采用双迭代寻优算法的舰船复HRRP估计

doi:10.19665/j.issn1001-2400.20221104

Abstract

Abstract:

In estimation of the complex high-resolution range profile(HRRP) of a ship in the sea clutter,the inaccurate target scattering model leads to the performance degradation of the existing linear program-based(LP-based) sparse recovery method and the sparse recovery method via iterative minimization(SRIM).In this paper,a bi-iterative optimization algorithm is proposed to solve this problem.The algorithm first adopts the LP-based sparse recovery method or SRIM method to estimate the complex HRRP of the ship at a given HRRP model and then tunes the position of the scatterer at each range cell by using the quasi-Newton algorithm to construct a more refined target scattering point model with the same scale.The bi-iterative process above is repeated until the recovery error of the ship HRRP meets the demand specified in advance.Through simulation and measured data experiments,the performance of ship complex HRRP estimation and that of radial size estimation with several sparse recovery methods are analyzed and compared.Experimental results show that the proposed bi-iterative optimization algorithm attains less estimation errors in ship complex HRRP and radial size than the LP-based sparse recovery method and SRIM method and requires much less computational time when it attains a comparable performance with the LP-based sparse recovery method using the oversampled HRRP model.

Key words: linear programming, clutter, estimation of complex high-resolution range profile of ship, radial size estimation, sparse recovery method

CLC Number:

TP958.3

SU Hailong, SHUI Penglang. Estimation of the complex HRRP of ships using the bi-iterative optimization algorithm[J].Journal of Xidian University, 2023, 50(6): 105-119.

Figures/Tables 13

References 18

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舰船编号	舰船类型	长度/m	宽度/m	方位角/(°)	船首向/(°)	平均信杂比/dB	峰值信杂比/dB
1	大货轮	292	45	39	34	45.52	66.45
2	挖泥船	133	24	164	96	44.34	61.31

稀疏问题的规模	稀疏恢复方法	计算时间
	迭代最小化稀疏学习方法	65.52
	线性规划稀疏恢复方法	120.34
	迭代最小化稀疏恢复方法	88.14
感知矩阵A(486×422)	过4采样HRRP模型-线性规划稀疏恢复方法	2 683.24
	过8采样HRRP模型-线性规划稀疏恢复方法	3 267.10
	交替循环寻优-线性规划稀疏恢复方法	218.28
	交替循环寻优-迭代最小化稀疏恢复方法	169.53

稀疏问题的规模	稀疏恢复方法	计算时间
	迭代最小化稀疏学习方法	8.12
	线性规划稀疏恢复方法	11.17
	迭代最小化稀疏恢复方法	8.48
感知矩阵A(272×214)	过4采样HRRP模型-线性规划稀疏恢复方法	349.29
	过8采样HRRP模型-线性规划稀疏恢复方法	1 159.33
	交替循环寻优-线性规划稀疏恢复方法	25.62
	交替循环寻优-迭代最小化稀疏恢复方法	21.18

Estimation of the complex HRRP of ships using the bi-iterative optimization algorithm

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