Dynamic model‐based feature aided data association filter in target tracking. Issue 2 (1st February 2020)

• Record Type: Journal Article
• Authors: Kim, Dohyeung; Hwang, Inseok
• Is Part Of: IET radar, sonar & navigation. Volume 14:Issue 2(2020)
• Page Start: 279
• Page End: 289
• Publication Date: 2020-02-01
• Subjects: target tracking -- radar tracking -- clutter -- sensor fusion; dynamic model‐based feature -- data association filter -- data association problem -- existing data association filters -- statistical model -- target behaviour -- feature characteristics -- inaccurate model -- heavily cluttered signal‐to‐noise ratio environments -- low signal‐to‐noise ratio environments -- feature models -- target kinematics -- illustrative target tracking scenarios; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2019.0381
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : In this study, the authors consider the data association problem where the feature of a target is available. Most existing data association filters mainly use a statistical or simple model of the feature without explicitly considering the correlation between the target behaviour and feature characteristics. The inaccurate model of the feature could lead to divergence of the estimation error or the loss of a target in heavily cluttered and/or low signal‐to‐noise ratio environments. To address the problem, they first develop feature models (e.g. target dimensions) dependent on the target behaviour (i.e. the distance between the sensor and the target, and the aspect angle between the longitudinal axis of the target and the axis of sensor line of sight). Then they propose a data association filter which can facilitate the feature models dependent on the target kinematics to reduce the misassociations. The performance of the proposed feature models and data association filter are demonstrated with illustrative target tracking scenarios.