Sensor selection for target tracking based on single dimension information gain

Abstract

In sensor management for target tracking, the existing sensor selection methods based on information entropy generally select the sensor that brings the most information gain for target; however, for multi-dimension state vector of target, information gain for target in the specified dimension cannot be guaranteed. For the ambiguity problem in the measure for multi-dimension state vector using information entropy, this study proposes a sensor selection method based on single dimension information gain of the target, which uses particle filter to compute the marginal probability density of target state in the specified dimension and exploits the Kullback–Leibler divergence to measure the information gain of target in the specified dimension, in such a way, by selecting the sensor that brings the most information gain for target in the specified dimension. The proposed method can guarantee higher tracking accuracy gain of target in the specified dimension and obtain more reliable tracking results. Experiment results illustrate that the proposed method has a higher track accuracy of position and velocity in the specified dimension than sensor selection method based on target state entropy.

Keywords

• particle filtering (numerical methods)
• entropy
• target tracking
• probability
• vectors
• sensors
• particle filter
• kullback–leibler divergence
• marginal probability density
• target state entropy
• sensor selection method
• multidimension state vector
• information entropy
• sensor management
• single dimension information gain
• target tracking