Radar High-Speed Moving Target Coherent Integration and Parameter Estimation Algorithm Using PSA-FAIR-NUFFT - Wang, Le; Tao, Haihong; Yang, Aodi; Yang, Fusen; Ma, Huihui (IEEE sensors journal, 2025)
With the extension of signal accumulation time, high-speed and low-radar-cross-section (RCS) aerial vehicles cause serious range cell migration (RCM) and Doppler frequency migration (DFM) in radar echoes, significantly degrading radar detection performance. To address the challenge of weak target detection, long-time coherent integration (LTCI) techniques enhance the signal-to-noise ratio (SNR) by concentrating energy in the time-frequency domain (TFD) or transform domain. However, existing LTCI methods primarily focus on acquiring the target's initial range parameters during the coherent integration interval (CPI), but neglecting potential distance deviation between the actual range and the initial range after the integration. This article proposes a novel LTCI algorithm, PSA-FAIR-NUFFT, combining frequency-domain angular inverse rotation (FAIR), nonuniform fast Fourier transform (NUFFT), and the proportional-integral-derivative (PID)-based search algorithm (PSA). The method effectively integrates signal energy and estimates final range parameters for moving targets. Specifically, FAIR establishes the frequency-domain axis rotation (AR) relationship to align the target trajectory into the final range unit. Concurrently, NUFFT performs coherent integration (CI) as well as the motion parameters estimation. To optimize the rotation angle search efficiency, an objective function based on the accumulated peak value is formulated with the PSA improving computational efficiency. This approach achieves precise parameter estimation with low complexity. Furthermore, for multitarget detection scenarios involving mixed-strength targets, a modified CLEAN technique is incorporated to sequentially separate strong and weak targets. Compared with typical LTCI algorithms, both numerical simulation experiments and real data analysis demonstrate the effectiveness and reliability of the proposed algorithm.