Tracking with MIMO sonar systems: applications to harbour surveillance. Issue 4 (1st April 2017)

• Record Type: Journal Article
• Authors: Pailhas, Yan; Houssineau, Jeremie; Petillot, Yvan René; Clark, Daniel Edward
• Is Part Of: IET radar, sonar & navigation. Volume 11:Issue 4(2017)
• Page Start: 629
• Page End: 639
• Publication Date: 2017-04-01
• Subjects: sonar tracking -- target tracking -- surveillance -- clutter -- sonar target recognition -- object tracking -- tracking filters -- signal classification; tracking filter -- signal‐to‐noise ratio -- acoustic field -- analogue filter -- acoustical time reversal techniques -- classification capability tracking -- robust multiobject tracking -- independent stochastic populations -- hypothesised filter -- analogue tracker -- digital tracker -- underwater target tracking problem -- autonomous underwater vehicles -- transmitter‐receiver pair -- MIMO simulator -- low‐profile mid‐water target recognition -- low‐profile mid‐water target tracking -- strong multipath clutter -- dense clutter -- multiple‐input multiple‐output sonar systems -- harbour surveillance -- MIMO sonar systems; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2016.0080
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Multiple‐input multiple‐output (MIMO) sonar systems offer new perspectives for area surveillance especially in complex environments where strong multipath and dense clutter can become very challenging. This study proposes a MIMO sonar system based scheme to tackle the difficult problem of harbour surveillance. An emphasis is put on recognition and tracking on low‐profile mid‐water targets. First, a MIMO simulator which can compute synthetic raw data for any transmitter/ receiver pair in a multipath, cluttered and dynamic environment is presented. The study then proposes two radically different methods for the underwater target tracking problem in complex environment: a digital tracker and an analogue tracker. On the digital side, an implementation of the recently developed hypothesised filter for independent stochastic populations is presented. This filter enables robust multi‐object tracking as well as track classification capabilities without the use of heuristics. An analogue filter based on acoustical time reversal techniques is also introduced. This filter directly uses the returned acoustic field from the scene to focus the sound on the expected target position, hence improving the signal‐to‐noise ratio on the target in complex environments and taking full advantage of the MIMO architecture.