In this paper, a vision-assisted multipath recognition and suppression method is proposed for the problem of millimeter wave (mmWave) radar producing false targets under the influence of multipath interference. First, object detection is performed on the image and rectangular clustering is performed on the mmWave radar point cloud to complete the data preprocessing. Subsequently, nearest-neighbor frame matching and direct linear transform (DLT) algorithms are used to achieve spatio-temporal calibration of the two sensors. An axial adaptive cost-normalized matching algorithm is then proposed to associate targets from the two sensors, thereby establishing target association pairs. Finally, multipath ghosts in mmWave radar are recognized and suppressed based on the target association results. Experimental results show that the proposed method efficiently recognizes and suppresses multipath ghosts in traffic scenarios.

Non-line-of-sight (NLOS) detection is crucial in applications such as autonomous driving and surveillance. This paper proposes a bistatic multiple-input multiple-output (MIMO) radar-based joint estimation algorithm to localize diffraction corners and estimate NLOS targets. By leveraging the direction of departure (DoD) and direction of arrival (DoA) of diffraction signal, the algorithm first estimates corner position. Furthermore, target motion state is estimated based on the estimated corner and Doppler information. Electromagnetic simulations confirm the accuracy and robustness of the proposed method under various noise conditions.

Compared with the monostatic radar which only performs signal fusion in the multi-pulse dimension, the bistatic multiple-input multiple-output (MIMO) radar can also fuse multi-channel signals to improve the detection ability of high-speed moving targets. However, how to tackle the range migration (RM) caused by high-speed motion and compensate the signal difference between channels are the key problems in bistatic MIMO radar. To solve these problems, this paper proposes a computationally efficient multi-channel multi-pulse fusion algorithm. Firstly, we establish the echo model of bistatic MIMO radar with high-speed moving targets and utilize the Radon Fourier transform (RFT) algorithm to overcome the RM and complete the multi-pulse fusion. Then, the output characteristics of RFT, including peak and phase differences, are analyzed in detail. On this basis, a multi-channel coherent fusion method based on geometric information is proposed, which can eliminate the phase differences among channels and complete coherent fusion. Finally, numerical experiments show that the proposed algorithm can effectively detect the target under a low signal-to-noise ratio (SNR). Compared with the existing methods, the proposed algorithm can achieve a balance between computational complexity and detection performance.

The traditional two-step localization method for transmitter requires to estimate the signal parameters such as angle of arrival (AOA) and time of arrival (TOA), wich confronts the problem of localization error cumulation. While the direct position determination (DPD) can effectively reduce the estimation error and achieve superior localization performance than the two-step localization, which is widely applied in passive radar. Unfortunately, the uncertainty of the transmission signal parameters will causes the localization performance degradation for DPD method in passive localization. To address these issues, this paper considers the LFM signal transmitter localization with passive radar. Firstly, the signal within each receiving channel is accumulated by fractional Fourier transform (FrFT). Then the signal envelope alignment of each channel is performed in the FrFT domain by using the characteristics of FrFT, so as to realize the multi-channel signal accumulation. Finally, the transmitter is accurately localized by the two-dimensional position search. Simulation results show that the proposed method outperforms the two-step method and DPD method in low SNR.

When detecting high-speed targets, the strong spoofing of range deception interference (RDI) can lead to failure of true target detection. Fortunately, the fusion processing provides an effective solution to this problem. In this paper, the target echo model under RDI conditions is established based on the range history model for multistatic radar system. On this basis, we propose an effective RDI recognition and target detection method based on coherent fusion processing. Specifically, the method firstly realizes the coherent fusion of single-channel echo by Radon Fourier transform (RFT). Then, topology-based entropy circulation matching (TECM) is used to accomplish the acquisition of the matching positions about target and RDI in different channels. Finally, the matching position is processed by elliptic positioning (EP) to realize the recognition of RDI and target. Simulation experiments verify the effectiveness of the method.

