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 adenoise-then-fuseprinciple: a Local Reliability Modulation (LRM) module learns soft, spatial reliability maps to suppress degraded regionsbeforecross-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 (only a 7.3% AP reduction versus drops exceeding 25% for representative alternatives). 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.

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 the field of radar, nonlinearity has always been significant challenge in target tracking algorithms. It is evident in the complexity of the target motion model, observation model, and maneuverability of the target. Traditional model-based algorithms often rely on numerical approximations or simulations to obtain suboptimal solutions, which may lead to conversion errors and increase algorithm complexity. Model-free methods based on deep neural networks have been continuously employed in nonlinear target tracking (NTT) to improve target state estimation performance. This paper introduces two nonlinear trackers based on the Transformer that are used for smoothing, filtering, and predicting target states in the NTT task. First, a classical Transformer-based method is proposed for smoothing and prediction, improving both inference efficiency and accuracy through parallel operation. After that, to handle the recursive operation required for filtering, we introduce a novel recursive Transformer for recursive filtering and predicting of the target state. This significantly reduces computational load compared to the classical Transformer method. Simulation results indicate that the proposed algorithm outperforms traditional and recurrent neural network-based methods.

To deal with the threat of the new generation of electronic warfare, we establish a non-cooperative countermeasure game model to analyze power allocation and interference suppression between multistatic multiple-input multiple-output (MIMO) radars and multiple jammers in this study. First, according to the power allocation strategy, a supermodular power allocation game framework with a fixed weight (FW) vector is constructed. At the same time, a constrained optimization model for maximizing the radar utility function is established. Based on the utility function, the best power allocation strategies for the radars and jammers are obtained. The existence and uniqueness of the Nash equilibrium (NE) of the supermodular game are proved. A supermodular game algorithm with FW is proposed which converges to the NE. In addition, we use adaptive beamforming methods to suppress cross-channel interference that occurs as direct wave interferences between the radars and jammers. A supermodular game algorithm for joint power allocation and beamforming is also proposed. The algorithm can ensure the best power allocation, and also improves the interference suppression ability of the MIMO radar. Finally, the effectiveness and convergence of two algorithms are verified by numerical results.

In this paper, a spatial-temporally varying generalized Pareto intensity distribution (STV-GPID) model is presented to characterize large-scene sea clutter in high-resolution maritime surveillance radars, and a multiscan recursive Bayesian bipercentile (MSRB-BiP) estimation method is proposed to implement the outlier-robust estimation of parameters in the STV-GPID model. Considering that sea clutter characteristics are affected by sea states and the viewing geometry of a radar, the large scene is segmented into clutter map cells based on an empirical backscattering coefficient model of sea surface to predict radar cross-section of sea surface per unit physical area. Sea clutter intensities on each clutter map cell are modelled by a generalized Pareto intensity distribution. In the parameter estimation, the data of previous scans are transformed into the prior information on the parameters to reduce the storage burden of radar systems. The MSRB-BiP estimator updates the parameters of the STV-GPID model recursively by a mixed sample set with the returns of the present scan and simulated data using the prior information. The mixture ratio adjusts the forgetting rate of data to adapt to temporally varying characteristics of sea clutter. At least, it brings three merits: low storage requirement, outlier-robustness, and mitigation of spatial small sample size of a single scan. The convergence and robustness of the estimation method are verified by simulated data. The experimental results on two measured radar datasets verify the effectiveness of the MSRB-BiP estimators and the errors at the steady state are reduced at least 17.7% and 66.7%, respectively.

This letter focuses on the passive localization of a moving source in the presence of random sensor location errors. We propose a modified two-stage closed-form method that takes the sensor location errors into account, utilizing time difference of arrival and frequency difference of arrival measurements. This method utilizes the weighted spherical interpolation approach to obtain an initial estimation in the first stage. The initial estimation is substituted into the original criterion to get a final estimation in the second stage. Theoretical analysis shows the proposed method can reach the Cramer-Rao lower bound under the small Gaussian noise assumption. Numerical simulation results show high noise tolerances of the method.

In this study, a constant false alarm rate (CFAR) decision scheme is devised for frequency diversity multiple‐input–multiple‐output (MIMO) radar. Under the assumption that there exists not only the unstructured disturbance but also the structured interference, a double threshold detector (DT‐MGLRT) based on the modified generalised likelihood ratio test (MGLRT) algorithm is proposed, of which the first stage deals with the unknown parameters and the second stage determines the final decision. It is proved that the proposed detector possesses a CFAR with respect not only to the unknown spectral properties of the unstructured disturbance but also to the structured interference distribution. Finally, some experiment results indicate that the proposed detection algorithm has 2 dB signal‐to‐interference‐plus‐noise power ratio improvement on average in detection performance at a low computation and communication cost over conventional detection algorithms.

Occlusion as a common phenomenon in object surface can seriously affect information collection of light field. To visualize light field data-set, occlusions are usually idealized and neglected for most prior light field rendering (LFR) algorithms. However, the 3D spatial structure of some features may be missing to capture some incorrect samples caused by occlusion discontinuities. To solve this problem, we propose an occlusion probability (OCP) model to improve the capturing information and the rendering quality of views with occlusion for the LFR. In this OCP model, a probability density model is applied to obtain the scores of visibility are modeled as hidden variables. The occlusion probability is calculated by the visibility, position and orientation of camera. We compare different capturing/reconstruction techniques to visualize/manipulate our OCP model.

