采用双迭代寻优算法的舰船复HRRP估计

doi:10.19665/j.issn1001-2400.20221104

摘要/Abstract

摘要：

针对非高斯海杂波背景下线性规划和迭代最小化稀疏恢复方法中目标散射点模型建模不精确导致舰船复高分辨距离像估计和径向尺寸估计性能下降的问题,提出了一种散射点位置和距离像幅度交替迭代寻优的新算法。该算法首先利用线性规划或迭代最小化稀疏恢复方法对舰船复高分辨距离像进行估计,然后采用拟牛顿法估计目标各个散射点在距离单元内的位置微偏移,在不增加模型复杂度的情况下构造出更加精细的目标散射点模型。重复利用上述双迭代过程,直到距离像的恢复误差满足预先设定的要求。通过仿真和实测数据实验对几种稀疏恢复方法的舰船复高分辨距离像估计性能以及径向尺寸估计性能进行了分析与对比。实验结果表明,提出的双迭代寻优算法与沿距离临界采样的线性规划和迭代最小化稀疏恢复方法相比,具有更低的舰船复高分辨距离像估计误差和径向尺寸估计误差;与沿距离过采样高分辨距离像模型的线性规划稀疏恢复方法相比,该方法在保持相近复高分辨距离像估计和径向尺寸估计精度的情况下显著减少了计算时间。

关键词: 线性规划, 杂波, 舰船复高分辨距离像估计, 径向尺寸估计, 稀疏恢复方法

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

中图分类号:

TP958.3

苏海龙, 水鹏朗. 采用双迭代寻优算法的舰船复HRRP估计[J]. 西安电子科技大学学报, 2023, 50(6): 105-119.

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.

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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