Xiaolong Li

Compared with monostatic radar, bistatic coherent multi-input multi-output (BC-MIMO) radar performs multi-channel coherent accumulation (intra-channel accumulation and inter-channel accumulation), which effectively improves the detection ability of high-speed moving targets. However, due to the high-speed movement of the target, the signal energy in each channel's echo spreads across the range dimension, leading to range walk (RW) and performance degradation of conventional intra-channel accumulation techniques. In addition, the target return's peaks and phases are different between channels, posing challenges to inter-channel accumulation. Furthermore, the existing algorithms ignore the inter-channel accumulation sidelobe (IASL), i.e., the undesired inter-channel response. To deal with the above issues, a computationally efficient multi-channel accumulation method by exploiting the radar and target topologies is proposed, and thereafter an IASL suppression method based on the point spread function (PSF) is presented. Specifically, we first construct echo model, and then the Radon Fourier transform (RFT) is utilized to accumulate intra-channel target energy. On this basis, we use the topology information of the BC-MIMO radar to design the integration strategy, including the integration path and phase compensation filter, to realize the inter-channel accumulation. Furthermore, the IASL response is analyzed theoretically, based on which the PSF is constructed to achieve sidelobe suppression. Finally, the effectiveness of the proposed method is verified by numerical experiments.

Coherent detection typically relies on multi-pulses coherent integration (CI) to achieve the effective target energy focusing, so that it can be highlighted and detected from the background. However, for the low altitude maneuvering target over sea-surface, the range migration (RM), Doppler migration (DM) and severe sea clutter often occur simultaneously in the echo, which seriously affect the coherent detection performance. To solve these problems, this paper proposes a coherent detection framework for low altitude maneuvering target over sea-surface based on hybrid neural network. This framework can be divided into two main steps: CI based on CV-HUNet and clutter suppression based on Dual-ResNet structure. First, the CV-HUNet is applied to extract the target energy trajectory, and the motion parameters of the maneuvering target are estimated by polynomial fitting. Based on the estimation results, the RM and DM can be compensated to obtain the CI results. Second, the CI results are input into the Dual-ResNet structure to classify the target and clutter components and then complete clutter suppression. After the above two procedures, the processing outputs are used for constant false alarm rate detection(CFAR). The publicly measured datasets collected by Naval Aviation University (NAU) of China in 2021 are applied to compare the coherent detection performance of the existed typical methods with the proposed one, which have demonstrated the validation of the given framework.

The high-speed and stealth characteristics of target and the strong spoofing of deception interference are two significant factors that affect the target detection performance of multistatic radar system. In response, this study considers the problem of range deception interference (RDI) recognition and high-speed target integration detection in multistatic radar system. Firstly, the target echo model under RDI environment is established based on the range history model. Then, the intra-channel coherent integration of the echoes after pulse compression is realized by Radon Fourier transform (RFT). Next, the entropy circulation matching (ECM) algorithm is proposed to accomplish the multi-channel coherent integration, while obtaining the estimated location values of the target and RDIs in different channels. Further, the location values are processed by elliptic positioning (EP) to realize the recognition of the target and RDIs. Finally, simulation experiments verify the effectiveness and robustness of the proposed method, and the effect of factors such as multiple targets scenario, jamming-to-signal ratio and pulse number on the performance of the method are covered.

Two primary challenges exist in moving target detection and estimation using long-term integration. First, the target velocity would cause range walk (RW). Second, the time information of target entry or departure radar beam usually unknown. These problems would cause severely impact for target detection and parameters estimation. To address signal integration and detection of moving targets with unknown time information, a coherent integration and estimation method named Normalization Window Radon Fourier Transform (NWRFT) is proposed. The method achieves target focusing and estimation through parameters search, and the optimality is also proved based on maximum likelihood. Besides, considering the computational cost of NWRFT, we propose a sub-optimal method based on Radon Fourier Transform and Normalization Window Fourier Transform (RFT-NWFT), using dimension reduction to maintain an optimal balance between integration efficiency and computational burden. In particular, the velocity estimation and range coarse estimation of target are obtained by Radon Fourier Transform (RFT), then the echo signal is retrieved using the estimated parameters. Subsequently, the extracted signal is processing through Normalization Window Fourier Transform (NWFT) to obtain the time information estimation. Experiments using both simulated and real radar datasets demonstrate the effectiveness of NWRFT and RFT-NWFT.

Coherent multiple-input multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multi-channel and multi-frame echo signal. However, due to the target motion and the geometric configuration differences of each node relative to the target, the multi-channel multi-frame accumulation of target echoes by coherent MIMO radar faces several difficulties, i.e., range migration (RM) and Doppler frequency migration (DFM) issues within each channel, spatial variations of target signal envelope and phase across different channels, time variations of target signal envelope and phase across different frames. To tackle these problems, two contributions are made in this paper. Firstly, by applying the generalized Radon Fourier transform (GRFT) to correct the RM/DFM and obtain the signal integration within each channel, we propose a multi-channel accumulation algorithm to compensate the spatial variations of target signal and realize the target energy accumulation across different channels, which takes full advantage of the GRFT output characteristics and the radar system topological structure. Secondly, a multi-frame accumulation algorithm is developed to compensate the time variations of target signal and achieve the further integration of target energy distributed in different frames. We also provide explicit expressions and analysis for performance metrics, including the output response, SNR improvement performance. The proposed method's effectiveness is confirmed through simulation experiments.