The conventional methods for target detection and discrimination in high-resolution synthetic aperture radar (SAR) images usually have low accuracy and slow speed, especially for large complex scenes. To overcome these drawbacks, in this paper, we propose a target detection and discrimination method based on visual attention model. In the detection stage, to pop out the targets and suppress the background clutter in the saliency map, we select the task-dependent scales from the Gaussian pyramid of the original SAR image. Moreover, we adopt the clustering algorithm to remerge several isolated focus of attention areas, which are obtained from the saliency map, into a complete target region. The candidate target SAR image chips are extracted with relative high accuracy and low time cost in this stage. Since there may be single target, multiple targets, or partial targets with complex clutter in each SAR image chip, it is hard to acquire accurate target-shaped blob via segmentation. Some classical discrimination features which are extracted based on target segmentation may lose effectiveness. In the discrimination stage of our method, to solve the above problem, based on the saliency and gist (SG) features for optical satellite images, we propose the modified SG (MSG) features for SAR target discrimination. The MSG features are complementary to each other and can provide a more complete description of the extracted SAR image chips without segmentation, which also reduces the computation burden. The experimental results on the synthetic images and miniSAR real SAR image data set demonstrate that the proposed target detection and discrimination method can detect and discriminate the targets from the complex background clutter with high accuracy and fast speed in high-resolution SAR images.

This study proposes a clutter suppression approach and the corresponding ground moving target imaging algorithm for hypersonic vehicle (HSV) borne synthetic aperture radar (SAR) system with multiple‐input–multiple‐output (MIMO) antenna. HSV‐borne radar platforms fly with a high speed, which can lead to severe Doppler ambiguity, and the radar system usually cannot provide enough channel freedom degree for clutter suppression. In this study, an SAR ground moving target indication (GMTI) approach with MIMO antenna is presented for HSV‐borne radar. Compared with the traditional multichannel SAR GMTI methods, the proposed approach can provide more space freedom degree and obtain a wider imaging swath without decreasing pulse repetition frequency. Besides, the improved deramp space‐time adaptive processing method decreases the ambiguity times of the ground clutter and focuses the moving target. The simulation results validate the effectiveness of the proposed method.

The classical target detection methods in synthetic aperture radar (SAR) images are mainly dependent on the intensity differences between the targets and clutter. Although they are effective in the simple scenes with high signal-to-clutter ratio (SCR), they may lose effectiveness in the complex scenes with low SCR. Generally, in high-resolution SAR images, the targets present not only high intensities but also specific size characteristics compared with the clutter. Based on this fact, in this paper, we propose a new target detection method for high-resolution SAR images via Bayesian-morphological saliency, which mainly contains two stages: Bayesian saliency map construction and morphological saliency map construction. The Bayesian saliency map can obtain the complete structures of the bright objects including the targets of interest and some bright clutter, via the superpixel segmentation and Bayesian framework. Furthermore, the morphological saliency map can highlight the targets of interest while suppressing both the natural and man-made clutter via the size prior information of the targets. The experimental results on the miniSAR real data set show that the proposed target detection method is effective.

In the user-congested high-frequency band, skywave over-the-horizon radar (OTHR) is easily interfered by radio frequency interference (RFI). Among the existing methods, transient and nontransient RFIs are suppressed by time-domain methods and beamforming methods, respectively. However, the existing beamforming methods cannot work in transient cases, whereas the time-domain methods are usually time-consuming and suffer performance loss in mass interference cases. Therefore, a unified method for two kinds of RFI cancelation is preferred. In this letter, based on the frequency characteristics of the RFI and the regular processing procedure of the skywave OTHR, an adaptive RFI cancelation scheme is proposed. In this scheme, the RFI is localized in the frequency domain, after which snapshots are chosen to complete the frequency-domain adaptive beamforming. The proposed scheme makes it possible to completely cancel both kinds of RFI in the beamforming stage, which is timesaving and proper for practical application. The performance of the proposed scheme is evaluated by experimental data.

The skywave over-the-horizon radar (OTHR) has attracted considerable attention for its ability of out-of-sight surveillance. However, in practice, its operational performance is severely degraded by transient interferences. Some previous works focus on time-domain methods to achieve transient interference suppression and cannot get rid of the corruption segment blanking and data interpolation. In fact, the data interpolation method is usually time-consuming and may suffer performance loss. To avoid data interpolation, some interpolation-free methods are proposed and show superior performance. However, the significant computational costs impede the application in reality. In this letter, a new method for transient interference suppression is proposed. After interference localization, a signal subspace, representing the clutter and target, is constructed by the interference-free data. Then, transient interferences are excised by solving a bicriterion optimization problem that minimizes the quadratic fitting error and the energy orthogonal to the signal subspace simultaneously. The proposed method can excise transient interferences with clutter and potential target echoes preserved at the same time; thus, data interpolation is no longer needed. The proposed method is evaluated by experimental data collected from a trial skywave OTHR, and the results verify its effectiveness.

Detection of manoeuvring weak targets in radars often encounters circumstance where target movement is modelled by non‐linear dynamic systems and received returns are corrupted by background noise of unknown statistics. It is known that the cost‐reference particle filter (CRPF) is an efficient algorithm for state estimation of non‐linear dynamic systems of unknown statistics. By combining an approximate logarithm likelihood ratio under the piecewise parametric model of signals with the CRPF algorithm, this study proposes a new track‐before‐detect detector, named CRPF‐based detector, for manoeuvring weak target detection from received returns corrupted by background noise of unknown statistics. Experiments using simulated noise and real background noise of over‐the‐horizon radar are made to verify the CRPF‐based detector. The results show that the CRPF‐based detector has comparable performance with the two PF‐based detectors for background noise of known statistics. For background noise of unknown statistics, the CRPF‐based detector attains better detection performance than the two PF‐based detectors where an assumptive probabilistic model is imposed on the background noise.