Zhen Ding

Track quality based multitarget tracking algorithm

In multitarget tracking alongside the problem of measurement to track association, there are decision problems related to track confirmation and termination. In general, such decisions are taken based on the total number of measurement associations, length of no association sequence, total lifetime of the track in question. For a better utilization of available information, confidence of the tracker on a particular track can be used. This quantity can be computed from the measurement-to-track association likelihoods corresponding to the particular track, target detection probability for the sensor-target geometry and false alarm density. In this work we propose a multitarget tracker based on a track quality measure which uses assignment based data association algorithm. The derivation of the track quality is provided. It can be noted that in this case one needs to consider different detection events than that of the track quality measures available in the literature for probabilistic data association (PDA) based trackers. Based on their quality and length of no association sequence tracks are divided into three sets, which are updated separately. The results show that discriminating tracks on the basis of their track quality can lead to longer track life while decreasing the average false track length.

Track Quality Based Multitarget Tracking Approach for Global Nearest-Neighbor Association

In multitarget tracking, in addition to the problem of measurement-to-track association, there are decision problems related to track confirmation and termination. In general, such decisions are taken based on the total number of measurement associations, length of no association sequence, and total lifetime of the track in question. For a better utilization of available information, confidence of the tracker on a particular track can be used. This quantity can be computed using the measurement-to-track association likelihoods corresponding to the particular track, target detection probability for the sensor-target geometry, and false alarm density. A track quality measure is proposed here for assignment-based global nearest neighbor (GNN) trackers. It can be noted that to compute track quality measure for assignment-based data association one needs to consider different detection events than those considered for computation of the track quality measures available in the literature, which are designed for probabilistic data association (PDA) based trackers. In addition to the proposed track quality measure, a multitarget tracker based on it is developed, which is particularly suitable in scenarios with temporarily undetectable targets. In this work, tracks are divided into three sets based on their quality and measurement association history: initial tracks, confirmed tracks, and unobservable tracks. Details of the update procedures of the three track sets are provided. The results show that discriminating tracks on the basis of their track quality can lead to longer track life while decreasing the average false track length.

Multisensor-multitarget tracking testbed

In this paper we present a multisensor-multitarget tracking testbed for large-scale distributed scenarios. The objective is to develop a testbed capable of handling multiple, heterogeneous sensors in a hierarchical architecture for maritime surveillance. The testbed consists of a scenario generator that can generate simulated data from multiple sensors including radar, sonar, IR and ESM as well as a tracker framework into which different tracking algorithms can be integrated. In the current stage of the project, the IMM/Assignment tracker, and the Particle Filter (PF) tracker are implemented in a distributed architecture and some preliminary results are obtained. Other trackers like the Multiple Hypothesis Tracker (MHT) are also planned for the future.

Towards a Cognitive Radar: Canada’s Third-Generation High Frequency Surface Wave Radar (HFSWR) for Surveillance of the 200 Nautical Mile Exclusive Economic Zone

Canada’s third-generation HFSWR forms the foundation of a maritime domain awareness system that provides enforcement agencies with real-time persistent surveillance out to and beyond the 200 nautical mile exclusive economic zone (EEZ). Cognitive sense-and-adapt technology and dynamic spectrum management ensures robust and resilient operation in the highly congested High Frequency (HF) band. Dynamic spectrum access enables the system to simultaneously operate on two frequencies on a non-interference and non-protected basis, without impacting other spectrum users. Sense-and-adapt technologies ensure that the system instantaneously switches to a new vacant channel on the detection of another user or unwanted jamming signal. Adaptive signal processing techniques mitigate against electrical noise, interference and clutter. Sense-and-adapt techniques applied at the detector and tracker stages maximize the probability of track initiation whilst minimizing the probability of false or otherwise erroneous track data.

Network-Centric Multisensor-Multitarget Tracking Testbed Based on Peer-to-Peer Communication

In this paper we present a multisensor-multitarget tracking testbed for large-scale distributed scenarios. The objective is to develop a testbed capable of handling multiple, heterogeneous sensors in a hierarchical architecture for maritime surveillance. The testbed consists of a scenario generator that can generate simulated data from multiple sensors including radar, sonar, IR and ESM as well as a tracker framework into which different tracking algorithms can be integrated. In the current stage of the project, the IMM/assignment tracker, and the particle filter (PF) tracker are implemented in a distributed architecture and some preliminary results are obtained. Other trackers like the multiple hypothesis tracker (MHT) are also planned for the future

A distributed multisensor-multitarget tracking testbed for maritime surveillance

In this paper we present the development of a multisensor-multitarget tracking testbed for large-scale distributed (or network-centric) scenarios. The project, which is in progress at McMaster University and the Royal Military College of Canada, is supported by the Department of National Defence and Raytheon Canada. The objective is to develop a testbed capable of handling multiple, heterogeneous sensors in a hierarchical architecture for maritime surveillance. The testbed consists of a scenario generator that can generate simulated data from multiple sensors including radar, sonar, IR and ESM as well as a tracker framework into which different tracking algorithms can be integrated. In the first stage of the project, the IMM/Assignment tracker, and the Particle Filter (PF) tracker are implemented in a distributed architecture and some preliminary results are obtained. Other trackers like the Multiple Hypothesis Tracker (MHT) are also planned for the future.

Overview of RRM Techniques

This chapter provides a preliminary survey of the multifunction phased array radar resource management algorithms. The survey summarizes important papers to illustrate existing algorithms for the radar resource optimization problem. The algorithms are categorized into six categories, where the first three categories are adaptive scheduling algorithms and the remaining categories are resource-aided algorithms. The resource-aided algorithms are relevant since a better algorithm needs fewer resources to achieve the same performance.

Radar Resource Management for Networked Radars

This chapter proposes coordinated radar resource management (RRM), which exploits the sharing of tracking and detection data between radars and compares its performance with that of Independent RRM. Two types of coordinated RRM using distributed management techniques are proposed, with each type characterized by varying amounts of coordination between the radars. In the simulation tool Adapt_MFR, a 2-radar network and 30-target scenario are modeled, and the performance of the two Coordinated RRM techniques are compared with that of Independent RRM. Results show that Coordinated RRM techniques achieve the same track completeness as Independent RRM, while decreasing track occupancy and frame time.

Adaptive Scheduling Techniques

This chapter presents two techniques for the adaptive scheduling of a multifunction radar. The optimal assignment scheduler (OAS) is a noninterleaving technique that schedules beams to minimize the maximum delay and accumulated delay for tracking tasks. Performance is compared to the time-balancing scheduler. The two-slope benefit function scheduler is also presented. This technique schedules looks to maximize the total benefit, and it is shown that the resulting maximization is equivalent to a linear program which can be solved efficiently using the simplex method. Scheduler performance is compared to that of the Orman scheduler.

Comparison of Adaptive and Nonadaptive Techniques

This chapter compares the performance of an adaptive radar resource management (RRM) technique to that of a nonadaptive technique, using modeling and simulation. Performance metrics are presented, followed by the simulation tool Adapt_MFR that is used in the performance study. The adaptive RRM technique is described, and the details of its adaptive prioritization, scheduling, and track update intervals are quantified. Finally, we present the scenario under consideration and the results of the performance comparison.