This study focuses on the information extraction from reported sensor data in the communication system of wide-area-search munitions (WASMs). Such sensor data could be erroneous and inconsistent. For example, two WASMs might detect the same target, but associate it with two different targets and tracks. Similarly, two WASMs might detect two distinct targets, but recognize them as the same target. The research challenge is how to fuse both accurate and inaccurate information broadcasted from WASMs, and reconstruct the battle space for accurate target tracking. For each of the detected target points, WASMs provide its location information, detection time, and directional velocity. We, herein, propose a target clustering approach to group target points detected by WASMs and identify the track of individual targets. Our approach differs from traditional clustering techniques as it performs clustering using the time and orientation information, in addition to the distance in the Euclidean space. Our approach employs a network modeling technique to reconstruct all target points and their feasible movement, and a new optimization technique to find the most probable target tracks. Our approach can also determine the optimal number of clusters (targets) automatically from the input data. In this study, distributed interactive simulation, a real-time simulation of a network's information exchange, is used to generate battle space test instances that are used in evaluating the proposed framework. Based on seven realistically simulated instances, the computational results show that our approach provides extremely accurate target-tracking results in a timely fashion. We also compare our results with those obtained using the k-means clustering technique. On average, our approach reconstructs the real target tracks with about 95% accuracy in less than 10 s, while the k-means clustering results yields about 80% accuracy in a similar computational time.
['Zhe Liang', 'Wanpracha Art Chaovalitwongse', 'Andrew Rodriguez', 'David E. Jeffcoat', 'Don A. Grundel', "John K. O'Neal"]
Optimization of Spatiotemporal Clustering for Target Tracking From Multisensor Data