), by unrolling the fast iterative shrinkage thresholding algorithm (FISTA). A new loss function based on the target-to-background ratio (TBR) is introduced for network training with the target enhancement capability in low signal-to-noise ratio (SNR) scenarios. The algorithm parameters in the traditional CS methods are substituted by network parameters learned from training data, getting rid of manual parameter tuning. In addition, simple convolution operations in our new method lead to lower computational complexity compared with existing methods. Experimental results based on diverse data show that the proposed method leads to higher computational efficiency with similar or better performance in comparison with existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, we deal with the point-like target fusion detection in a partially homogeneous environment with distributed MIMO radar. Specifically, we consider the imperfect waveform separation problem, which means that the matched filter output contains not only the auto-correlation term of the current matched waveform but also the cross-correlation terms, called waveform residuals, with the remaining transmitted waveforms. To this end, a hybrid-order Gaussian (HOG) model is utilized, where the target amplitude is deterministic but unknown, and the waveform residuals obey the Gaussian distribution. Then two adaptive detectors are developed according to the GLRT and Wald test, named HOG-GLRT and HOG-Wald respectively. At the fusion detection stage, we focus on the problem of signal-to-noise ratio (SNR) diversity, i.e., the detection performance degradation due to the average weighting of spatial diversity channels when the echo SNR differs. Combined with the Model Order Selection criterion and multiple hypothesis test, two modified fusion detectors based on channel selection are proposed, named MHOG-GLRT and MHOG-Wald. Finally, the numerical simulation results show that the HOG-GLRT is sensitive against waveform residuals, while the HOG-Wald exhibits strong robustness. And it also demonstrates the effectiveness of the MHOG-GLRT and MHOG-Wald facing the extreme scene of SNR diversity. • We propose two detectors for distributed MIMO radar under imperfect waveform separation. • We develop two modified detectors to solve the problem of SNR diversity. • These detectors can improve the detection performance compared with the existing ones. [ABSTRACT FROM AUTHOR]

• A new decision tree with preference is proposed to build a feature-based detector for small target detection in sea clutter. • Novel splitting and pruning processes are developed to meet the unbalanced demands of error probabilities and samples in radar target detection. • The proposed detector breaks the dimension limitation and has good performance and generalizability. It is difficult for maritime high-resolution radars to realize the small target detection in sea clutter, due to weak target returns and complicated clutter characteristics. Cooperation of multiple features is a recognized way to distinguish target returns from clutter. Therefore, it becomes crucial to build a detector, a special classifier, with unbalanced training samples, i.e., ergodic clutter versus non-ergodic target samples. In this paper, a preferential decision tree (pre-decision tree) with the oblique stopping criterion is proposed, where a preferential Gini index (pre-Gini index) is defined to replace the Gini index and considers rigorous false alarm rates and tolerable missed probabilities for radar target detection. Then, an improved pruning is added to the pre-decision tree to generate a dominant clutter tree, which can accurately control the false alarm rate. The two-step decision is based on the anomaly detection framework and solves the unbalance of the training samples. The proposed method can work in the high-dimensional space directly, and its decision only involves linear operations. The experimental results on the recognized IPIX and CSIR databases illustrate that the proposed method performs well among the available feature-based detectors. [ABSTRACT FROM AUTHOR]

A chirp cyclostationary (CCS) process is a nonstationary stochastic process, whose statistics have been defined and utilized in radar and communication signal processing. However, spectrum analysis of CCS signals, which provides a spectral view to characterize a signal, has not been performed. Classical spectrum analysis is based on the Fourier transform, which is not suitable for CCS signals because of the multiplicative chirp signals in a CCS model. In this study, we propose a generalized spectral representation of chirp cyclostationary signals associated with a linear canonical transform. The proposed representation shows that a CCS signal can be represented by a linear combination of a series of jointly stationary and equal-bandwidth processes, which bridge the nonstationary and stationary processes. Furthermore, the relationships between the second-order statistics of the CCS process and its stationary representations are introduced. Finally, based on the above observations, a linear time-varying Wiener filter was designed for CCS signal denoising and time-varying channel estimation. Finally, the relationships between the nonstationarity of the input and output and time-varying properties of the systems are discussed, which are helpful for determining the nonstationarity of the outputs or the time-varying property of a system. • A generalized spectral decomposition of chirp cyclostationary processes is proposed. • The CCS process is proven as a linear combination of joint stationary processes. • Statistics of the CCS process are linked with those of the stationary components. • A linear chirp periodically time-varying chirp cyclic Wiener filter is designed. [ABSTRACT FROM AUTHOR]

