The Set JPDA (SJPDA) filter is a recently developed multi-target tracking filter that utilizes the relation
between the density of a random finite set and the ordinary density of a state vector to improve on the Joint
Probabilistic Data Association (JPDA) filter. One advantage to the filter is the improved accuracy of the Gaussian
approximations of the JPDA, which result in avoidance of track coalescence. In the original presentation of the
SJPDA filter, the focus was on problems where target identity is not relevant, and it was shown that the filter
performs better than the JPDA filter for such problems. The improved performance of the SJPDA is due to
its relaxation of the labeling constraint that hampers most tracking approaches. However, if track identity is
of interest a record of it may be kept even with a label-free approach such as the SJPDA: label-free targets are
localized via the SJPDA, and then the identities are recalled as an overlay.
KEYWORDS: Motion models, Electronic filtering, Detection and tracking algorithms, Filtering (signal processing), Promethium, Gaussian filters, Switches, Monte Carlo methods, Data modeling, 3D modeling
The Set JPDA (SJPDA) filter is a recently developed multi-target tracking filter that utilizes the relation
between the density of a random finite set and the ordinary density of a state vector to improve on the Joint
Probabilistic Data Association (JPDA) filter. One advantage to the filter is the improved accuracy of the
Gaussian approximations of the JPDA, which results in avoidance of track coalescence. Another advantage is an
improved estimation accuracy in terms of a measure which disregards target identity. In this paper we extend the
filter to also consider multiple motion models. As a basis for the extension we use the Interacting Multiple Model
(IMM) algorithm. We derive three alternative filters that we jointly refer to as Set IMMJPDA (SIMMJPDA).
They are based on two alternative descriptions of the IMMJPDA filter. In the paper, we also present simulation
results for a two-target tracking scenario, which show improved tracking performance for the Set IMMJPDA
filter when evaluated with a measure that disregards target identity.
In this paper we look at various options for calculating target-measurement association probabilities and updating
the state estimates in the Joint Probabilistic Data Association Filter (JPDAF). In addition to the "standard"
methods, we look at other methods that try to improve the estimation accuracy by coupling the states, discarding
certain joint association events, or by applying random finite set theory to change how the states are updated.
We compare the performance of trackers based on several of these concepts to each other and to the PMHT,
the MHT, and the GNN tracker. We also single out approaches that are "snake oil", in that they are either not
suited for practical use, or that their complexity is higher than that of calculating the probabilities exactly.
Additionally we show how the JPDAF* can be implemented to have a lower worst-case complexity than the
regular JPDAF when the number of targets and/or observations is large. We also review some oft overlooked
references on gating that are useful for implementations in real systems.
A parametric model for the infrared signature caused by a buried land mine is presented. Further, two ways of modeling the colored background noise, is proposed. In the first, it is assumed the noise can be approximated by an autoregressive process, while in the second, the statistics of the noise is described using recent development in texture modeling, the so called FRAME method. Given an a priori distribution of the mine parameters in combination with a trained noise distribution, a Bayesian detector is derived. Experiments indicate that significant gains in performance can be achieved as compared to the standard detector used, which correlates the infrared image with the known mine shape and thresholds the square of the output.
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