Systems Engineering and Electronics ›› 2022, Vol. 44 ›› Issue (12): 3811-3820.doi: 10.12305/j.issn.1001-506X.2022.12.26

• Guidance, Navigation and Control • Previous Articles     Next Articles

Research on long-time coherent integration and parameter estimation algorithm of high-speed maneuvering targets

Yuchao YANG*, Ming FANG, Chenfan ZHAO, Yueqi WANG, Gang FANG   

  1. Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China
  • Received:2021-09-06 Online:2022-11-14 Published:2022-11-24
  • Contact: Yuchao YANG

Abstract:

Long-time coherent integration is one of the important techniques to improve radar detection performance. The range migration (RM), range curvation (RC), Doppler frequency migration (DFM) and Doppler frequency curvation (DFC) induced by the movement of high-speed maneuvering target during the long-time coherent integration would bring about serious integration signal-to-noise ratio (SNR) loss, which may furthermore cause the target detection performance of the radar sharp deterioration in the background of noise. In order to solve the problem, the paper proposes a parameterized long-time integration algorithm for high-speed maneuvering targets. The method applies keystone transform (KT) to eliminate linear RM firstly. Then coherently integrated modified cubic phase function (CIMCPF) and coherently integrated modified high-order ambiguous function (CIMHAF) methods are utilized respectively to estimate the first-order and second-order accelerations. Thanks to the use of signal coherent property, the method is still effective at low signal-to-noise ratio. Furthermore, since there is no brute-force search process, the computational complexity of the method is low. Simulation results and analysis demonstrate the excellent performance of the proposed method.

Key words: long-time coherent integration, keystone transform (KT), coherently integrated modified cubic phase function (CIMCPF), coherently integrated modified high-order ambiguous function (CIMHAF), parameter estimation

CLC Number: 

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