The coexistence of clutter range ambiguity and range dependence in the airborne forward-looking radar may render the common space–time adaptive processing (STAP) ineffective. To cope with this challenge, a novel vertical element-pulse coding (EPC) radar is proposed in our paper. Note that both the elevation angles and coding factors determine the transmitted spatial frequency of the proposed vertical EPC radar. Therefore, the clutters corresponding to different ambiguous areas can be distinguished in the spatial frequency dimension based on this property. The element-pulse decoding and pre-STAP filtering are devised to separate and extract these clutters corresponding to different ambiguous areas. In the following, the clutter elimination is independently accomplished for each ambiguous area that has been separated. Consequently, the clutter range ambiguity as well as clutter range dependence are addressed simultaneously. The efficacy of the proposed approach is verified with simulation experiments. • A general signal model of vertical EPC array radar is established. • Clutter properties are analyzed in the vertical spatial frequency domain. • Range-ambiguous clutter is separated using element-pulse decoding and pre-filtering. • The design criteria of the element-pulse coding factor are derived. [ABSTRACT FROM AUTHOR]

Mechanically steered scanning radars receive measurements in different azimuths sequentially rather than simultaneously for target detection and tracking. However, in conventional track-before-detect (TBD) methods, the requirement of waiting for all measurements of a whole scan, referring to a certain azimuth, leads to significant processing delays and boundary effect, which appears as that targets around the boundary may be scanned 0 or 2 times in a scan. This may also cause degraded detection and estimation performances. To solve these problems, a continuous TBD method is proposed for target detection and tracking in rotating radars. A space–time joint retrodiction method is presented to produce precise revisit intervals and retrodicted positions, which contribute to immediate and accurate energy integration. Once measurements in an azimuth cell are received, each range cell in this azimuth is retrodicted according to the space–time joint retrodiction method with an assumed velocity. Then, a number S of pseudo-spectra are constructed around the cell and its retrodicted positions in the past S − 1 scans. Samples of each pseudo-spectrum are added onto corresponding cells for energy accumulation. After that, some cells in the past (S − 1) th scan and their guard and reference cells complete energy integration, and thus are detected immediately. Continuous integration and detection are realized without waiting for measurements at the boundary of a scan. This eliminates the boundary effect and minimizes the processing latency. Experimental results corroborate the validity of the continuous TBD method. • Problems of processing delay and boundary effect in rotating radars are considered. • A space–time joint retrodiction method is proposed. • A continuous track-before-detect method based on the pseudo-spectrum is proposed. • The processing delay is minimized, and the boundary effect is eliminated. • Continuous integration and detection are realized. [ABSTRACT FROM AUTHOR]

