A Data Fusion Algorithm for Distributed Serial Detection Systems Based on Joint Optimization of Sensor Decision Thresholds

相明; 张亚明; 葛新雷

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A Data Fusion Algorithm for Distributed Serial Detection Systems Based on Joint Optimization of Sensor Decision Thresholds

更新时间：2025-12-08

- A Data Fusion Algorithm for Distributed Serial Detection Systems Based on Joint Optimization of Sensor Decision Thresholds
- Vol. 42, Issue 10, Pages: 1209-1212+1234(2008)
- 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： TP274
- Online First：10 October 2008，
  
  Published：2008
- 稿件说明：
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相明, 张亚明, 葛新雷. A Data Fusion Algorithm for Distributed Serial Detection Systems Based on Joint Optimization of Sensor Decision Thresholds[J]. 2008, 42(10): 1209-1212+1234.
DOI：

相明, 张亚明, 葛新雷. A Data Fusion Algorithm for Distributed Serial Detection Systems Based on Joint Optimization of Sensor Decision Thresholds[J]. 2008, 42(10): 1209-1212+1234. DOI：

摘要

针对传感器观测相关的条件下最优分布式检测融合算法较为复杂这一问题

提出了一种基于似然比判决规则的系统性能优化方法.该方法的基本思想是

限定各部传感器采用似然比判决规则

并在此约束条件下

联合优化各部传感器的判决门限

以使系统检测性能达到最优.对于由N部传感器组成的分布式串行检测融合系统

在Bayes准则下推导了各部传感器判决门限联合最优化的必要条件

得到了最优传感器判决门限满足的系统方程

并在此基础上给出了求解最优传感器判决门限的数值迭代算法.由于将传感器判决规则的联合优化问题简化为了判决门限的联合优化问题

因此该算法计算量较小

收敛性较好

更加便于工程应用.

Abstract

In order to reduce the computational complexity of optimal fusion algorithms with dependent sensor observations

a method for optimizing the system performance based on likelihood ratio decision rules is proposed. The basic idea of the method is to optimize the system performance by constraining the sensor decision rules to be likelihood ratio decision rules

and optimizing the sensor decision thresholds jointly. The necessary condition for the joint optimal sensor decision thresholds is derived for the distributed serial detection systems consisting of N sensors under Bayesian criterion

and the system equations that are satisfied by the optimal sensor decision thresholds are obtained. An iterative algorithm is also proposed to solve for the optimal sensor decision thresholds. Since the joint-optimization of sensor decision rules is simplified to the joint-optimization of decision thresholds

the amount of computational costs is reduced. The algorithm converges well and is more applicable in real applications.

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references

TENNEY R R, Jr SANDELL N R. Detection with distributed sensors [J]. IEEE Trans on Aerospace Electron Syst, 1981, 17(4):501-510.

WILLETT P, SWASZEK P F, BLUM R S. The good, bad and ugly: distributed detection of a known signal in dependent Gaussian noise [J]. IEEE Trans on Signal Processing, 2000, 48(12):3266-3279.

ZHU Yunmin, BLUM R S, LUO Zhiquan, et al. Unexpected properties and optimum distributed sensor detectors for dependent observation cases [J]. IEEE Trans on Automat Contr, 2000, 45(1):62-72.

YAN Qing, BLUM R S. Distributed signal detection under the Neyman-Pearson criterion [J]. IEEE Trans on Inform Theory, 2001, 47(5):1368-1377.

TANG Zhuangbo, PATTIPATI K R, KLRINMSN D L. A distributed M-ary hypothesis testing problem with correlated observations [J]. IEEE Trans on Automat Contr, 1992, 37(7):1042-1046.

VARSHNEY P K. Distributed detection and data fusion[M]. Berlin,Germany: Springer-Verlag, 1996.

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