Proc. SPIE. 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV
KEYWORDS: Cognitive modeling, Detection and tracking algorithms, Data modeling, Databases, Computer simulations, Data processing, Signal processing, Algorithm development, Systems modeling, Data fusion
This paper introduces the merge at a point (MAP) algorithm to detect the vehicles convoys whose destination locations are unknown. The algorithm will predict the merged vehicles identification numbers in an iterative manner. We applied this method using the simulated Ground Moving Target Indicator (GMTI) data. The technique is similar to the dead reckoning and Kalman filtering algorithms. This algorithm consists of following procedures: 1) approximates the destination locations for each vehicle using its tracks, 2) validates what vehicles are going to merge at these predicted destination locations using the minimum error solution (MES), and 3) predicts the future destination locations where the vehicles will be merged at for the next iteration. This algorithm will be iteratively processed until predicted destination locations converge. We can use this algorithm to associate the vehicles that will merge to some unknown destination locations. It also has the potential to identify the convoy names and the threats associated with these vehicle groups.
This paper describes a novel technique to detect military convoy’s moving patterns using the Ground Moving Target Indicator (GMTI) data. The specific pattern studied here is the moving vehicle groups that are merging onto a prescribed location. The algorithm can be used to detect the military convoy’s identity so that the situation can be assessed to prevent hostile enemy military advancements. The technique uses the minimum error solution (MES) to predict the point of intersection of vehicle tracks. Comparing this point of intersection to the prescribed location it can be determined whether the vehicles are merging. Two tasks are performed to effectively determine the merged vehicle group patterns: 1) investigate necessary number of vehicles needed in the MES algorithms, and 2) analyze three decision rules for clustering the vehicle groups. The simulation has shown the accuracy (88.9% approx.) to detect the vehicle groups that merge at a prescribed location.