Detection and tracking of the trajectories of dynamic UAVs in restricted and cluttered environment. (30th November 2021)

• Record Type: Journal Article
• Authors: Memon, Sufyan Ali; Ullah, Ihsan
• Is Part Of: Expert systems with applications. Volume 183(2021)
• Publication Date: 2021-11-30
• Subjects: Detection -- Estimation -- False-track discrimination (FTD) -- Smoothing -- Tracking -- Targets existence -- UAV; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115309
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A novel smoothing data association idea in the LM-IPDA algorithm. The significant detection and tracking performance of UAV are analyzed by experiment. Backward multi-tracks are used to estimate a forward track in past scan for smoothing. False-track discrimination is obtained using smoothing target existence probability. The algorithm reduces RMS errors, and tracks multi-vehicles in clutter efficiently. Abstract: The unidentified number of unmanned aerial vehicles (UAVs) can execute aggressive maneuvers in the restricted and the cluttered environment. Therefore, it is difficult to detect and track the uncertain motion of the UAV target in such complex environment. In addition, multi-target tracking (MTT) algorithms such as joint data association approach faces various computational complexities that could exceeds the available computation resources. This paper develops a novel smoothing data association idea in a linear multi-target (LM) tracking based on integrated probabilistic data association (sLM-IPDA) algorithm that acts like a single target tracker in the MTT situation. The significant detection and tracking performance of a UAV are validated without a-prior information of the UAV's initial position. The forward and backward tracks are initialized separately using sensor measurements received in each scan. The sLM-IPDA estimates the backward multi-tracks simultaneously associating backward tracks in a subsequent predicted forward track for fusion. Thus, a forward track state estimate is obtain using the smoothing (fusion) measurements. This significantly improves estimation accuracy for large number of cross-over targets in heavy clutter. Numerical assessments of the sLM-IPDA are verified using both simulation and experiment to demonstrate the application of the proposed algorithm.