Ensuring the safety and security of our oceans demands a comprehensive Maritime Situational Awareness (MSA) strategy. However, this task has several challenges, including using multi-modal data, data sharing among agencies, privacy concerns, and bandwidth limitations. To address these challenges, this research introduced FedRS-Net. FedRS-Net is a federated deep learning framework that trains multi-modal remote sensing data without exposing client data. The system employs a communication-efficient federated averaging algorithm and a novel convolutional neural network architecture called Redesigned Skip Connection. It integrates synthetic aperture radar (SAR) and optical satellite imageries to achieve remarkable results. Extensive experiments were conducted on the maritime vessel datasets, resulting in a testing accuracy of $99.8 \%$. Further, applying secure aggregation and momentum-based gradient compression reduced communication costs by $\mathbf{7 \%}$. FedRS-Net overcomes privacy concerns and facilitates agency collaboration by enabling collective maritime monitoring through decentralized data. This research provides a robust federated learning solution tailored for multi-modal remote sensing analytics applications.

This article addresses the multi-channel integration detection issue of high-speed maneuvering weak targets in airborne coherent multi-input multi-output (MIMO) radar. Coherent MIMO radar can significantly improve the detection performance through joint intra-channel and multi-channel fusion processing. Nevertheless, the range migration (RM) and Doppler frequency migration (DFM) are resulted from highspeed motion, and the envelope and phase differences among multi-channels are challenging. To address these limitations, we propose a multi-channel integration approach in generalized Radon-Fourier transform (GRFT) domain. First, the system and signal models are established. GRFT is utilized to accumulate intra-channel energy. Then, we construct a set of coupled equations, and estimate the target’s position, speed and acceleration with the Newton-Raphson algorithm and solving linear equations. Based on the estimated outputs, the envelope alignment and phase compensation functions are constructed to eliminate the differences across channels. After that, the multi-channel fusion is realized in GRFT domain. The superiority of the proposed approach is shown via simulations.

This paper solves the through-wall imaging (TWI) problem with a narrow-band system, and proposes an adaptive TWI method based on data fusion of multiple scan paths. First, we use a Wentzel-Kramers-Brillouin-based (WKB-based) approximation to model the interaction of the transmitted wave with the unknown area. Then we use Radon inverse transform to reconstruct the image from the received signal strength data of different paths. Furthermore, we evaluate the impact of scan paths on imaging. Finally, finite-difference time-domain (FDTD) simulation results demonstrate the validity of proposed method.

This paper deals with the tracking problems for multiple human targets hidden behind the wall using through-wall imaging radar (TWIR). We propose a robust tracking algorithm in image domain, combining mean-shift algorithm with Kalman filter. Comparing with the traditional mean-shift algorithm, the proposed algorithm has a greater performance in multiple human targets tracking, especially considering the case of the temporary loss of target. Real data validates the robustness of the proposed algorithm.

This paper considers the location problem for Moving targets behind a corner. Exploiting multi-path and the algorithm based on phase comparison among the multiple channels can obtain the position of the target behind a corner. To localize the moving target, a scanning radar system with multiple channels is suggested. The false target range can be achieved by the fast Fourier transform(FFT) technique. In addition, the false target azimuth is derived via exploiting the phase differences between the return signals among the multiple channels. Due to false targets and real targets are geometric symmetry, true targets can be localized by the radar system. Finally the experiment results validate this method, and demonstrate the effectiveness.

In this paper, under the situation of multiple interference regions, an optimal antenna placement problem for a distributed Multi-Input Multi-Output (MIMO) radar is studied. Considering multiple interference regions, we solve the antenna placement problem by utilizing antenna placement method based on Multi-Objective Particle Swarm Optimization (MOPSO). However, it is not clear when to stop the iteration for which no knowledge about the optimum result is available. Hence, computational resource may be wasted over iterations. Nevertheless, time and computational resource is limited in real application. Therefore, to obtain the optimal placement result with limited time and computational resource, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the optimization process efficiently when the optimal antenna placement algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.

This paper considers the location and tracking problem for the indoor and outdoor targets with the single input multiple output (SIMO) radar. An effective algorithm based on phase comparison is presented to derive the target azimuth by exploiting the phase differences between the return signals among the multiple channels. In addition, the target range is derived via employing the fast Fourier transform (FFT) technique. Combined with the azimuth achieved, this method can be applied to accurately locate and track the moving targets whatever indoors or outdoors. Finally, the experiment results validate this method, and demonstrate the effectiveness.