Retransmission interference in radar and communication signal processing significantly threatens the received linear frequency modulation signal. With the advancement in the hardware capabilities of interference devices, they can intercept and retransmit signals within the duration of a single pulse. Therefore, we refer to this type of interference as intra-pulse retransmission interference, which includes retransmission modes smeared spectrum, interrupted sampling repeater jamming, and interrupted nonuniform sampling repeater jamming. Although traditional interference cancellation techniques offer optimal solutions under the assumption of fully known interference parameters, practical scenarios often involve unknown parameters, estimation errors, random parameters, and multiple interferences, leading to performance degradation. To address these problems, we propose a Multi-Residual Encoder-Decoder Network (MRDENet), a novel approach for effectively mitigating intra-pulse retransmission interferences and recovering the signal of linear frequency modulation. In contrast to parameter estimation and interference cancellation, MREDNet extracts crucial features of both the target and interference and performs reconstruction. More specifically, MREDNet acquires information about the retransmission pattern of interference and the modulation frequency through its multi-level residual shortcut connections. To address the issue of network parameters being real while the signals are complex, we devised decomplexification—a data preprocessing approach enabling neural networks for direct utilization in front-end complex signal processing. Besides, we evaluate the performance of MREDNet comprehensively. The experimental conditions encompassed single and multiple interferences, incomplete data sets, varying signal-to-noise ratios, and jamming-to-signal ratios. The computational simulations demonstrate that MREDNet successfully solves the problem of recovering linear frequency modulation signals interfered with by intra-pulse retransmission interference. According to the boundary condition of MREDNet, the proposed method exhibits valuable applications in real-world scenarios. • We propose a recovery network named MREDNet, which is employed for recovering LFM signals interfered by intra-pulse retransmission interference. • In contrast to other methods, MREDNet possesses the capability to batch-process multiple pulses and handle multiple interferences. • MREDNet is applicable in scenarios with varying interference types between the test and training data sets. • Simulations demonstrate that MREDNet still performs well even under conditions of high signal-to-interference ratios and low signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

• The multi-model belief propagation method implemented using Gaussian mixture. • Extracting sensor association measurements using greedy algorithm. • Fusion of all association measurements using the belief iterative updating algorithm. • Multisensor-multitarget three-dimensional tracking simulation scenario with clutter. This paper presents a novel Gaussian mixture multi-model belief propagation (GMM-BP) filter for maneuvering multitarget tracking with multiple sensors. The filter is built upon the BP-based multisensor-multitarget tracking scheme, enabling accurate estimation of target numbers and states. It assumes linear Gaussian target motion, birth process, and sensor measurement models and utilizes the Gaussian mixture model to represent the target's marginal posterior probability density function. Additionally, multiple models are incorporated for target motion, enhancing the maneuvering target tracking capability. Closed-form recursions for means, covariances, and weights of Gaussian components in the filter are derived. To enhance scalability with the number of sensors, a simplified multi-sensor data fusion process is proposed, thereby preserving a linear relationship between Gaussian components and the number of sensors. The performance of the filter was evaluated in a three-dimensional (3D) simulated environment. Compared to the particle-implemented BP filter and the labeled multi-Bernoulli (LMB) filter, the proposed GMM-BP filter exhibited a significant improvement in computational efficiency while maintaining a high level of tracking accuracy. [ABSTRACT FROM AUTHOR]

To improve the detectability of extended targets, a novel mismatched filter is designed, which deliberately broadens the 3 dB mainlobe width (MW) of the output response. Similar to how narrowband radar accumulates target energy, this filter concentrates the energy of scattering centers into the same resolution unit. To achieve this, constraints on 3 dB MW, mismatched filter gain, and signal-to-noise ratio (SNR) are imposed on the mismatched filter, while the integration sidelobe level (ISL) is minimized as the figure of merit. To handle the resulting non-convex problem, a polynomial-time method based on sequential convex approximation (SCA) which slacks the non-convex constraint is proposed to decrease ISL. Numerical results demonstrate successful broadening of the 3 dB MW within a certain range, and efficient accumulation of extended target energy. [ABSTRACT FROM AUTHOR]

The existing event-triggered (ET) strategies for distributed multi-target filter commonly employ triggering conditions focusing on the instantaneous information discrepancy. Although this approach can save communication resource, it suffers from its sensitivity to instantaneous disturbance and outliers that results in unnecessary communications. In this correspondence, an integral-type event-triggered mechanism is integrated with the consensus-based labeled multi-Bernoulli filter (LMB). The proposed algorithm provides a passivation effect that lowers the communication activities while preserving the tracking performance in the uncertain environment with disturbance. The theoretical analysis demonstrates that the integral-type event-triggered mechanism introduces bounded information discrepancy in the sense of Kullback–Leibler (KL) divergence. The proposed algorithm is also verified by numerical simulations in a target tracking scenario. • Integral term is introduced into the ET-based consensus-based LMB filter. • The proposed solution is insensitive to the instantaneous disturbance and outliers. • The theoretical analyses of the stability are provided. [ABSTRACT FROM AUTHOR]

