Maintaining the constant false alarm rate (CFAR) is an important issue for the dynamic programming‐based track‐before‐detect (DP‐TBD) in low signal‐to‐noise ratio environment. However, the existing method for analysing the false alarm probability, based on extreme value theory (EVT), leads to the inaccuracy of the obtained detection threshold. In this study, a new analytical approach to compute the false alarm probability of DP‐TBD is presented. In the proposed method, the generalised Pareto distribution is utilised to approximate the false alarm probability based on the peaks over threshold model, which can maintain CFAR for DP‐TBD method in the low signal‐to‐noise environment effectively. Simulation results show that this approach provides a more accurate false alarm probability estimation than previous EVT methods.

Main-lobe interrupted-sampling repeater jamming is a coherent type of interference. As this interference is injected through the main lobe of the antenna into a radar system, the interference heavily degrades the performance of the radar system. In this paper, we propose an anti-jamming method based on minimum variance distortionless response (MVDR) that estimates a covariance matrix by obtaining small-sample pure jamming signals through jamming recognition. Time-frequency analysis is performed in the one-dimensional range profile of the pulse-compressed radar echo, and the snapshot sampling of pure jamming is achieved by searching the range gates for the target and the jamming in a two-dimensional range-frequency graph considering the time-frequency differences between the target and jamming signals. Assuming that the available data of snapshot sampling are insufficient, a covariance matrix for pure jamming is reconstructed through an iterative adaptive approach (IAA) and used as the training sample for the MVDR beamformer to suppress main-lobe interrupted-sampling repeater jamming. Finally, the method is validated by comparing the simulation results with the measured results.

The performance of wideband radar detectors can be further improved if the features of a target's high-resolution range profile (HRRP) are fully utilized. In this paper, a target adaptive double threshold detection method is proposed. First, the amplitude distribution parameters of the HRRP's range cells are extracted for data generation, and the detection probability is statistically calculated. The online gradient descent method is then used to adaptively adjust the second thresholds of the double threshold detector, thereby improving the detection performance with a given constant false alarm rate. The effectiveness of the proposed algorithm is verified using two sets of measured data. The proposed algorithm exhibits superior detection performance compared to traditional detection algorithms for range-spread targets.

Conventional detectors in tracking system generally work according to the Neyman-Pearson criterion. Recently, Bayesian detection has become an alternative for many trackers such as probabilistic data association filter (PDAF). However, a critical problem of the tracking system with Bayesian detection is to predict the tracking performance without simulations. As the Bayesian detection is introduced, clutters are nonuniformly distributed and the detection threshold varies with time, which increases the difficulty of the analysis. In this paper, an offline method is developed to predict the performance of the PDAF with Bayesian detection (PDAF-BD). In the approach, the information reduction factor (IRF) of the PDAF-BD is derived, describing the influence of measurement origin uncertainty. Unlike the IRF of PDAF, the IRF of PDAF-BD has analytical expression which is efficient in computation. On this basis, the offline recursion of the error covariance and the quantification of track loss are achieved. The experiments show that the nonsimulated result generated by the proposed algorithm is reasonably close to the simulated one.

The edges of a target provide essential geometric information and are extremely important for human visual perception and image recognition. However, due to the coherent superposition of received echoes, the continuous edges of targets are discretized in synthetic aperture radar (SAR) images, i.e., the edges become dispersed points, which seriously affects the extraction of visual and geometric information from SAR images. In this article, we focus on solving the problem of how to recover smooth linear edges (SLEs). By introducing multiangle observations, we propose an SAR parametric image reconstruction method (SPIRM) that establishes a parametric framework to recover SLEs from SAR echoes. At the core of the SPIRM is a novel physical characteristic parameter called the scattering-phase-mutation feature (SPMF), which reveals the most essential difference between the residual endpoints of a disappeared SLE and points. Numerical simulations and real-data experiments demonstrate the robustness and effectiveness of the proposed method.