Measurement Data Classification Optimization Based on a Novel Evolutionary Kernel Clustering Algorithm for Multi-Target Tracking

Abstract

In the field of range-based multi-target localization and tracking, measurement data of each time cannot associate with its corresponding target, and clustering analysis can be used to solve this problem. In this paper, a novel evolutionary kernel clustering algorithm was developed for range-based multi-target tracking in wireless sensor networks. First, the locations of multi-targets are predicted according to the previous trajectories. Second, we apply the clustering number recognition algorithm to filter out the outliers and calculate the initial cluster center by analyzing the density of each measurement data. For each cluster, its relationship with corresponding target is established according to the predictive position and the initial cluster center. Third, the density factors of each measurement data are fused into the Gaussian kernel function to improve the accuracy of the cluster center. Finally, the accurate position of each target at current moment is calculated based on the predictive position and the measurement data set of corresponding cluster. Two different experiments are done in this paper: in the first experiment, the clustering performance of our proposed algorithm is evaluated based on the training data set. In the second one, the accuracy improvement of range-based target tracking is shown when proposed algorithm is used to analyze the measurement data. The tracking results show that the proposed algorithm is more robust to large localization errors.