A Robust Coarse-to-Fine Ambiguity Resolution Algorithm for Moving Target Tracking Using Time-Division Multi-PRF Multiframe Bistatic Radars

Highlights: What are the main findings? A novel time-division multi-PRF multiframe (TD-MPMF) framework is proposed to achieve coherent integration, detection, and tracking in bistatic radar systems. The proposed TD-MPMF-SVM algorithm effectively resolves range and Doppler ambiguities by exploiting the multi-PRF coupling relationship and feature-domain SVM classification. What is the implication of the main finding? The method substantially enhances range and Doppler ambiguities’ resolution accuracy and robustness under low-SNR and high-speed target conditions. Demonstrate its capability for accurate long-range and high-speed target tracking under low-SNR bistatic radar conditions. The bistatic radar has been widely applied in moving target detection and tracking due to its unique bistatic perspective, low power, and good concealment. With the growing demand for detecting remote and high-speed moving targets, two challenges inevitably arise in the bistatic radar. The first challenge is the range ambiguity and Doppler ambiguity caused by long-range and high-speed targets. The second challenge is the low signal-to-noise ratio (SNR) of the target caused by insufficient echo power. Addressing these challenges is essential for enhancing the performance of the bistatic radar. This paper proposes a robust two-step ambiguity resolution algorithm for detecting and tracking moving targets using a time-division multiple pulse repetition frequency (PRF) multiframe (TD-MPMF) under the bistatic radar. By exploring the coupling relationship between measurement data under different PRFs and frames, the data in a single frame is divided into multiple subframes to formulate a maximization problem, where each subframe corresponds to a specific PRF. Firstly, all possible state values of the measurement data in each subframe are listed based on the maximum unambiguous range and the maximum unambiguous Doppler. Secondly, a coarse threshold is applied based on prior knowledge of potential targets to filter out candidates. Thirdly, the sequence is transformed from the polar coordinate into the feature transform domain. Based on the linear relationship between the range and velocity of multiple PRFs with moving targets in the feature domain, the support vector machine (SVM) is used to classify the target measurements. By employing the SVM to determine the maximum margin hyperplane, the true target range and Doppler are derived, thereby enabling the generation of the target trajectory. Simulation results show better ambiguity resolution performance and more robust qualities than the traditional algorithm. An experiment using a TD-MPMF bistatic radar is conducted, successfully tracking an aircraft target.