Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (11): 3417-3422.doi: 10.13229/j.cnki.jdxbgxb.20240444

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Distributed multi-sensor target joint localization under state data covariance cross fusion algorithm

Peng ZHANG1,2(),Shu-yu ZHOU1,2,Peng LIU2,3   

  1. 1.School of Instrumentation and Electronic，North University of China，Taiyuan 030051，China
    2.Academy for Advanced Interdisciplinary Research，North University of China，Taiyuan 030051，China
    3.School of Electrical and Control Engineering，North University of China，Taiyuan 030051，China
  • Received:2024-04-25 Online:2024-11-01 Published:2025-04-24

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Abstract:

Aiming at the problems of single sensor target localization technology being unable to adapt to complex environments and susceptible to external interference， a distributed multi-sensor target joint localization method based on state data covariance cross fusion algorithm is proposed. Observing the position information of target objects through distributed multiple sensors and using the position information as target state data； On this basis， input the target state observation values of each sensor into the Kalman filter for error compensation， in order to improve the accuracy of subsequent target joint positioning； Therefore， the covariance cross fusion algorithm is used to fuse the observation values of each sensor after error compensation， and the fusion value is the final target position， thereby completing the joint target localization. The experimental results show that the intersection to union ratio of the positioning results for 10 target objects is close to 1， and the obtained target object position coordinates are closest to the actual position coordinates of the object. The proposed method has strong anti-interference performance and high accuracy in target localization.

Key words: covariance cross fusion, distributed multi-sensor, joint target localization, kalman filtering, error compensation

CLC Number: 

  • TN953

Fig.1

Distributed sensor networking"

Table 1

Setting of the experimental parameters"

参数名称参数值

卡尔曼滤

波器参数

初始位置（0 m， 0 m）
初始速度（0 m/s， 0 m/s）

协方差交叉融合

算法参数

状态转移矩阵维度4×4
融合权重0.4
融合周期每5个采样周期一次

Fig.2

Error compensation effect"

Table 2

Average intersection to union ratio"

目标序号本文方法文献［3］方法文献［4］方法
10.967 50.867 80.813 5
20.956 30.843 90.796 2
30.943 30.901 50.802 3
40.962 70.876 20.813 7
50.949 60.891 60.791 5
60.943 90.882 50.823 5
70.954 60.902 80.815 2
80.963 80.863 70.799 3
90.947 10.855 40.806 8
100.958 40.881 60.837 5

Fig.3

Target iocalization results"

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