A Method to Accurately Estimate threshold Factors for Radar Constant False Alarm Ratio Detectors

刘盼芝; 韩崇昭; 介婧

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A Method to Accurately Estimate threshold Factors for Radar Constant False Alarm Ratio Detectors

更新时间：2025-12-08

- A Method to Accurately Estimate threshold Factors for Radar Constant False Alarm Ratio Detectors
- Vol. 43, Issue 2, Pages: 67-71(2009)
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN952
- Online First：10 February 2009，
  
  Published：2009
- 稿件说明：
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刘盼芝, 韩崇昭, 介婧. A Method to Accurately Estimate threshold Factors for Radar Constant False Alarm Ratio Detectors[J]. 2009, 43(2): 67-71.
DOI：

刘盼芝, 韩崇昭, 介婧. A Method to Accurately Estimate threshold Factors for Radar Constant False Alarm Ratio Detectors[J]. 2009, 43(2): 67-71. DOI：

摘要

针对恒虚警检测器在利用求解阈值因子方法时需要大量样本且搜索耗时较长的问题

提出了一种基于仿生微粒群的精确估计雷达恒虚警检测器阈值因子的方法.将指定虚警概率下的求解阈值因子问题转化为一个最小化问题

再利用微粒群方法对其进行求解

而微粒群优化采用基于种群的搜索方式.惯性因子可以随着迭代数的变化自适应调整

从而线性地减少了惯性因子的数值

致使算法具有平衡全局搜索、局部搜索的能力和较高的搜索效率.仿真结果表明

所提方法可在指定的精度下快速实现单雷达或者雷达组网等多种恒虚警检测器阈值因子的精确估计

节省了近50%的时间

提高了解的检测精度

并具有良好的鲁棒性和快速收敛等特点.

Abstract

A new method is proposed to improve the problem that many samples and long search time are required in the traditional methods to determine the threshold factor of constant false alarm ratio(CFAR)detector. The method estimates threshold factors for radar CFAR detectors based on particle swarm optimization(PSO)algorithm. The problem to determine the threshold factor under given false alarm probability is first converted into a minimization problem

then the minimization problem is solved using PSO method. The inertia weight is adaptively adjusted with the iteration during minimization. Therefore the method has better performance of global search and local search

and higher efficiency of search. Simulation results show that the method can accurately estimate the threshold factor for single radar or radar netting system. Compared with a genetic algorithm

50 percent of running time is saved

and the accuracy of estimation is improved. The numerical results indicate that the proposed scheme can quickly and accurately find the estimation of threshold factors for most CFAR detectors.

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Keywords

references

SRINIVASAN R. Fast simulation of smallest-of and geometric-mean CFAR detectors [J]. IEE Proceedings, 2001, 148(3): 186-191.

KENNEDY J, EBERHART R C, SHI Yuhui. Swarm intelligence [M]. San Francisco, CA, USA: Morgan Kaufman, 2001.

KENNEDY J, EBERHART R C. Particle swarm optimization [C]∥Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ, USA: IEEE, 1995: 1942-1948.

CUI Zhihua, ZENG Jianchao, CAI Xingjuan. A new stochastic particle swarm optimizer [C]∥Proceedings of the 2004 Congress on Evolutionary Computation. Piscataway, NJ, USA: IEEE, 2004: 316-319.

SHI Yuhui, EBERHART R C. Modified particle swarm optimizer [C]∥Proceedings of IEEE International Conference on Evolutionary Computation. Piscataway, NJ, USA: IEEE, 1998: 69-73.

KENNEDY J. The particle swarm: social adaptation of knowledge [C]∥Proceedings of IEEE International Conference on Evolutionary Computation. Piscataway, NJ, USA: IEEE, 1997: 303-308.

FINN H M, JOHNSON R S. Adaptive detection mode with threshold control as a function of spatially sampled clutter-level estimates [J]. RCA Review, 1968, 29(2): 414-464.

ROHLING H. Radar CFAR threshold in clutter and multiple target situations [J]. IEEE Trans on Aerospace and Electronic Systems, 1983, 19(1): 608-621.

UNER M K, VARSHNEY P K. Distributed CFAR detection in homogeneous and nonhomogeneous backgrounds [J]. IEEE Trans on Aerospace and Electronic Systems, 1996, 32(1): 84-96.

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