• The block tridiagonal structure constraint is proposed to model long-time sea clutter sequences. • Fast inversion algorithm of block tridiagonal speckle covariance matrices is given. • Long-time adaptive GLRT-LTD is presented and verified by X-band measured radar data. Low-velocity small target detection in maritime surveillance radars is always a challenging task. Low signal-to-clutter ratio requires long-time coherent integration to obtain enough gain of target returns. However, long-time coherent integration encounters insufficient secondary data due to the spatial inhomogeneity of sea clutter. In this paper, considering the decorrelation time of speckle component of sea clutter short up to a dozen of milliseconds, the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and the inverse Gamma distributed texture is proposed to model sea clutter sequences in several tenths of a second. In this model, a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD) is constructed. Owing to the block tridiagonal structure of speckle covariance matrices, the adaptive detection requires much less reference cells for speckle covariance matrix estimation and much lower computational cost for its inversion. The proposed detector is verified by an X-band high-resolution island-based radar data with an unmanned aerial vehicle (UAV) as test target. The experimental result shows that it obtains competitive detection performance in comparison with the state-of-the-art detectors. [ABSTRACT FROM AUTHOR]

In urban environments, the challenges brought about by multipath propagation are significant for traditional adaptive radar detection problems. To tackle this issue, a generalized multipath signal model combined with environmental geometric prior information (EGPI) is developed. This model can be utilized regardless of whether the multipath signals are fully, partially, or partially-resolvable in the range domain. During the process of designing a detector using the criteria of the one-step generalized likelihood ratio test (GLRT), an attempt to find a closed-form expression for the maximum likelihood estimator of the unknown parameters was unsuccessful. However, by utilizing an adaptive configuration for the initial value, a cyclic optimization algorithm was able to obtain the estimated value within just a few iterations, demonstrating a rapid convergence rate. Furthermore, a detector based on the two-step GLRT criteria is proposed. Compared to traditional detectors, the newly developed detectors make more efficient use of the energy in multipath signals, resulting in improved detection performance. It has been demonstrated that when the whitened angles between subspaces are set to two specific values, the GLRT detectors exhibit the Constant False Alarm Probability (CFAR) property. Simulation results show that the false alarm probability(P f a) at these two specific angles provides upper and lower bounds of P f a. • Deal with the partially resolvable multipath signals in range domain. • The superior detection performance than conventional detector. • The false alarm rate of the detector is only related to principle angles of signal subspaces. [ABSTRACT FROM AUTHOR]

Dual-function radar and communications (DFRC) systems based on multiple-input multiple-output (MIMO) arrays have received considerable attention in recent years due to their excellent ability to alleviate spectrum congestion. The MIMO DFRC systems enable high-resolution detection of multiple targets while communicating with multiple users simultaneously. However, MIMO arrays require numerous radio frequency (RF) units and suffer a strong mutual coupling among antennas, resulting in significant system overhead and performance degradation, respectively. In light of this, this paper investigates the joint optimization of transmit precoding and antenna selection for MIMO DFRC systems, aiming to improve the angular ambiguity function with a guaranteed communication quality of service (QoS) using a small number of antennas. To address the resultant non-convex optimization problem, both the indirect and direct precoding methods are proposed. In the former, the waveform covariance and antenna selection vector are first jointly optimized via a promoted sparsity along the covariance diagonal, followed by the precoding matrix indirectly derived from the optimal covariance. In the latter, the precoding matrix is directly optimized via an imposed group sparsity under the communication QoS and power constraints. Simulation results demonstrate that the proposed sparse MIMO DFRC system with fewer active antennas can achieve comparable dual-functional performance to that of the full array system. • The joint design of precoding and antenna selection for MIMO DFRC is proposed. • The indirect precoding method is proposed to address the joint optimization problem. • The direct precoding method is proposed to directly optimize the precoding matrix. • The proposed sparse system achieves comparable performance to that of a full system. [ABSTRACT FROM AUTHOR]

