This article considers the signal integration detection and estimation issue for high-speed targets with range ambiguity (RA) using a multiple pulse repetition frequencies (multi-PRFs) radar system, where range walk (RW) occurs during the integration period. A coherent integration detection and estimation method, encompassing both intrasegment integration and intersegment integration, is proposed. First, we divide the received signal of multi-PRF radar into multiple segments according to different PRFs (i.e., a segment corresponds to a certain PRF). Second, the modulo Radon Fourier transform (MRFT) is proposed to achieve signal integration within each segment in the range-velocity domain. Third, we analyze the MRFT's peak location and phase difference within different segments. After that, we design an intersegment integration method to further obtain the signal accumulation among different segments, via joint searching of range, ambiguity number, and velocity. Thereafter, target detection and estimation could be obtained. Finally, we demonstrate the superiority of the proposed method through numerical simulation, where comparisons with some existing typical methods are also given.

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 multipulses 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 article 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 complex-valued half U-structure network (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. The publicly measured datasets collected by Naval Aviation University 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.

This paper focuses on the problem of long-time coherent integration (LTCI) for radar detection of maneuvering targets with multiple motion stages, involving the issues of range migration (RM), Doppler frequency migration (DFM), and motion stage uncertainty (i.e., targets follow multiple motion stages during integration). To address these challenges, a segmented model-data co-driven method is proposed in this paper. First, the range-compressed signal is divided into segments. Subsequently, each segment undergoes data-driven and model-driven blocks sequentially. For the data-driven block, a ResU-Net is used to correct RM by inferring target trajectories from range-compressed signals. Once trained, this block can process range-compressed signals of different sizes without retraining, and focusing on motion within short segments reduces computational and data resource demands. For the model-driven block, dechirp-based processing is employed to eliminate the DFM. This block first estimates motion parameters and marks segments containing stage-changing points (i.e., when the target's motion stage changes). After all segments have been processed, these points are then accurately estimated. Finally, the estimated motion parameters and stage-changing points from all segments are integrated as the final output of the method. Experiments demonstrate that the proposed method significantly reduces computational cost at the expense of detection performance compared with the state-of-the-art model-driven method, thus offering a clear performance–complexity trade-off.

Localization and tracking of weak targets remain challenging problems in radar signal processing. Coherent multiple-input multiple-output (MIMO) radar can simultaneously acquire multichannel spatial echoes and multi frame temporal echoes, enabling spatio-temporal joint processing to enhance the target signal-to-noise ratio (SNR) and improve localization and tracking performance. However, practical performance is limited by inter-channel and inter-frame inconsistencies in echo peak envelopes and phases caused by radar geometry and target motion. To address these issues, this paper proposes a spatio-temporal fusion–based method for weak target localization and tracking in coherent MIMO radar. First, the Generalized Radon–Fourier Transform (GRFT) is applied to each channel to coherently accumulate multi-pulse energy. Then, a spatio temporal joint accumulation algorithm is developed by exploiting the characteristic variations of GRFT peaks across channels, enabling coherent integration of multi frame and multichannel echoes and significantly enhancing the target SNR. On this basis, a localization and tracking algorithm based on spatio-temporal trajectory backtracking is proposed. Threshold detection on the joint accumulation plane, together with a state transition matrix, is used to reconstruct the target's spatio-temporal accumulation trajectory. The extracted GRFT peak characteristics are then coupled with the target motion parameters to form estimation equations, whose solutions yield accurate multi frame motion parameter estimates. Simulation and real-data experimental results demonstrate that the proposed method achieves superior fusion performance and significantly improves localization and tracking accuracy compared with existing approaches.

This paper addresses the challenging problem of radar detection and parameter estimation for high-speed targets with unknown time of entry and departure. A computationally efficient joint processing framework combining Radon-Fourier transform (RFT) and normalized windowed Fourier transform (NWFT) is proposed. The presented method operates in two stages. First, the RFT is applied to the pulse-compressed signal to correct range walk (RW) and achieve coherent integration, yielding an estimate of the target's radial velocity and a coarse range estimation. Subsequently, based on the coarse estimates, the pulsed compressed signal is extracted. A two-dimensional search in the time domain is then performed using the NWFT to accurately estimate the target's entry and departure times. These time estimates are finally used to correct and obtain the precise range parameter. Simulation results demonstrate the effectiveness of proposed method.

