A novel approach to high frequency radar ship tracking exploiting aspect diversity

Abstract

Low-power High-Frequency Surface-Wave (HFSW) radars, designed for oceanic applications, are promising tools also for long-range surveillance in open-water Multi-Target Tracking (MTT) applications. This paper focuses on the fusion of multiple aspects over single-perspective systems. The singlesensor tracking steps, made up by the Joint Probabilistic Data Association (JPDA) rule and the Unscented Kalman Filter (UKF), are followed by a Track-to-Track association and Fusion (T2TF) strategy. Tracking performance improvements are investigated using real data collected by two simultaneously operated HFSW-radars.

EKF based trajectory tracking and integrity monitoring of AIS data

“…in [7] or [8]. In this work, the state of the vessel for the CV at time instance k is defined as follows:…”

“…Two motion models will be introduced, that are expected to represent the vessels dynamics either in nearly straight line motion or turn manoeuvres, respectively. To merge the benefits of both models in one filter, the Interacting Multiple Model (IMM) framework of [6] was adopted, which showed good performance for vessel tracking in [7] and [8]. By monitoring the innovation of the filter, hypotheses tests can be applied to detect abnormal system behaviour.…”

Detection, tracking and fusion of multiple HFSW radars for ship traffic surveillance: Experimental performance assessment

“…Special attention is paid also to the output of the 3D (range, azimuth, range-rate) Ordered Statistics Constant False Alarm Rate (OS-CFAR) algorithm [3]. See [1,2] and references therein for further information about the experiment setup and the proposed algorithms. The Dopplercoupling effect of CW signals, responsible of the range shifts in the detections, was addressed in [4].…”

“…In this paper, the tracking algorithm behavior is investigated by comparing the tracks generated by two HFSW radars, with overlapping fields of view, and the ship reports from the Automatic Identification System (AIS). Furthermore, the single-sensor JPDA-UKF is followed by a track-to-track association and fusion (T2T-A/F) logic, [2]. Single-sensor versus multi-sensor tracking performance are investigated using real data, collected during the Battlespace Preparation 2009 (BP09) HF-radar experiment, which took place between May and December 2009 in the Ligurian Sea, Italy.…”

Validation of Radar Image Tracking Algorithms with Simulated Data

“…The dominating source for traffic situation assessment in the maritime domain has been and will be the marine radar, which is still the primary sensor for collision avoidance. Various approaches have been published in the literature to augment maritime surveillance or collision avoidance systems, mostly based on radar fusion with additional sensors such as laser in Perera, Ferrari, Santos, Hinostroza, and Soares (2015) or multiple stationary radar systems for exploiting aspect angle diversity as in Braca, Vespe, Maresca, and Horstmann (2012). The matter of AIS and radar fusion was mainly addressed for anomaly detection, e.g., based on multi hypothesis tests in Guerriero, Willett, Coraluppi, and Carthel (2008) or by exploiting historical traffic route knowledge for SAR/AIS fusion in Mazzarella and Vespe (2015).…”