Multimodal remote sensing combines optical and synthetic aperture radar (SAR) imagery to improve perception, yet real deployments face spatially varying degradations (e.g., clouds, low light, sensor interference) that can corrupt fusion. To make robustness measurable, we introduce a controlled mixed-severity setting in which only the optical stream is synthetically cloud-degraded while SAR remains intact, providing a standardized testbed for evaluating multimodal detection under modality imbalance. We further present CAIR-Net, a reliability-aware information routing network that follows a denoise-then-fuse principle: a Local Reliability Modulation (LRM) module learns soft, spatial reliability maps to suppress degraded regions before cross-modal interaction, and a Global Information Selection Mechanism (GISM) performs confidence-aware expert routing across optical, fused, and SAR experts. On the mixed-severity benchmark, CAIR-Net consistently outperforms strong unimodal and fusion baselines and exhibits a substantially smaller performance drop under severe clouds. These results indicate that explicit reliability modeling and quality-guided routing provide a practical path toward robust multimodal detection when one modality is partially or nearly completely occluded.

The spatial bias of sensor in the process of target tracking and the temporal bias between the time axis of each sensor and the absolute time axis, if not accounted for, can seriously affect the positioning accuracy. Meanwhile, the sensor position reported by Global Positioning System (GPS) is not accurate. In this paper, the problem of angles-only target motion analysis (TMA) by asynchronous sensors is studied in the presence of spatiotemporal bias and sensor position error. A new target tracking method is proposed by taking the target state, spatiotemporal bias and sensor position as the augmented state vector. Using the filter concept and the minimum mean square error (MMSE) criterion for real-time processing, the augmented state vector can be estimated simultaneously. Simulation results show the superiority of the proposed algorithm for target position estimation, and verify the effectiveness of the proposed in achieving the Posterior Cram & eacute;r-Rao lower bound (PCRLB) performance under the distance-dependent measurement noise.

Accurate estimation of the clutter-plus-noise covariance matrix (CNCM) without prior knowledge of the array manifold is a significant problem in airborne radar space-time adaptive processing (STAP) with partly calibrated arrays (PCAs). To estimate the CNCM under limited samples, distributionally robust optimization (DRO) is employed in this article. This approach constructs a Wasserstein uncertainty set centered on the empirical distribution. A robust optimization problem for inverse CNCM estimation is obtained by minimizing the worst-case expected Stein’s loss over this set. We provide theoretical justification for the curse of dimensionality in this approach and propose a localized reduced-dimension DRO algorithm to mitigate its impact. Then, we obtain the closed-form solution for inverse CNCM by imposing the rotation-equivariant property. An improved Grey Wolf optimization (IGWO) is introduced to determine an appropriate size of the uncertainty set. Simulation results indicate that the proposed algorithm outperforms benchmark methods in clutter suppression and low-velocity target detection under limited training samples.

This letter focuses on the problem of maneuvering target tracking under motion model mismatch. We propose the Kullback–Leibler (KL) Minimization Dual-Parameter Strong Tracking Filter (KDP-STF), which explicitly incorporates the multivariate residual distribution structure and achieves parameter adaptation through residual covariance matching. Unlike traditional approaches, the KDP-STF explicitly optimizes the fading factor and the softening factor by minimizing the KL divergence between the estimated and corrected residual distributions via gradient-based algorithm. Theoretical analysis reveals that the conventional strong tracking filter (STF) can be viewed as a second-order Taylor approximation of the proposed KDP STF framework. Numerical simulation results demonstrate the proposed method significantly mitigates estimation bias, reducing root mean square error (RMSE) compared with the conventional STF.

The interrupted sampling repeater jamming (ISRJ), as a typical type of intra-pulse coherent interference, severely degrades radar performance. Most existing countermeasures against ISRJ rely on the accurate estimation of jamming parameters and overlook the mechanism of ISRJ. This paper proposes a unified waveform design method for ISRJ suppression under different types of ISRJ and imprecise interference prior. Firstly, based on the characteristics of ISRJ, a robust intercepted sequence tolerant to estimation errors of jamming parameters is designed. This sequence partitions the designed waveform in the time domain, defining potentially intercepted regions as trick signal and the rest as matching signal. Subsequently, for the ISRJ suppression and false alarm reduction, an optimization criterion is formulated by minimizing the peak level (PL) of the filter output of the trick signal and the peak sidelobe level (PSL) of the filter output of the target echo, where the filter is designed as the matched filter of the matching signal. The constant modulus constraint is imposed on the waveform to meet practical hardware requirements. To solve this complex problem, an efficient iterative method based on gradient is proposed. Numerical simulations validate the excellent performance of the proposed method in pulse compression, interference suppression, and robustness.

