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A crucial point in the decision-level identity fusion is to combine information in an appropriate way to generate an optimal decision, according to the individual information coming from a set of different sensors. An interesting approach was developed for the decision- level identity fusion, which use optimization techniques to minimize an objective function which measure the dissimilarities between...
Works have investigated the problem of the conflict redistribution in the fusion rules of evidence theories. As a consequence of these works, many new rules have been proposed. Now, there is not a clear theoretical criterion for a choice of a rule instead another. The present paper proposes a new theoretically grounded rule, based on a new concept of sensor independence. This new rule avoids the conflict...
Probabilistic multi-hypothesis tracking (PMHT) is an algorithm for tracking multiple targets when measurement-to- target assignments are unknown and must be jointly estimated with the target tracks. Multi-frame assignment PMHT (MF- PMHT) is an algorithm designed to mitigate some performance problems associated with PMHT. In MF-PMHT, the PMHT algorithm is applied to multi-frame sequences in the last...
This paper is concerned with performance prediction of multiple target tracking system. Effects of misassociation are considered in a simple (linear) framework so as to provide closed-form expressions of the probability of correct association. In this paper, we focus on the development of explicit approximations of this probability for a unique false measurements. Rigorous calculations allow us to...
PMHT algorithm, as proposed, promises high performance multi target tracking in clutter with (relatively) modest computational resources. However, when applied to practical target tracking situations, a number of problems need to be overcome. PMHT assumes fixed number of tracks, and furthermore it assumes that all tracks are true tracks. No track quality measure is provided within PMHT to enable false...
Multiple ground targets tracking with a GMTI (ground moving target indicator) sensor is considered a challenging problem in order to establish battlefield assessment. An IMM algorithm with a variable structure is adapted to the road network and used to track multiple manoeuvring ground targets. However, the case of undetected targets due to terrain elevation or Doppler obscuration was not taken into...
Tracking objects using multiple sensors is more efficient than those using one sensor. In this paper, we proposed a method to fuse data from multiple sensors in Gaussian mixture probability hypothesis density filter. This method can avoid the data association problem in multi-sensor multi-object tracking. Moreover, it is more reliable and less computational than particle probability hypothesis density...
A cooperative team's performance strongly depends on the view that the team has of the environment in which it operates. In a team with many autonomous vehicles and many sensors, there is a large volume of information available from which to create that view. However, typically communication bandwidth limitations prevent all sensor readings being shared with all other team members. This paper presents...
The interacting multiple model (IMM) algorithm is a widely accepted state estimation scheme for solving maneuvering target tracking problems, which are generally nonlinear. During the IMM filtering process, serious errors can arise when a Gaussian mixture of posterior probability density functions is approximated by a single Gaussian. Particle filters (PFs) are effective in dealing with nonlinearity...
An algorithm is developed for joint tracking and detection of multiple maneuvering targets using a wireless sensor network. The target existence probability framework is adopted in which a collection of tentative tracks, each characterised by a posterior density and existence probability, is maintained. Track state posterior densities are approximated using the unscented Kalman filter and the interacting...
This paper details and deepens a previous work where the Interpreted Systems semantics was proposed as a general framework for situation analysis (SA). This framework is particularly efficient for representing and reasoning about knowledge and uncertainty when performing situation analysis tasks. Our approach of SA is to base our analysis on the production of state transition systems consisting in...
The construction of belief networks is a widely used methodology for high level fusion modeling. While some of the components of a belief network deal with ambiguous (probabilistic) data, others may deal with vague (possibilistic) data. Given the need to represent both probabilistic and possibilistic components in a single belief network, a framework and toolset for building Hybrid networks, utilizing...
The probability hypothesis density (PHD) filter, which was derived from finite set statistics is a promising approach to multi-target tracking. An analytical closed-form solution for the PHD, named Gaussian mixture PHD Filter, is given for linear Gaussian target dynamics with Gaussian births by B. Vo and W. Ma. Based on the Gaussian mixture PHD filter, in this paper, without consideration of data...
Evidential-reasoning methods, such as the Dempster-Shafer calculus of evidence, are widely applied to information fusion problems. In this paper we examine methodological requirements as well as conceptual issues that are relevant to their understanding and applicability. These matters include interpretation of its basic constructs and that of the notion of evidential independence, the characterization...
The cardinalized probability hypothesis density (CPHD) filter is a recursive Bayesian algorithm for estimating multiple target states with varying target number in clutter. In particular, the Gaussian mixture variant (GMCPHD) for linear, Gaussian systems is a candidate for real time multi target tracking. The present work addresses the following three issues: (i) we show the equivalence between the...
This paper defines and implements a non-Bayesian fusion rule for combining densities of probabilities estimated by local (non-linear) filters for tracking a moving target by passive sensors. This rule is the restriction to a strict probabilistic paradigm of the recent and efficient proportional conflict redistribution rule no 5 (PCR5) developed in the DSmT framework for fusing basic belief assignments...
An algorithm for detection and tracking of multiple targets using bearings measurements from several sensors is developed. The algorithm is an implementation of a multiple hypothesis tracker with pruning of unlikely hypotheses. Tracking conditional on each hypothesis can be performed using any suitable filtering approximation. In this paper a range- parameterized unscented Kalman filter is used. Each...
The idea of particle filter is to represent probability density function (PDF) of nonlinear/non-Gaussian system by a set of random samples. One of the key issue of particle filter is the proposal distribution. In this paper, the iterated unscented Kalman filter (IUKF) is used to generate the proposal distribution for particle filter. The proposal distributions integrate the current observation, thus...
In nonlinear Bayesian estimation it is generally inevitable to incorporate approximate descriptions of the exact estimation algorithm. There are two possible ways to involve approximations: Approximating the nonlinear stochastic system model or approximating the prior probability density function. The key idea of the introduced novel estimator called Hybrid Density Filter relies on approximating the...
Summary form only given. The problem of track-to-track association and track fusion has been considered in the literature where the local trackers assume the same target motion model and send their local state estimates to the fusion center on demand. Many issues arise when local trackers use different target motion models or even operate on different target state spaces. In this case, selecting an...
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