Low probability of intercept (LPI) radar signals pose significant challenges for electronic support (ES) in Internet of military Things (IoMT) due to low signal-to-noise ratios (SNRs) and complex modulations. Traditional noise reduction and recognition techniques struggle to maintain performance in low-SNR environments. Recent advances in deep neural networks (DNNs) have enabled progress in both signal enhancement and recognition tasks, but most existing methods remain dependent on time-frequency transform (TFT), increasing computational cost and relying on prior expert knowledge. Besides, these methods rarely provide comprehensive evaluation across multiple metrics. To address these issues, this paper proposes LPI-ETDE-STAR, an end-to-end framework for time-domain LPI signal enhancement (LSE) and modulation recognition (LMR). The framework employs an end-to-end time-domain enhancement network (ETDE-Net) to enhance signals, which integrates convolution for local feature extraction and a structured state space (S4) module for capturing long-range dependencies (LRDs). Then, the STAR-MRNet utilizes STAR operations to model high-order and high-dimensional feature interactions for accurate modulation recognition. Comprehensive experiments show that ETDE-Net surpasses both traditional filtering and DNN-based methods for the LSE. Furthermore, STAR-MRNet achieves superior LMR performance based on the outputs of ETDE-Net, outperforming existing DNN approaches under low-SNR conditions.

The early-warning capability of airborne radar holds significant importance for radar detection systems. Space-time adaptive processing (STAP), by leveraging the spatiotemporal characteristics of clutter to construct adaptive filters, suppresses long-range clutter and enables target detection, thereby gaining extensive application in the field of airborne radar. How ever, in scenarios involving high-speed moving target, the high speed motion of the target gives rise to the range migration (RM) issue, which causes a degradation in the performance of STAP, preventing the radar from achieving timely early warning. To suppress clutter and address the RM issue of high-speed moving target, this paper proposes a combination method of STAP and RM correction. The method first converts the target's RM into phase variations via range-dimensional discrete Fourier transform, then, the phase variations are incorporated into the weight vector design of STAP, and the combined processing of clutter suppression and RM correction is realized through the improved STAP filter. Simulation experiments verify the effectiveness and feasibility of the proposed method, with particularly superior performance exhibited in multi-target scenarios.

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. First, 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. Next, the entropy circulation matching algorithm is proposed to accomplish the multichannel 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 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.

Low probability of intercept (LPI) signal detection is essential to cognitive electronic warfare (CEW) systems, but it is challenged by the coexistence of impulsive noise and the low signal-to-noise ratios (SNRs) stemming from LPI signals’ high time-bandwidth product and low peak power, thereby degrading the performance of traditional detection. Although recent deep neural network (DNN)-based methods show improvements, they lack principled false alarm control under Neyman-Pearson criterion (NPC) and overlook the impulsive noise. Besides, they fail to incorporate characteristics of LPI signals, and provide limited interpretability. To fill these gaps, this paper proposes an NPC-guided DNN detection framework. The key idea is to utilize DNN-based posterior probabilities as test statistic, achieving principled false-alarm control under NPC. The network is trained to extract discriminative LPI signal features, and detection threshold is computed using noise-only data. Building on this framework, we develop an end-to-end LPI signal detection network (ETED-Net), which integrates multi-scale feature extraction with channel attention to capture weak LPI signal features under hybrid AWGN and impulsive noise. Extensive experiments demonstrate that, within proposed framework, ETED-Net achieves superior detection performance and generalization over traditional and DNN-based methods. Furthermore, we demonstrate ETED-Net’s interpretability by tracing decisions to underlying signal structure.

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.

This paper addresses the signal coherent refocusing problem for improving radar moving target detection and imaging performance, which targets with unkown time information (i.e., times of entry/leave radar beam). A coherent refocusing method based on window Radon Fourier Transform (WRFT) is proposed. By searching for the target motion parameters and time parameters, WRFT could extract the target’s signal in the pulse loading window and achieve the target signal refocusing, whereafter the target detection and imaging ability could be improved. Simulation and actual testing have proven the effectiveness of the proposed method.

