Sensors, Vol. 20, Pages 3821: A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

Sensors, Vol. 20, Pages 3821: A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements Sensors doi: 10.3390/s20143821 Authors: Yifang Shi Sundas Qayyum Sufyan Ali Memon Uzair Khan Junaid Imtiaz Ihsan Ullah Darren Dancey Raheel Nawaz Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research