The Histogram Probabilistic Multi-Hypothesis Tracker (H-PMHT) is a parametric track-before-detect algorithm that has
been shown to give good performance at a relatively low computation cost. Recent research has extended the algorithm
to allow it to estimate the signature of targets in the sensor image. This paper shows how this approach can be adapted to
address the problem of group target tracking where the motion of several targets is correlated. The group structure is treated
as the target signature, resulting in a two-tiered estimator for the group bulk-state and group element relative position.
Conventional tracking algorithms rely on the assumption that the targets under observation are point source
objects. However, due to increasing resolution capabilities of modern sensors, the point source assumption is
often not suitable and estimating the target extension becomes a crucial aspect. Recently, a Bayesian approach
to extended target tracking using random matrices has been proposed. Within this approach, ellipsoidal object
extensions are modeled by random matrices and treated as additional state variables to be estimated. However,
only a single-target solution has been presented so far. In this work we present the multi-target extension of
this approach. We derive a new variant of Probabilistic Multi-Hypothesis Tracking (PMHT) that simultaneously
estimates the ellipsoidal shape and the kinematics of each target. For this purpose, the PMHT auxiliary function
is extended by random matrices representing the target ellipsoids. Both, the ellipsoids and the kinematic states
are iteratively optimized by specific Kalman filter formulae arising directly from the auxiliary function. The new
method is demonstrated and evaluated by simulative examples.
KEYWORDS: Chemical fiber sensors, Sensors, Computer security, Surveillance, Chemical analysis, Detection and tracking algorithms, Radar, Distance measurement, Information security, Kinematics
Timely recognition of threats can be significantly supported by security assistance systems that work continuously
in time and call the attention of the security personnel in case of anomalies. We describe the concept and
the realization of an indoor security assistance system for real-time decision support. Data for the classification
of persons are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal
resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate
this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from
laser-range-scanners. Both, tracking and fusion of tracks with chemical attributes can be processed within one
single framework called Probabilistic Multiple Hypothesis Tracking (PMHT). An extension of PMHT for dealing
with classification measurements (PMHT-c) already exists. We show how PMHT-c can be applied to associate
chemical attributes to person tracks. This affords the localization of threads and a timely notification of the
security personnel.
Tracking multiple targets in a cluttered environment is a challenging task. Probabilistic Multiple Hypothesis
Tracking (PMHT) is an efficient approach for dealing with it. Essentially PMHT is based on the method of
Expectation-Maximization for handling with association conflicts. Linearity in the number of targets and measurements
is the main motivation for a further development and extension of this methodology. Unfortunately,
compared with the Probabilistic Data Association Filter (PDAF), PMHT has not yet shown its superiority in
terms of track-lost statistics. Furthermore, the problem of track extraction and deletion is apparently not yet
satisfactorily solved within this framework. Four properties of PMHT are responsible for its problems in track
maintenance: Non-Adaptivity, Hospitality, Narcissism and Local Maxima.1, 2 In this work we present a solution
for each of them and derive an improved PMHT by integrating the solutions into the PMHT formalism. The
new PMHT is evaluated by Monte-Carlo simulations. A sequential Likelihood-Ratio (LR) test for track extraction
has been developed and already integrated into the framework of traditional Bayesian Multiple Hypothesis
Tracking.3 As a multi-scan approach, also the PMHT methodology has the potential for track extraction. In
this paper an analogous integration of a sequential LR test into the PMHT framework is proposed. We present
an LR formula for track extraction and deletion using the PMHT update formulae. As PMHT provides all
required ingredients for a sequential LR calculation, the LR is thus a by-product of the PMHT iteration process.
Therefore the resulting update formula for the sequential LR test affords the development of Track-Before-Detect
algorithms for PMHT. The approach is illustrated by a simple example.
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