In this paper, several nonlinear filters (EKF/CMKF/CMEKF, UKF and PFs) are compared using real datasets
and simulations based on two representative radar datasets. The first dataset was collected from an air traffic
control (ATC) radar experiment with several aircraft. The second dataset was recorded from a high frequency
surface wave radar (HFSWR) trial that was characterzed by a very long integration time and a limited set of
manoeuvre types. RMSE, NEES and NIS are used as measures of performance. Comments on the performance,
computational requirements of the nonlinear filters, practical modelling and filter tuning issues for the two types
of radars are also presented.
In this paper, we present two practical modifications of the original Murty algorithm. First, the algorithm is modified to handle rectangular association matrix. The original Murty algorithm was developed for a square matrix. It is found that the expanding rules should be changed so that the cross-over pair within an assignment can be extended to the last column and can be repeated for the last column upon certain conditions. The second modification is the allowance of an "infeasible" assignment, where some tracks are not assigned with any measurements, therefore, good "infeasible" hypotheses are maintained and clutter seduced hypotheses are suppressed when the information evidence becomes stronger. Examples are used to demonstrate the modifications of the existing Murty algorithm for a practical implementation of an N-best Multiple Hypothesis Tracker.
KEYWORDS: Detection and tracking algorithms, Target detection, Quality measurement, Logic, Sensors, Personal digital assistants, Monte Carlo methods, Data modeling, Time metrology, Error analysis
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.
KEYWORDS: Sensors, Data fusion, Detection and tracking algorithms, Environmental sensing, Data centers, Process modeling, Radar, Algorithm development, Surveillance, Kinematics
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.
This paper describes a Wide Area Integrated Maritime Surveillance (WAIMS) system based on High Frequency Surface Wave Radar (HFSWR) for monitoring surface and low-level air activity within the 200 nm Exclusive Economic Zone (EEZ). The system is designed to help authorities to locate and identify illegal activities. In particular, we investigate the bias problem from a nonlinear latitude and longitude transformation. The geometric relationship between a sensor and a target position is formulated. Then using Taylor series, an analysis of the nonlinear transformation bias is done with comparison of calculated and simulated results.
In this paper, six trackers are reviewed and their performance is compared. Real radar target data are used for this study, where the data were collected from commercial and military aircrafts in various conditions. Since the true target trajectors are unavailable, the prediction RMS error is used as the performance criterion. The six trackers are compared in terms of their performance and computation complexity as well. Evaluated results shows that non-model trackers generally outperform model based trackers. A brief discussion is given.
KEYWORDS: Detection and tracking algorithms, Personal digital assistants, Algorithm development, Sensors, Monte Carlo methods, Optical engineering, Data communications, Data modeling, Data fusion, Statistical analysis
KEYWORDS: Detection and tracking algorithms, Sensors, Algorithm development, Data communications, Data modeling, Data fusion, Motion models, Electronic filtering, Systems modeling, Kinematics
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