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In this paper, a plain data-driven and simulation-based approach to object tracking is investigated. The basic idea is to use the probabilistic model of the tracking problem to simulate a large amount of state and observation sequences. Both are fed into a regression algorithm that learns a mapping from the observations to the states. In particular, we consider random forest regression and apply it...
We present a particle filter for multi-object tracking that is based on the ideas of the Approximate Bayesian Computation (ABC) paradigm. The main idea is to avoid the explicit computation of the likelihood function by means of simulation. For this purpose, a large amount of particles in the state space is simulated from the prior, transformed into measurement space, and then compared to the real...
Assessing the fundamental performance limitations in Bayesian filtering can be carried out using the parametric Cramér-Rao bound (CRB). The parametric CRB puts a lower bound on mean square error (MSE) matrix conditioned on a specific state trajectory realization. In this work, we derive the parametric CRB for state-space models, where the measurement equation is modeled by a Gaussian process regression...
This paper investigates the box-particle filter for multi-target tracking, and proposes a clustering based box-particle implementation of PHD filter. A subdivision step is added before the estimation of states. Each box is divided into several sub-box based on the estimated number of targets. An equivalent set of particles can be extracted from the set of subdivided boxes. Then, clustering technique...
Cold atom interferometer is a promising technology to obtain a highly sensitive and accurate absolute gravimeter. With the help of an anomalies gravity map, local measurements of gravity allow a terrain-based navigation. We describe the model of the absolute gravity measurement. We develop a Laplace-based particle filter adapted to this context. This non-linear filter is able to estimate the positions...
This work documents our investigation of multiple target tracking filters in proximity sensor networks when the target power levels are not known. The challenge is that when the targets are close, it is hard to determine if the sensor reports are the results of a loud target or multiple quiet targets. Given the binary measurements:1 for detection of targets and 0 for nondetection of targets, the works...
A loosely coupled INS/GPS integrated navigation system is a nonlinear dynamic system. A particle filter (PF) is a particular tool for the nonlinear and non-Gaussian problems. However typical bootstrap particle filters (BPFs) cannot solve the mismatch between the importance function and the likelihood function very well so that they are invalid to some extent in the application of the INS/GPS integrated...
We propose a deterministic recursive algorithm for approximate Bayesian filtering. The proposed filter uses a function referred to as the approximate Gaussian flow transformation that transforms a Gaussian prior random variable into an approximate posterior random variable. Given a Gaussian filter prediction distribution, the succeeding filter prediction is approximated as Gaussian by applying sigma...
We present a novel joint detection and tracking algorithm using raw measurements, in a compressed sensing framework. The sparse vector representing the state space is directly reconstructed, which transforms the nonlinear estimation problem into a linear one through sparse representation. A number of significant grids are obtained based on the sparse vector, indicating the positions of multiple potential...
Particle filters are a widely used tool to perform Bayesian filtering under nonlinear dynamic and measurement models or non-Gaussian distributions. However, the performance of particle filters plummets when dealing with high-dimensional state spaces. In this paper, we propose a method that makes use of multiple particle filtering to circumvent this difficulty. Multiple particle filters partition the...
Uncertainty measures in evidence theory can supply a new criterion to rate the quality of information carried by belie structures. It can also be used to measure the quantity of knowledge conveyed by belief structures. Following the work of Klir and Yuan, several uncertainty measures for belief structures have been developed. Among them, aggregate uncertainty AU, the total uncertainty TU and the ambiguity...
Traffic control and vehicle route planning require accurate estimates of the traffic state in order to be successfully implemented. This estimation problem can be solved by using particle filters in conjunction with macroscopic traffic models such as the stochastic compositional model. The accuracy of the estimates can be decreased for road segments where there are no measurements available. However,...
This paper proposes an improvement to FastSLAM. The approach is applicable when the dynamic model describing the motion of the camera has linear sub-structure. The core novelty of the proposed algorithm is to separate the consideration of the camera's dynamic model into two sub-models without constraining the two sub-models to have independent noise processes. In contrast to commonly-used FastSLAM...
Particle Filter (PF) is a popular sequential Monte Carlo method to deal with non-linear non-Gaussian filtering problems. However, it suffers from the so-called curse of dimensionality in the sense that the required number of particle (needed for a reasonable performance) grows exponentially with the dimension of the system. One of the techniques found in the literature to tackle this is to split the...
Our newly proposed approach to extended object tracking (EOT) using extension deformation is simple and effective. This approach assumes that the extension of an object is deformed from an ellipsoidal reference extension, which unfortunately restricts its use for complex extensions. To overcome this weakness, this paper proposes that the current object extension be modeled as deformed from the one...
The probability hypothesis density (PHD) filter is a promising filter for multi-target tracking which propagates the posterior intensity of the multi-target state. In this paper, a Gaussian mixture particle flow PHD (GMPF-PHD) filter is proposed which uses a bank of particles to represent the Gaussian components in the Gaussian mixture PHD (GM-PHD) filter. Then a particle flow is implemented to migrate...
Particle filters using Gmapping proposal distribution has demonstrated their effectiveness in target tracking and robot self-localization. Due to the number of particles required in this approach, the computational demand is an issue associated with the Gmapping proposal distribution. The traditional approach is often ad hoc by setting a threshold for acceptance/rejection sampling to reduce the number...
Emission source localization and sensor registration using received signal strength (RSS) measurements is investigated. Previous studies for RSS localization assume that the sensors receiving signals are bias free, which is not the case in practice. This issue is taken into consideration in this paper for the localization problem. To avoid non-convexity of the global optimization problem for the traditional...
The paper deals with the state estimation of nonlinear stochastic dynamic systems. The stress is laid on the assessment of the estimate error, which is caused by the violation of the estimator design assumptions. The assessment is based on measures comparing estimators actual working conditions and the assumptions under which the estimators have been proposed. In particular, the measures of nonlinearity...
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