Two primary challenges exist in moving target detection and estimation using long-term integration. First, the target velocity would cause range walk. 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 suboptimal 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, then the echo signal is retrieved using the estimated parameters. Subsequently, the extracted signal is processing through normalization window Fourier transform to obtain the time information estimation. Experiments using both simulated and real radar datasets demonstrate the effectiveness of NWRFT and RFT-NWFT.

Traditional distributed optical fiber sensors (DOFSs) are limited to measuring a single parameter. Although distributed multiparameter sensing ( DXS) systems, which integrate various sensing mechanisms, have been proposed, they remain largely confined to laboratory settings. In this study, we present a practical application of a novel DXS interrogation technique using hybrid ultra-weak fiber Bragg gratings (UWFBGs) for multidimensional monitoring of gas pipeline leakage. During the measurement cycle, which includes air inflation and leakage events, the wavelength variation of FBG on the main pipeline demonstrates a linear correlation between pressure variation and wavelength shift, with a coefficient of determination exceeding 0.999. The demodulated vibration and wavelength data enable comprehensive analysis for leakage localization and classification.

Accurate parameter estimation of linear frequency modulation (LFM) pulses is important for passive radar detection. However, due to the increasingly complex electromagnetic environment, the pulse density of the received signal from noncooperative transmitters increases, which may cause aliasing effect for multiple LFM pulses in both the time-frequency domain and the fractional Fourier transform (FrFT) domain. In addition, the low probability of intercept technology results in the low signal-to-noise ratio (SNR) of the received signal. In order to achieve effective parameter estimation of aliasing LFM pulses under low-SNR conditions, this article proposes an aliasing removal algorithm based on analytic signal construction (ASC) and blind source separation (BSS). Specifically, the analytical expressions of the aliasing LFM pulses at different FrFT transformation angles are first derived. Then, ASC is applied to construct the virtual channel signal, and subsequently, BSS is utilized to realize aliasing removal in the FrFT domain. Finally, pulse modulated parameters (including pulsewidth, initial frequency, and chirp rate) can be effectively estimated. Simulation experiments prove the validity of the proposed method.

The Radon Fourier transform (RFT) is a commonly utilized method in high-speed weak target detection, which can effectively achieve coherent integration (CI) in the presence of migration through range cell (MTRC). However, the blind speed side-lobe (BSSL) phenomenon may appear in RFT outputs because of discrete pulse-sampling, restricted range resolution and confined CI period. For the case that scattering intensities of multiple targets differ significantly, the side-lobe of the strong target may conceal the main-lobe energy of the weak one and then result in loss detection. To obtain effective coherent detection for weak target, this paper proposes a novel weak target coherent detection method under strong target BSSL covering condition based on blind source separation. This method applies the RFT to obtain the outputs mixing of strong and weak target energy at first. Then, constructing the strong target RFT response from the position of its main-lobe. Finally, the BSS is used to process the strong target RFT response and mixing RFT outputs, effectively cleansing the side-lobes of the strong target RFT response and retaining the weak target RFT response. At this moment, the high-speed weak target detection results can be achieved. The efficacy of the proposed method is confirmed by simulation experiments and measured data processing.

Coherent multiple-input-multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multichannel and multiframe echo signal. However, due to the target motion and the geometric configuration differences of each node relative to the target, the multichannel multiframe 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 article. First, by applying the generalized radon Fourier transform (GRFT) to correct the RM/DFM and obtain the signal integration within each channel, we propose a multichannel 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. Second, a multiframe 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.

This article investigates the performance of coherent multiple-input multiple-out (MIMO) radar in the presence of independent but not necessarily identically distributed random amplitude errors. We first establish the coherent MIMO radar echo model that incorporates random amplitude errors. Then, to accurately assess the multi-channel signal integration performance with amplitude errors, we develop closed-form solutions for the average power, processing gain and detection probability. In addition, we provide a simplified expression of the average power in the case of independent and identically distributed (i.i.d.) errors. Further, to bridge the gap between theory and practical applications, two representative i.i.d. error distributions (uniform distribution and truncated Gaussian distribution) are examined. The average power, processing gain and detection probability are calculated in closed-form. Finally, numerical simulations confirm the validity and accuracy of closed-form analytical results.