Development of Generalized Track to Track Association Algorithm Based on Hierarchical Clustering

Abstract

Track-to-track association has emerged as a signi cant problem to extract critical information from the tracks shared by multiple sensors interconnected through a wireless network. e information shared in the form of packets over a network contains biases that results in deviation of the object position from its real position. e tracks obtained are initially preprocessed to analyze, compute and update the state co-variance matrix of each sensor in Earth Centered Earth Fixed frame, which is a function of measurement errors, process model(s) and the Markov chain transition matrix. In inclusion, a track synchronization lter is used to deal with the delays. A linear lter is used to predict the objects state at a speci c time before proceeding to the track-to-track association stage. An unsupervised machine learning technique is proposed for track-to-track association problem in a network of multiple sensors and objects in the presence of sensor noises. e proposed association technique requires state vector and the corresponding state co-variance matrix. e approach involves classifying the objects data in the form of clusters using a variant of hierarchical agglomerative clustering algorithm. is classi cation is o en based on a prede ned threshold which is selected from past data of similar scenarios. A more realistic approach is proposed for threshold calculation that updates with the current object data and is independent of past scenarios data. e proposed threshold estimation technique is put to the test against a variety of scenarios, ranging from simple to extremely complex. Furthermore, the accuracy of the proposed algorithm is determined by implementing it against multiple complex and realistic environments. e e ect of alternating systematic and random biases on the accuracy of the proposed algorithm is analyzed against range, azimuth, and elevation of object.