Data fusion method in multi-sensors autonomous tracking

Hai-jiao SHAN; Wei-ning LIU; Jia-cheng WANG; Pei-xun LIU; Zhi-gen LI; Yao-hua DENG

doi:10.3788/YJYXS20163108.0801

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Data fusion method in multi-sensors autonomous tracking

Driving and Controlling | 更新时间：2020-07-07

- Data fusion method in multi-sensors autonomous tracking
- Chinese Journal of Liquid Crystals and Displays Vol. 31, Issue 8, Pages: 801-809(2016)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
  3.广东工业大学, 广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20163108.0801
  CLC： V557.5;TP274.2
- Received：01 April 2016，
  
  Accepted：19 May 2016，
  
  Published：2016-08
- 稿件说明：
移动端阅览
Hai-jiao SHAN, Wei-ning LIU, Jia-cheng WANG, et al. Data fusion method in multi-sensors autonomous tracking[J]. Chinese journal of liquid crystals and displays, 2016, 31(8): 801-809.
DOI：

Hai-jiao SHAN, Wei-ning LIU, Jia-cheng WANG, et al. Data fusion method in multi-sensors autonomous tracking[J]. Chinese journal of liquid crystals and displays, 2016, 31(8): 801-809. DOI： 10.3788/YJYXS20163108.0801.

摘要

为了提高光电跟踪系统的稳定性和自主跟踪能力，对多传感器数据融合算法和数据的有效性估计进行研究，提出一种多传感器自主跟踪算法。首先按照统计学方法，实时估计各传感器数据的误差协方差。然后按照均方误差最小准则，对各路数据进行融合。将最小二乘多项式拟合法和记忆衰减因子应用到误差协方差估计中，提高了融合结果的可信度。最后，提出一种多传感器跟踪数据切换策略，自动选择有效传感器数据中置信度最高、跟踪效果最优的一路数据，从而实现自主稳定跟踪的目的。实验结果表明，使用改进后的数据融合算法比原始方法的最终传感器选择结果正确率提高37.5%左右。在几种典型的传感器数据异常情况下，该数据融合算法和多路数据切换策略能够完成自主跟踪的目的。

Abstract

In order to improve the stability and the ability of autonomous tracking of electro-optical tracking system

multi-sensor data fusion algorithm and estimate of the availability of sensor data is researched. In this paper a multi-sensor data fusion algorithm for autonomous tracking was put forward. First

the variance was estimated according to the statistics data real timely. Secondly

the fusion of the sensor data was made based on the rule of least mean square error. Then an improved estimation formula of the variance based on least square polynomial fit and memory attenuation factor was put forward which can improve the confidence interval of the fusion result. Finally

a multi-sensor switch-over strategy was put forward which can choose the sensor data that has the highest confidence interval and optimal tracking result from the valid ones autonomously. By this way we can achieve the aim of autonomous and stable tracking. Experiments results prove that the accuracy of sensor choice using the improved formula was improved 37.5% than the previous algorithm. This algorithm of data fusion and choice strategy can realize autonomous track in several typical scene of sensor-invalid.

关键词

Keywords

references

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