The detection of small sea-surface targets remains challenging due to their low signal-to-clutter-plus-noise ratios and low Doppler frequencies. Conventional adaptive detectors fail to accurately model sea clutter, resulting in high FARs and reduced detection probability. Feature-based methods avoid explicit clutter modeling but struggle to extract sufficiently discriminative features and generally require long observation durations. Although deep learning (DL) methods offer a promising alternative, most existing DL detectors rely on single-range-cell feature extraction, limiting their ability to distinguish weak targets from complex clutter backgrounds. In practice, sea clutter across adjacent range cells is typically independently and identically distributed. Consequently, the autocorrelation features of a target-containing cell under test (CUT) will exhibit distinct discrepancies compared with those of reference cells. Accordingly, we propose a detection method that leverages both the temporal correlation of the echo signal within each range cell and the similarity of their autocorrelation information between the CUT and reference cells. While standard Transformers are adept at sequence processing, their inherent architecture cannot explicitly model such intracell autocorrelation. To realize the proposed mechanism, we introduce a neural network framework that employs a multidimensional attention-autocorrelation module for explicit feature extraction. Subsequently, a standard Transformer is cascaded to evaluate the spatial similarity of these robust features across range cells. Experimental results demonstrate that the proposed method significantly enhances detection performance, reduces reliance on long observation durations, and exhibits superior generalization capability.

This article considers the problem of target detection in colocated multiple-input multiple-output radar under the Gaussian disturbance with unknown covariance when the sample number of transmitted waveform is small. We model the disturbance as an autoregressive (AR) process with unknown parameters. Three parametric adaptive detectors are designed according to the generalized likelihood ratio test (GLRT), Rao test, and Wald test. Notably, the training data are not required in the proposed detectors. We point out that the detection problem to be solved can be equivalent to a hypothesis test within the context of subspace interference. From this perspective, these detectors exhibit different echo projections to achieve the accumulation of useful signals. Moreover, the asymptotic detection performance is analyzed when the AR process matches or mismatches with the actual disturbance. Numerical simulations show that the proposed detectors outperform their competitors. The proposed Wald detector has stronger robustness against the mismatched signals, and the proposed Rao detector is more sensitive to the mismatch in the steering vector. The proposed GLRT detector exhibits better false alarm control ability.

This article considers the application scenarios of dual-function radar communication (DFRC) systems with multiple communication users and multiple detection targets. Generally, communication requires the transmission of data to users, whereas the task of radar is to get the target parameters by analyzing echoes. Therefore, we jointly optimize the radar-receiving filter and dual-function waveform in the integrated sensing and communication system. Unlike existing research, which introduced a tradeoff parameter to integrate radar and communication functions into one objective function, we minimize communication multiuser interference energy under radar signal-to-interference-plus-noise ratio constraints. In addition, a low peak-to-average-power ratio constraint is imposed on the waveform to avoid nonlinear distortion after amplification. According to the alternating direction method of multipliers framework, a cyclic algorithm is presented to seek the solution of the multiconstraint nonconvex optimization problem. In addition, we discuss the proposed method from computational load and convergence. The numerical results demonstrate that the designed filter and waveform enable the DFRC system to achieve satisfactory communication and detection capabilities.

This paper considers the application scenarios of dual-function radar communication (DFRC) systems with multiple communication users and multiple detection targets. Generally, communication requires the transmission of data to users, whereas the task of radar is to get the target parameters by analyzing echoes. Therefore, we jointly optimize the radar-receiving filter and dualfunction waveform in the integrated sensing and communication system. Unlike existing research, which introduced a trade-off parameter to integrate radar and communication functions into one objective function, we minimize communication multiuser interference (MUI) energy under radar signal-to-interference-plusnoise ratio (SINR) constraints. Additionally, a low peak-to-averagepower ratio (PAPR) constraint is imposed on the waveform to avoid nonlinear distortion after amplification. According to the alternating direction method of multipliers (ADMM) framework, a cyclic algorithm is presented to seek the solution of the multiconstraint nonconvex optimization problem. In addition, we discuss the proposed method from computational load and convergence. The numerical results demonstrate that the designed filter and waveform enable the DFRC system to achieve satisfactory communication and detection capabilities

