This paper presents the theory of a new multiple model probabilistic data association filter (PDAF). The analysis is generalized for the case of multiple nonuniform clutter regions within the measurement data that updates each model of the filter. To reduce the possibility of clutter measurements forming established tracks, the solution includes a model for a visible target. That is, a target that gives sensor measurements that satisfy one of the target models. Other features included in the algorithm are the selection of a fixed number of nearest measurements and the addition of signal amplitude to the target state vector. The nonuniform clutter model developed here is applicable to tracking signal amplitude. Performance of this algorithm is illustrated using experimentally recorded over-the-horizon radar (OTHR) data.

PDAF with multiple clutter regions and target models

Unlike other tracking algorithms the probabilistic multi-hypothesis tracker (PMHT) assumes that the true source of each measurement is an independent realisation of a random process. Given knowledge of the prior probability of this assignment variable, data association is performed independently for each measurement. When the assignment prior is unknown, it can be estimated provided that it is either time independent, or fixed over the batch. This paper presents a new extension of the PMHT, which incorporates a randomly evolving Bayesian hyperparameter for the assignment process. This extension is referred to as the PMHT with hysteresis. The state of the hyperparameter reflects each model's contribution to the mixture, and thus can be used to quantify the significance of mixture components. The paper demonstrates how this can be used as a method for automated track maintenance in clutter. The performance benefit gained over the standard PMHT is demonstrated using simulations and real sensor data

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Integrated track maintenance for the PMHT via the hysteresis model

HF radar is an important tool to remotely measure oceanographic parameters like currents and waves from shore over large areas up to 6500 km/sup 2/. Maps of these parameters can be obtained 3 times per hour. In areas of high ship traffic, there are ship echoes within the radar backscatter signal, which can appear in the frequency bands of the Doppler spectrum which are used to extract current- and wave information. While the current speed is derived from the first-order Bragg peaks, wave height and direction is calculated from the second-order bands around the first-order peaks. The second-order bands require 15 to 20 times the bandwidth of the first-order peaks and their amplitude is about 20 dB lower. Due to these facts, ship echoes can cause problems especially to wave algorithms. This paper discusses the special signatures of ship echoes and shows possibilities to identify and to track ships.

HF radar wave measurements in the presence of ship echoes - problems and solutions

Track coalescence is undesirable when estimating the states of multiple manoeuvring targets, with a less-than-unity probability of detection, in clutter. Simple and compound forms of coalescence are defined and discussed. Simple coalescence is when two or more identical tracks follow a single target; compound coalescence is when two or more identical tracks follow the midpoint (or centroid) of two or more targets. It is shown that the incidence of compound track coalescence in joint probabilistic data association (JPDA) may be reduced using a scaling factor to favour the most likely association hypothesis. This prevents multiple hypothesis equivalence when tracking closely-spaced or crossing targets. The performance of the scaled JPDA (SJPDA) algorithm is compared with probabilistic data association (PDA) and JPDA using real and simulated data. Larger scaling factors decrease the likelihood and duration of compound track coalescence; however, they also increase the likelihood of track divergence on clutter or other targets. A value of unity corresponds to JPDA. The optimal value may be chosen to suit the application. A factor of two was found to give good results in the test data.

Controlling track coalescence with scaled Joint Probabilistic Data Association

This paper defines an approach to evaluate automatic target tracking system performance against 15 metrics. These metrics are grouped in the categories of track establishment, track maintenance, track error and false tracks. Performance for each metric is expressed as a probability and these are weighted and combined to give an overall measure of performance. The data for this assessment process comes from comparing the tracking system output with tracks in a truth file. This comparison produces a results file for each tracking system and data set. The results file is combined over multiple data sets to form a single file for each tracking system which is the source data to assess tracking performance. This approach is embodied in a Tracker Assessment Tool (TAT) which is described with an example of using it to compare alternative Over-the-Horizon Radar (OTHR) tracking systems.

/pdf/tracking-system-performance-assessment-60005ip0ba.pdf

Tracking system performance assessment

: This paper presents the theory and examples of performance for a new algorithm that initiates tracks using a multiple model Probabilistic Data Association (PDA) filter. The analysis is generalized for the case of multiple non-uniform clutter regions within the measurement data that updates the filter. The algorithm starts multiple parallel PDA filters from a single sensor measurement. Each filter is assigned one of a range of possible target model parameters. To reduce the possibility of clutter measurements forming established tracks, the solution includes a model for a visible target. That is, a target that gives sensor measurements that satisfy one of the target models. Other features included in the algorithm are the selection of a fixed number of nearest measurements and the addition of signal amplitude to the target state vector. The inclusion of signal amplitude is one coordinate that is applicable to the non-uniform clutter model developed in this a paper.

S. B. Colegrove

Papers

Advanced Jindalee Tracker: Probabilistic Data Association Multiple Model Initiation Filter