A Multi-Layer K-means Approach for Multi-Sensor Data Pattern Recognition in Multi-Target Localization

Multi-target tracking (MTT) is focused on the accurate detection and localization for multiple dynamic targets when measurements from these targets often come from numerous spatially distributed sensors. Obtaining the locations of the targets can be complex when sensors have limited sensing capabilities. Due to the potential applications of MTT, MTT can be dated back to 1960's initially related to aerospace applications [1]. The theoretical advances in MTT, new sensor capabilities, and more computational power have made it possible to apply MTT in numerous applications such as surveillance [2], [3], computer vision [4], [5], network and computer security [6] and sensor network [7]. In general, solving the MTT problem involves three tasks: (i) Extraction - extract target related information from the raw data acquired from the sensors; (ii) Data association - identify each target's corresponding measurements; and, (iii) Estimation - estimate the position of targets via single target tracking techniques (as shown [8]-[10]). Perhaps the most challenging task is to conduct data association because if data associated with each target is determined, it becomes much easier to conduct estimation for each individual target. In this paper, our focus is also on the data association problem. The main objective of this paper is to investigate the applicability of machine learning algorithms for the data association problem and then develop a new multi-layer learning algorithm by leveraging the advantages of different machine learning algorithms.