Processor based estimation of targets

Abstract

Data fusion techniques combine data from multiple sensors and related information from associated databases, to achieve improved accuracies and more specific inferences than could be achieved by the use of a single sensor alone. Objective of this paper is to implement data association algorithms for target tracking based on range only measurements given by multiple sensors located at different positions using a TMS processor, first in a single target scenario and then in a multi-target scenario where ranges obtained from each sensor are required to be associated to the correct targets. In case of a single target multi-sensor (STMS) scenario, Gauss-Newton iterative method based Data Fusion is carried out to determine the target position. In case of multi-target multi-sensor (MTMS) scenario, each radar provides data regarding range of targets as detected by it. Considering all possible combinations of ranges, the probable fused target positions are estimated using Gauss-Newton technique. The near approximated fused position is then determined from probable positions by Minimum Mean Square Estimation (MMSE).