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Open AccessArticle

Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments

1
Department of Electrical Engineering, COMSATS University, Abbottabad Campus, Abbottabad 22060, Pakistan
2
Department of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulsan 44919, Korea
3
School of Automation, Hangzhou Dianzi University, Xiasha Higher Education Zone, 2rd Street, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(11), 4043; https://doi.org/10.3390/s18114043
Received: 2 September 2018 / Revised: 26 September 2018 / Accepted: 15 November 2018 / Published: 20 November 2018
(This article belongs to the Section Physical Sensors)
A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch–Tung–Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy. View Full-Text
Keywords: information fusion; Kalman filter; out-of-sequence measurements; Rauch–Tung–Striebel; smoothing; state estimation; Time to Impact information fusion; Kalman filter; out-of-sequence measurements; Rauch–Tung–Striebel; smoothing; state estimation; Time to Impact
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MDPI and ACS Style

Ullah, I.; Qureshi, M.B.; Khan, U.; Memon, S.A.; Shi, Y.; Peng, D. Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments. Sensors 2018, 18, 4043. https://doi.org/10.3390/s18114043

AMA Style

Ullah I, Qureshi MB, Khan U, Memon SA, Shi Y, Peng D. Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments. Sensors. 2018; 18(11):4043. https://doi.org/10.3390/s18114043

Chicago/Turabian Style

Ullah, Ihsan; Qureshi, Muhammad B.; Khan, Uzair; Memon, Sufyan A.; Shi, Yifang; Peng, Dongliang. 2018. "Multisensor-Based Target-Tracking Algorithm with Out-of-Sequence-Measurements in Cluttered Environments" Sensors 18, no. 11: 4043. https://doi.org/10.3390/s18114043

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