• To the best of our knowledge, this is the first study to consider the azimuth estimation errors of the HRRP and relate them to deep neural network parameter control. • We proposed two new convolutional kernel parameterization methods, VPCONV and extended CVPCONV, which embed SI into deep network by using it to parameterize the convolutional kernel. This enables direct control of the network structure. Simultaneously, variational encoding is introduced in the reparameterization process to generalize the azimuth estimation error using a heteroscedastic Gaussian distribution with varying mean and variance. Notably, VPCONV and CVPCONV exhibit good transferability and can be used as plug-and-play modules. • Based on CVPCONV, we designed a lightweight network CVPCNN, which can make full use of the azimuth of the HRRP, decouple the tight coupling between the azimuth and target HRRPs, and extract discriminative characteristics that are more relevant to the current environment (azimuth). This helps obtain a more robust and accurate recognition performance. In addition, the CVPCNN introduces a sample-dependent kernel attention mechanism based on parallel convolution to achieve efficient inference, even with increased model complexity. • The effectiveness of the proposed model was tested in a target recognition task using a three-class measured aircraft HRRP dataset. The experiments show that the CVPCNN model, with just three convolutional layers, demonstrate optimal performance on this dataset. This represents a 4 % improvement over the standard CNN model under identical experimental conditions and model structure. Significantly, the CVPCNN maintain good recognition performance under non-ideal conditions, such as small sample and incomplete HRRP attitudes. Radar target recognition tasks often incorporate valuable side information (SI) that aids in the recognition task. The effectiveness of SI has been fully verified in traditional statistical modeling approaches. Its full potential remains untapped in deep learning-based high-resolution range profile (HRRP) target recognition tasks. Taking into account the azimuth sensitivity of HRRP, azimuth is chosen as the SI of the proposed model. To integrate SI into deep neural networks, we propose two novel parameterized convolution methods, namely variational parameterized convolution (VPCONV) and the extended conditionally VPCONV (CVPCONV). Specifically, VPCONV makes the convolutional kernel weights as the function of the auxiliary azimuth, which gives the network the capability of adaptively adjusting to changes in azimuth. Acknowledging the challenge of estimating HRRP azimuth accurately in practical scenarios, VPCONV employs a heteroscedastic Gaussian distribution, featuring varying mean and variance, to generalize the estimation error of SI through variational encoding. Moreover, CVPCONV incorporates the properties of the HRRP samples themselves by kernel attention module. Finally, we present a lightweight SI-based network. The experimental results based on the measured HRRPs validate the effectiveness of proposed method across various extended recognition tasks, underscoring the potential of fusing SI via parameterized convolution in advancing target recognition systems. [ABSTRACT FROM AUTHOR]

The existing radar-communication coexistence (RCC) waveform design based on chirp signal causes the increase of the complexity in the communication receiver, and multi-dimensional reuse also introduces more interference, leading to the deterioration of dual function performance. In this paper, a frequency division RCC system with on-off keying (OOK) based on frequency modulated continuous wave is designed for near sensors wake-up or low rate broadcast. The RCC signal has the fixed whole band and the variable sub-bands for radar signal and communication signal. We propose a two-stage envelope detection (TSED) scheme by extracting data from the frequency difference component of the two signals at the receiver without generating local radio frequency signal or using a mixer. By flexibly regulating the frequency band of two signals, the anti-interference ability of TSED can be further improved. In radar system, we perform small frequency hopping (FH) on radar signal to realize the full use of the whole band, and propose a scheme of abnormal peak detection and elimination (APDE) to eliminate the chirp interference. The simulation results show that the signal-to-interference ratio of TSED scheme for the RCC waveform can be respectively enhanced by 6 dB and 2 dB if compared with that of conventional envelope detection and coherent demodulation with a bit error rate of 10−5. Besides, The APDE scheme based on small FH reduces the range sidelobe by 20 dB and completely eliminates the added in-band interference. [ABSTRACT FROM AUTHOR]

