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Given a map of a polygon-shaped search area with obstacles and a group of mobile networked observers equipped with radiation dose counters (such as the Geiger-Muller counter), the data fusion problem is twofold: (1) to establish if any radioactive point sources are present in the area; (2) if present, to determine their number and their parameters (locations and intensities of radiation). The detection/estimation...
The reception state of a satellite is an unavailable information for Global Navigation Satellite System receivers. His knowledge or estimation can be used to evaluate the pseudorange error. This article deals with the problem using three reception states: direct reception, alternate reception and blocked situation. This parameter, estimated using a Dirichlet distribution, is included in a particle...
Fusion of a simulation model and observation data has been investigated extensively for the purpose of data assimilation in geophysics. The inaccuracy of the parameters, initial conditions, or boundary conditions causes a discrepancy in the simulation results and the actual phenomenon. The present paper describes the parameter identification of a pressure regulator with a nonlinear structure by sequential...
This paper is concerned with the application of target tracking in a network of sensors that provide binary output. The binary sensor network tracking problem is formulated in a sequential Bayesian estimation framework and is readily solved by means of a particle filter. We will perform sensor selection by means of a newly proposed scheme. This proposed approach is especially suitable for problems...
Indoor WLAN positioning should be modeled as a nonlinear and non-Gaussian dynamic system due to the complex indoor environment, radio propagation and motion behaviour. The aim of this paper is to analyze different filtering strategies for real life indoor WLAN positioning systems. The performance criteria for the comparison are the mean of localization errors and computational complexity. Three nonlinear...
The leader node particle filter is a partially distributed approach to tracking in a sensor network, in which the node performing the particle filter computations (the leader node) changes over time. The primary advantage is that the position of the leader node can follow the target, improving the efficiency of data collection. When the leader node changes, the particle filter must be communicated...
The detection and tracking of targets in aerial imagery of cluttered urban environments is addressed. Polar matching, using dual-tree complex wavelet transforms, is used as a shift and rotation invariant detector. A particle filter is employed to add robustness, especially in the event of target occlusion. We show that, together, these methods can robustly track a ground based target as it becomes...
In recent years, the particle filter has become commonly accepted as the preferred tool for single target tracking in highly non-linear and non-Gaussian environments. This paper investigates the issues that arise when particle filters are integrated into a hierarchical data fusion system, in which the sensor-level tracking is performed using particle filters, but central-level track fusion is performed...
Different information theoretic sensor management approaches are compared in a Bayesian target-tracking problem. Specifically, the performance using the expected Renyi divergence with different parameter values is compared theoretically and experimentally. Included is the special case in which the expected Renyi divergence is equal to the expected Kullback-Leibler divergence, which is also equivalent...
In theory, a good joint particle filter allows to approximate the exact Bayesian filter solution arbitrarily well. This has motivated a strong and successful development of single target tracking particle filters. Nevertheless, for tracking multiple closely spaced maneuvering targets, there is evidence in literature which seems to contradict the theoretical expectation. The mystery of this apparent...
Several nonlinear filtering techniques are investigated for nonlinear tracking problems. Experimental results show that for a weakly nonlinear tracking problem, the extended Kalman filter and the unscented Kalman filter are good choices, while a particle filter should be used for problems with strong nonlinearity. To quantitatively determine the nonlinearity of a nonlinear tracking problem, we propose...
Within the area of target tracking particle filters are the subject of consistent research and continuous improvement. The purpose of this paper is to present a novel method of fusing the information from multiple particle filters tracking in a multisensor multitarget scenario. Data considered for fusion is under the form of labeled particle clouds, obtained in the simulation from two probability...
This paper proposes a technique for motion estimation of groups of ground targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure...
A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point-measurements from the observed sensor data. Track-before-detect (TkBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TkBD problem. This paper...
This paper presents algorithms for consistent joint localisation and tracking of multiple targets in wireless sensor networks under the decentralised data fusion (DDF) paradigm where particle representations of the state posteriors are communicated. This work differs from previous work as more generalised methods have been developed to account for correlated estimation errors that arise due to common...
This paper presents Monte Carlo (MC) methods for multi-target tracking and data association. We focus on comparing different estimation methods based on joint and non-joint state particle filters (PF) and joint probabilistic data association (JPDA) techniques. A novel data association algorithm for PF, founded on a combination of PDA and nearest neighbour (NN) techniques, is also developed. In this...
We propose a particle filter based solution which uses auxiliary fixed point smoothers to the problem of out of sequence measurements. Three different cases, namely, auxiliary extended Kalman smoother, auxiliary unscented Kalman smoother and auxiliary particle smoother are considered for the auxiliary fixed point smoother. The proposed filter which can effectively combine out of sequence measurements...
In target tracking, standard sensors as radar and EO/IR observe the target with a negligible delay, since the speed of light is much larger than the speed of the target. This contribution studies the case where the ratio of the target and the propagation speed is not negligible, as is the case in sensor networks with microphones, geophones or sonars for instance, where the speed of air, ground waves...
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