The sensor position uncertainties and synchronization offsets can cause substantial performance degradation if the sensors are not properly calibrated. This paper investigates the localization of a constant velocity moving target and the self-calibration of sensors using a sequence of range and azimuth measurements observed at successive instants. A theoretical study by the Cramer-Rao Lower Bound (CRLB) reveals that the sensor positions can only be self-calibrated when there are at least two sensors and synchronization offsets can be handled by joint estimation. A low complexity sequential closed-form solution is proposed to estimate the target position and velocity first, and the coordinates of each sensor and synchronization offset afterward. While less intuitive, the analysis shows that the closed-form solutions for both the target and sensor parameters can reach the CRLB accuracy under small Gaussian noise. We also develop a semidefinite programming (SDP) solution by semidefinite relaxation (SDR) for joint localization and calibration from the Maximum Likelihood formulation, which exhibits higher noise tolerance than the closed-form solution. Simulations validate the analysis and the performance of the proposed methods.

The presence of mainlobe jamming (MLJ) poses a significant threat to the radar system performance, particularly when the MLJ is coherent with the target echo. Conventional blind source separation (BSS) algorithms demonstrate degraded performance in such coherent MLJ scenarios. To address this challenge, this paper proposes a novel method based on source separation and identification to suppress the coherent MLJ. The method innovatively incorporates prior information about array structure to realize source separation through sparse recovery. Furthermore, it enables rapid and accurate target identification by jointly extracting time and correlation domain features of pulse-compressed signals. The method offers several notable advantages that are not possessed by BSS, including immunity to the coherence between jamming and target echo, and the ability to provide angle and amplitude information about the target while suppressing the MLJ. Numerical simulations demonstrate the superior performance of the proposed method in mitigating the MLJ. In conclusion, this work provides a novel perspective for mainlobe jamming suppression.

The range-spread target detection problem typically faces uncertainty in the number and locations of target scattering centers (TSCs), which severely limits the performance of traditional algorithms. Conventional methods estimate the TSCs based on the energy information of echo signals and heavily rely on certain assumptions and specific prior knowledge. The performance of these algorithms may degrade significantly in the presence of assumption mismatches or a lack of required prior information. In practical applications, echo signals from one target shall exhibit some aspects of correlation, while clutter echo signals from different range cells are independent. This distinct characteristic between target and clutter may offer extra information in range-spread target detection. Therefore, a neural network based on the intrinsic features of echo signals is proposed for TSCs estimation by outputting the probability of TSC presence in each range cell. In addition, conventional methods directly sum the test statistics of estimated TSC-present range cells to form the final test statistic. This approach may lead to performance degradation, as weak target signals diminish the contribution of strong target signals to the integrated test statistic. To address this limitation, we propose to assign weights to the test statistics of each range cell based on the predicted probability. This approach enables more effective integration of the TSCs energy of all range cells under test. Simulation experiments demonstrate that the proposed algorithm achieves substantial performance improvements over the traditional methods.

This paper investigates the joint localization of a target and transmitter in 3-D space using passive sensors when the transmitter position is unattainable. We propose a novel twostep joint localization algorithm based on the differential time delay (DTD) between the line-of-sight signal from the transmitter and the target-scattered signal, as well as their angles of arrival (AOA). The algorithm provides closed-form solutions, only requiring two passive sensors at the minimum and without the need for raw signal passing between different sensors. Simulation results demonstrate that the proposed solutions can achieve the Cramer-Rao Lower Bound under the Gaussian noise model ´ over the small noise region. Moreover, the proposed algorithm provides superior target localization performance compared to the existing algorithm

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.

Radar jamming recognition provides prior information for anti-jamming measures, thus is a crucial part in modern battlefield. Feature extraction and SVM based algorithm is a mainstream method in practical applications. However, its robustness cannot be guaranteed when jamming parameters and external environments change. To address this issue, many methods based on deep neural networks have been studied. Nonetheless, deep neural networks often require a large number of samples, which is difficult to obtain in practice. Additionally, it is often impossible to obtain all data at once in practice, necessitating that the network has the ability to update in real-time without forgetting previously learned knowledge. This paper proposes an updatable neural network based on ReduNet. Simulation experiments demonstrate that this network achieves high accuracy with small samples. Furthermore, two update algorithms for this network is proposed, and experiment results show that these methods possess the ability of continual learning.

This paper investigates the target localization using bistatic range measurements with multiple transmitters and one passive receiver. We introduce an angular constraint to incorporate the valuable information from the main-lobe beamwidth of the receiving antenna into the passive multistatic localization problem. Then, a specific Semidefinite Relaxation (SDR) based algorithm is proposed to tackle this quadratically constrained quadratic programming (QCQP) problem. Simulation results illustrate that the proposed algorithm effectively leverages the prior main-lobe beamwidth information, resulting in lower estimation errors compared to alternative methods.

