High-Speed Target Detection Algorithm Based on Sparse Fourier Transform

Radar detection of high-speed targets suffers from range walks during the integration time. Methods in current use for mitigating range walks are beset by high computational complexity therein that hinders practical real-time processing. In this context, we exploit the sparsity of the target echo in the transform domain and propose an efficient range walk mitigation algorithm based on sparse Fourier transform (SFT). Concretely, the input long echo sequence is first divided into short overlapped segments with an SFT bucket structure. Then, speed compensation is performed to the short segments, which involves less complex multiplications. Subsequently, SFT is employed which efficiently obtains the Fourier transform of the long sequence such that the range alignment of the multi-pulse echo is accomplished. As such, the proposed SFT-based algorithm significantly reduces the amount of complex multiplications required in speed compensation and long sequence transform, and thus substantially improves the computational efficiency. In this paper, the selection of the window function and the length of segments are examined for their influence on the detection performance with different signal-to-noise ratios. The superiorities of the proposed algorithm in both detection performance and computational efficiency are demonstrated by numerical experiments. The proposed algorithm can potentially find applications in other radar systems such as synthetic aperture radars, inverse synthetic aperture radars, and so on where echo range walk is also encountered.