This talk goes through the components in generic single-scan target tracking algorithms from filtering to data association, track initiation, and termination. In many areas, reference is made to functions in the open-source copyleft-free Tracker Component Library (available online) so that attendees can rapidly apply the algorithms that are discussed.
Both maximum likelihood estimation as well as minimum mean optimal subpattern assignment (MMOSPA)
estimation have been shown to provide meaningful estimates in instances of target identity uncertainty when the
number of targets present is known. Maximum likelihood measurement to track association (2D assignment) has
been widely studied and is reviewed in this paper. However, it is widely believed that approximate MMOSPA
estimation can not be performed in real time except when considering a very small number of targets. This paper
demonstrates the MMOSPA estimator arises as a special case of a minimum mean Wasserstein metric estimator
when the number of targets is unknown. Additionally, it is shown that approximate MMOSPA estimates can
be calculated in microseconds to miliseconds without extensive optimization, making MMOSPA estimation a
practicable alternative to more traditional estimators.
KEYWORDS: Detection and tracking algorithms, Target detection, Monte Carlo methods, Error analysis, Algorithm development, Computer simulations, Statistical analysis, Time metrology, Signal processing, Data processing
In this paper we present a new algorithm for approximating the target-measurement association
probabilities of the Joint Probabilistic Data Association Filter (JPDAF). This algorithm is designed to
robustify the JPDAF against track coalescence which can greatly degrade the performance of the JPDAF
and other approximate algorithms. It is based on the works of Roecker and the JPDAF* of Blom and
Bloem. We compare our new algorithm with the two it is based on, as well as the "cheap JPDAF" and
the Set JPDAF, and show that it offers a significant improvement in computational complexity over the
JPDAF*, and improvement in tracking error over the Roecker algorithm. We compare their performance
with respect to the Mean Optimal Subpattern Assignment (MOSPA) statistic in scenarios involving several
closely-spaced targets. A consistency comparison of the various algorithms considered is also presented.
KEYWORDS: Error analysis, Radar, Particles, Matrices, Data analysis, Particle filters, Computer engineering, Signal processing, Data processing, Current controlled current source
We define the notion of an "identity variance" for expressing the level of uncertainty between target identities
in a PDF representing the states of multiple targets. This, coupled with an OSPA covariance introduced in
past work, can form a basis for evaluating both the accuracy of the state estimates as well as the confidence
in the identities of the states. A potential application of the identity variance is as a criterion for choosing
between waveforms optimized for producing accurate position estimates versus good classification information
on a multifunction radar.
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.
KEYWORDS: Detection and tracking algorithms, Target detection, Monte Carlo methods, Time metrology, Computer simulations, Error analysis, Computing systems, Algorithm development, Signal processing, Data processing
In this paper we look at various algorithms for approximating the target-measurement association probabilities
of the Joint Probabilistic Data Association Filter (JPDAF). We consider their computational complexity and
compare their performance with respect to the Mean Optimal Subpattern Assignment (MOSPA) statistic in a
scenario involving closely-spaced targets.
This paper considers the problem of dynamic residual bias estimation in the presence of measurement association uncertainty
using common targets of opportunity under a decentralized information processing architecture i.e. independent
trackers at each sensor. This is done by extending the scope of the synchronous version of the bias estimation algorithm
presented by Lin, Bar-Shalom and Kirubarajan in "Multisensor-Multitarget Bias Estimation for General Asynchronous
Sensors" to develop approaches to bias estimation in the presence of measurement association uncertainty. We consider
the computational complexity and the sensor-to-fusion-center communication requirements of each of these approaches
and compare their simulated performance in terms of RMSE and consistency. Though the simulations are performed with
synchronous polar measurements having additive biases, the algorithm may easily be extended to the case with asynchronous
measurements in other coordinate systems having both additive and multiplicative biases.
We present a target tracking system for a specific sort of passive radar, that using a Digital Audio/Video
Broadcast (DAB/DVB) network for illuminators of opportunity. The system can measure bi-static range and
range-rate. Angular information is assumed here unavailable. The DAB/DVB network operates in a single
frequency mode; this means the same data stream is broadcast from multiple senders in the same frequency
band. This supplies multiple measurements of each target using just one receiver, but introduces an additional
ambiguity, as the signals from each sender are indistinguishable. This leads to a significant data association
problem: as well as the usual target/measurement uncertainty there is additional "list" of illuminators that must
be contended with.
Our intention is to provide tracks directly in the geographic space, as opposed to a two-step procedure of
formation of tracks in (bi-static) range and range-rate space to fuse these onto a map. We offer two solutions:
one employing joint probabilistic data association (JPDA) based on an Extended Kalman Filter (EKF), and the
other a particle filter. For the former, we explain a "super-target" approach to bring what might otherwise be
a three-dimensional assignment list down to the two dimensions the JPDAF needs. The latter approach would
seem prohibitive in computation even with these; as such, we discuss the use of a PMHT-like measurement model
that greatly reduces the numerical load.
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