The increased electronic equipment makes the spectrum occupied by wireless applications more crowded, seriously weakening the detection performance of the radar. This article addresses the problem of transmit beampattern synthesis for wideband multiple-input multiple-out (MIMO) radar facing the same frequency electromagnetic conflict. According to the prior information about the environment, the spectral constraints are forced on the radar waveform to flexibly control the energy in the specified interference frequency band and direction. Meanwhile, low peak-to-average power ratio (PAR) constraints are added to the radar waveform to achieve high transmission efficiency. To solve this resultant nonconvex multiconstraint beampattern matching design problem, this article proposes an iterative algorithm that simplifies the problem by introducing double auxiliary variables (DAV) and then solves it based on the alternating direction method of multipliers (ADMM) framework. These auxiliary variables allow us to directly control the radar radiation energy at each discrete space-frequency point. In particular, the proposed DAV-ADMM algorithm can guarantee convergence with bounded penalty parameters. Finally, the effectiveness of the algorithm is verified by numerical simulations in various challenging scenarios.

In this article, we investigate the problem of point-like weak moving target detection in the distributed multiple-input multiple-output (MIMO) radar. Due to limitations in communication bandwidth, data from certain spatially diversity channels (SDCs) necessitate low-bit quantization before transmission to the fusion center, while high-precision echo data from other channels can be utilized directly. The transmission channels are modeled as binary symmetric channels. Given the challenge in obtaining the maximum likelihood estimate of the target complex reflection coefficient, we initially develop a fusion detector applicable to the hybrid data using the Rao criterion, where its asymptotic distribution is also analyzed. Simultaneously, optimizing quantization thresholds for the low-bit quantized SDC using Fisher information as the objective function is conducted to maximize the detection probability. As a crucial aspect of the moving target detection, we examine the problem of target velocity estimate, presenting a maximum likelihood estimator for target velocity and deriving a closed-form Cram & eacute;r-Rao lower bound. Finally, two metrics are designed to characterize the impact of various factors on the performance of the distributed MIMO radar, named as normalized detection probability gain and normalized velocity estimation precision gain, respectively. Simulation results indicate that under high-quality transmission channels, 2-bit quantization yields performance close to optimal. In scenarios of poor channel quality, high-precision echo data help to alleviate the adverse effects of low-bit data on signal fusion when channel distortion occurs.

This paper addresses the localization of a target in 3D scenarios using bistatic range measurements with a single passive receiver and multiple transmitters. The main-lobe beamwidth of the receiving antenna contains valuable a-priori information about the target position but is commonly overlooked. This study introduces an angular constraint to incorporate the beamwidth information into the elliptic localization, resulting in a Quadratically Constrained Quadratic Programming (QCQP) problem. A novel Two-Stage Main-lobe beamwidth Constrained Estimation (TSMCE) algorithm is proposed to solve this challenging localization problem. Theoretical analysis demonstrates that the algorithm can converge to the candidate solutions that satisfy the Karush–Kuhn–Tucker (KKT) optimality conditions. The corresponding covariance can achieve the Cramér-Rao Lower Bound (CRLB) under a small noise assumption. Numerical simulations confirm that the proposed algorithm achieves superior localization accuracy compared to other methods, particularly in the low Signal to Noise Ratio (SNR) regime. Additionally, the algorithm demonstrates fast convergence and robustness to variations in localization geometry.

This paper focuses on the distributed state estimation (DSE) problem in sensor networks through the application of a consensus-based methodology. In practical scenarios, the estimation errors within the sensor network exhibit inherent correlation, primarily arising from the consideration of same prior estimate and process noise in a distributed estimation system. However, prevailing distributed approaches, particularly those embedded within consensus-based DSE frameworks, simply assume independence among estimation errors or utilize a suboptimal strategy that neglect cross-covariance matrix, which may lead to a degraded estimation results. To address this limitation, we propose the fusion algorithm Weighted Average Consensus considering Correlated Estimates Errors, abbreviated as WACCEE. The algorithm improves DSE performance by integrating the correlation among local estimation errors as indicated by the cross-covariance matrix into the fusion process. Furthermore, we prove that the proposed WACCEE fusion algorithm exhibits consensus. Additionally, the stability of the WACCEE filters is guaranteed through an analysis of mean-square exponential boundedness of the estimation error using stochastic stability theory. Finally, the effectiveness of the proposed algorithm substantiated by its application to a target tracking case study and a connected undirected sensor network.