In this paper, we propose a joint transmit and receive antenna selection scheme for frequency diverse array (FDA) radar that aims at finding an optimal selection of employed FDA antennas, formed by minimizing the Cramér–Rao lower bound (CRLB) of the target localization problem given the available a priori knowledge of potential target locations. The resulting problem is a non-convex Boolean problem in which the objective function is a quadratic objective function with respect to the optimization variables. To allow for an efficient solution, an iterative relaxed algorithm is proposed, substantially simplifying the resulting minimization. Numerical simulations are provided to demonstrate the effectiveness of the proposed approach. • The joint transmit and receive antenna selection scheme for frequency diverse array (FDA) radar is investigated. • The antenna placement is formed by minimizing the Cramér–Rao lower bound (CRLB) of the target localization problem. • An iterative relaxed algorithm is proposed for the resulting non-convex Boolean problem. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of artificial intelligence and robotics, the pursuit of accurate positioning in mobile robots has intensified. This research addresses the limitations of single-sensor SLAM (Simultaneous Localization and Mapping) techniques in complex settings by harnessing the collective strengths of LiDAR (Light Detection And Ranging), Camera, IMU (Inertial Measurement Unit), and GNSS (Global Navigation Satellite System) sensors. The proposed multi-sensor tightly-coupled SLAM framework is an integration of point-line feature-based laser–visual–inertial odometry, visual–laser fusion loop closure detection, and factor graph-based back-end optimization. Within the visual–inertial subsystem, an advanced LSD (Line Segment Detector) feature extraction strategy is introduced, incorporating point-line fusion to enhance visual line features. Additionally, the laser point cloud is projected onto the camera coordinate system, establishing depth associations with visual attributes. Strengthening the robustness of the visual–inertial subsystem in low-texture environments, camera poses undergo optimization through a sliding-window bundle adjustment method. In the laser-inertial subsystem, IMU preintegration mitigates laser point cloud distortion. Extracting edge and plane features, coupled with frame-to-local-map matching, enhances matching efficiency while streamlining computational intricacies. This amalgamation forms the basis of the laser–visual–inertial odometry fusion system. To overcome the limitations of standalone visual and laser-based loop closure detection, a dual-loop closure method utilizing visual–laser fusion is proposed. Leveraging the DBoW2 bag-of-words model, complemented by temporal–spatial consistency checks, enhances detection efficiency and accuracy. The integration of GNSS factors imparts global constraints for expansive outdoor scenarios. Employing factor graph-based back-end optimization, the refinement of laser–visual–inertial odometry factors, visual–inertial odometry factors, IMU preintegration factors, loop closure factors, and GNSS factors culminates in precise global pose estimation and high-fidelity point cloud maps. Through rigorous evaluation of the M2DGR dataset and a mobile robot platform, the proposed methodology emerges as an exemplar of performance, showcasing superiority over the state-of-the-art LIO-SAM technique. Achieving a reduction of 2.86 m and 3.23 m in the root mean square error of absolute pose estimation across divergent environments, this approach exhibits remarkable efficacy in outdoor scenarios, thereby elevating the precision and resilience of SLAM algorithms for mobile robots. • Introduces a tightly coupled multi-sensor SLAM framework that harnesses the synergies of LiDAR, Camera, IMU, and GNSS. • Laser–visual–inertial odometry fusion system optimizes pose estimation, overcoming limitations of standalone methods. • Visual–laser fusion and GNSS factors improve loop closure efficiency, contributing to precise global pose estimation. • Refines laser–visual–inertial odometry, visual–inertial odometry, IMU preintegration, loop closure, and GNSS factors. • The proposed methodology excels in outdoor scenarios, elevating precision and resilience of SLAM algorithms for mobile robots. [ABSTRACT FROM AUTHOR]

