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Open AccessArticle

A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements

1
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
2
Department of Electrical and Computer Engineering, CUI, Abbottabad Campus, Abbottabad 22060, Pakistan
3
Department of Mechatronics Engineering, Mehran University, Jamshoro 76090, Pakistan
4
Department of Electrical Engineering, Bahria University Islamabad Campus, Islamabad 44230, Pakistan
5
School of Computer Science, Manchester Metropolitan University, Manchester M15 6BH, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(14), 3821; https://doi.org/10.3390/s20143821
Received: 2 March 2020 / Revised: 30 April 2020 / Accepted: 15 May 2020 / Published: 9 July 2020
(This article belongs to the Section Remote Sensors)
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 Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD). View Full-Text
Keywords: tracking; estimation; OOSM; false track discrimination; sensor fusion tracking; estimation; OOSM; false track discrimination; sensor fusion
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MDPI and ACS Style

Shi, Y.; Qayyum, S.; Memon, S.A.; Khan, U.; Imtiaz, J.; Ullah, I.; Dancey, D.; Nawaz, R. A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements. Sensors 2020, 20, 3821.

AMA Style

Shi Y, Qayyum S, Memon SA, Khan U, Imtiaz J, Ullah I, Dancey D, Nawaz R. A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements. Sensors. 2020; 20(14):3821.

Chicago/Turabian Style

Shi, Yifang; Qayyum, Sundas; Memon, Sufyan A.; Khan, Uzair; Imtiaz, Junaid; Ullah, Ihsan; Dancey, Darren; Nawaz, Raheel. 2020. "A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements" Sensors 20, no. 14: 3821.

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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