The probability hypothesis density (PHD) filter is an estimator that approximates, on a given scenario, the
multitarget distribution through its first-order multitarget moment. This paper presents two particles labeling
algorithms for the PHD particle filter, through which the information on individual targets identity (otherwise
hidden within the first-order multitarget moment) is revealed and propagated over time. By maintaining all
particles labeled at any time, the individual target distribution estimates are obtained under the form of labeled
particle clouds, within the estimated PHD. The partitioning of the PHD into distinct clouds, through labeling,
provides over time information on confirmed tracks identity, tracks undergoing initiation or deletion at a given
time frame, and clutter regions, otherwise not available in a regular PHD (or track-labeled PHD). Both algorithms
imply particles tagging since their inception, in the measurements sampling step, and their re-tagging once they
are merged into particle clouds of already confirmed tracks, or are merged for the purpose of initializing new
tracks. Particles of a confirmed track cloud preserve their labels over time frames. Two data associations
are involved in labels management; one assignment merges measurement clouds into particle clouds of already
confirmed tracks, while the following 2D-assignment associates particle clouds corresponding to non-confirmed
tracks over two frames, for track initiation. The algorithms are presented on a scenario containing two targets
with close and crossing trajectories, with the particle labeled PHD filter tracking under measurement origin
uncertainty due to observations variance and clutter.
KEYWORDS: Sensors, Error analysis, Statistical analysis, Time metrology, Target detection, Information fusion, Sensor fusion, Stars, Data fusion, Binary data
With current processing power, Multiple Hypothesis Tracking (MHT) becomes a feasible and powerful solution;
however a good hypothesis pruning method is mandatory for efficient implementation. The availability of a continuously
increasing number of tracking systems raises interest in combining information from these systems. The purpose of this
paper is to propose a method of information fusion for such trackers that use MHT locally with local information sent in
the form of sensor global hypotheses and the fusion center combining them into fused global hypotheses. The
information extracted from the best fused global hypotheses, in the form of ranking of received sensor global
hypotheses, is sent back to local trackers, for optimized pruning. Details of the method, in terms of sensor global
hypotheses generation, evaluation, pruning at local sensors, association and fusion of sensor global hypotheses at fusion
center, and usage of the information received as feedback from the fusion center are presented.
KEYWORDS: Sensors, Statistical analysis, Switching, Error analysis, Information fusion, Monte Carlo methods, Fourier transforms, Switches, Data processing, Astatine
In a single-frame track-to-track association, due to the local sensors track swapping (switching of the track from an
estimated target to another estimated target, under measurement uncertainty conditions), the identities of the fused tracks
over several frames are not preserved. The main goal of the proposed track-to-track association method is to link the
histories of fused tracks over several frames and avoid track swapping at the fusion center level (e.g. to preserve the
continuity of the fused tracks through their identities). In this method, the previous association hypotheses are taken as
priors in a multiple-hypothesis association chain. The continuity of the fused tracks over several frames is achieved
through the prediction of the fused tracks obtained from a set of best association hypotheses at each frame. Through this,
if in computing the fused tracks estimation errors, their identities are taken into account (e.g. the errors of a fused track
over all the frames are computed with respect to the same true target), this procedure will improve also the fused track
state estimation error. The method and implementation proposed is intended to be used to identify the histories of two or
more tracks at the fusion center, and possibly to improve the track-to-track association.
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