In contemporary security detection systems, the utilization of millimeter-wave radar has assumed a central role owing to its non-contact and innocuous nature. This study addresses the challenging issues of detecting low-resolution and small targets in active millimeter wave (AMMW) images concealed detection. We identify a prevalent drawback in existing detectors, specifically the adoption of strided convolution or pooling layers, leading to a loss of crucial details that adversely impacts the detection rate. To overcome this limitation, we introduce a novel convolutional structure, termed Wavelet-Conv, which maintains information integrity while segregating features from high and low frequency bands, effectively replacing the unfavorable design. Furthermore, we harness the wavelet transform to enhance channel and spatial attention mechanisms, enabling more effective utilization of frequency band features and ensuring interpretability in the computational process. In this pursuit, we integrate the proposed Wavelet-Conv and Wavelet-Attention modules into the YOLOv8 framework, culminating in a unified model, termed Wavelet-YOLO. Through rigorous experimental validation on two AMMW datasets, our approach exhibits superior performance by significantly enhancing the recall and mean average precision (mAP) of small targets in AMMW images, while maintaining competitive inference speed. Extensive experiments demonstrate the outperformance of our proposed method over existing state-of-the-art approaches. • Enhancing object detection in Active Millimeter Wave Images. • Utilizing DWT for feature dimensionality reduction in convolutional networks. • Leveraging wavelet transform for enhanced spatial and channel attention. • Improving small target detection in blurred images with wavelet-based approaches. [ABSTRACT FROM AUTHOR]

In this work, we propose a time-varying wave-shape extraction algorithm based on a modified version of the adaptive non-harmonic model for non-stationary signals. The model codifies the time-varying wave-shape information in the relative amplitude and phase of the harmonic components of the wave-shape. The algorithm was validated on both real and synthetic signals for the tasks of denoising, decomposition, and adaptive segmentation. For the denoising task, both monocomponent and multicomponent synthetic signals were considered. In both cases, the proposed algorithm can accurately recover the time-varying wave-shape of non-stationary signals, even in the presence of high levels of noise, outperforming existing wave-shape estimation algorithms and denoising methods based on short-time Fourier transform thresholding. The denoising of an electroencephalograph signal was also performed, giving similar results. For decomposition, our proposal was able to recover the composing waveforms more accurately by considering the time variations from the harmonic amplitude functions when compared to existing methods. Finally, the algorithm was used for the adaptive segmentation of synthetic signals and an electrocardiograph of a patient undergoing ventricular fibrillation. • A fully adaptive method for time-varying wave-shape extraction is proposed. • The proposed tool can accurately follow the time-varying pattern of the oscillations. • Results on synthetic signals show high performance in common signal processing tasks. • Experiments on real signals show that our proposal outperforms current techniques. [ABSTRACT FROM AUTHOR]

The target localization performance is not only affected by the measurement noise, but also related with the sensor error such as sensor position error and measurement biases. If not properly estimated and calibrated, sensor error may cause large localization error or ghost tracks. This paper investigates the sensor error calibration problem with imperfect calibrators whose positions are not accurately known when both the sensor position error and measurement biases exist. The performance loss without considering the sensor error is analyzed by evaluating the mean square error (MSE). A closed-form solution based on weighted least squares (WLS) is proposed to estimate the sensor measurement bias and update the sensor position by using the biased range and angle measurement about calibrators. Moreover, the optimal geometry for calibration objects is analyzed and the optimum criterions for updating sensor position are proposed by minimizing the trace of the CRLB. The optimal calibrator-sensor geometries in the presence of measurement bias are generally different from the optimal calibrator-sensor geometries in the absence of measurement bias. Finally the performance of the proposed solution is verified and the optimal calibrator-sensor geometry results for 2 or 3 sensors are solved efficiently by using Nelder–Mead simplex method in simulation. • Formulation of sensor error calibration problem using imperfect calibrators. • Theoretical analysis of the performance loss without considering the sensor errors. • Constructing closed-form solution based on WLS to calibrate sensor errors. • Formulation and analysis of the optimal geometry for calibration objects. • Demonstrating performance improvement of the proposed solution and propositions. [ABSTRACT FROM AUTHOR]