Radar Coherent Detection for Maneuvering Target Based on Product-Scaled Integrated Cubic Phase Function

被引:5
作者
Chen, Jinyang [1 ,2 ]
Jin, Ke [1 ]
Yu, Shangjiang [2 ]
Lai, Tao [3 ]
Zhao, Yongjun [1 ]
机构
[1] PLA Strateg Support Force Informat Engn Univ, Zhengzhou 450001, Henan, Peoples R China
[2] PLA, Acad Mil Sci, Inst Def Engn, Luoyang 471000, Peoples R China
[3] Sun Yat Sen Univ, Sch Elect & Commun Engn, Guangzhou 510000, Guangdong, Peoples R China
基金
中国国家自然科学基金;
关键词
RADON-FOURIER TRANSFORM; PARAMETER-ESTIMATION; ESTIMATION ALGORITHM; KEYSTONE TRANSFORM; WEAK TARGET;
D O I
10.1155/2019/8691903
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper considers the long-time coherent detection problem for maneuvering targets with jerk motion. A novel method based on product-scaled integrated cubic phase function (PSICPF) is proposed. The main strategy of PSICPF is to estimate target's motion parameters along the slow time for each range frequency cell. In order to eliminate the coupling terms between range frequency and slow time, the scaled nonuniform fast Fourier transform (SNUFFT) is newly defined in the integrated cubic phase function (ICPF). Then, the product operation is employed to coherently synthesize the estimation results, improve the antinoise performance, and suppress the cross terms. Finally, coherent integration is achieved via keystone transform (KT) and fold factor searching. Analysis demonstrates that the SNUFFT has the same computational complexity with nonuniform fast Fourier transform (NUFFT), and thus the PSICPF could be efficiently implemented via complex multiplications, the fast Fourier transform (FFT), and NUFFT. Detailed comparisons with other representative methods in computational cost, motion parameter estimation performance, and detection ability indicate that the PSICPF could achieve a good balance between the computational cost and detection ability. Simulations and real data processing results are presented to verify the effectiveness of the proposed method.
引用
收藏
页数:16
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