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Risk-sensitive filter is a robust and numerically efficient algorithm compared to risk neutral filter with model uncertainties. For nonlinear plant, the square root unscented Kalman risk-sensitive filter (SUKRSF) is proposed in this paper by using unscented transformation approximation. Square root unscented Kalman filter (SRUKF), a derivative-free nonlinear estimation tool is used to solve risk-sensitive...
A classification system such as an automatic target recognition (ATR) system with N possible output labels (or decisions) will have N(N-1) possible errors. The receiver operating characteristic (ROC) manifold was created to quantify all of these errors. Truthed data will produce an approximation to a ROC manifold. How well does the approximate ROC manifold approximate the true ROC manifold? Several...
The identity management problem is the problem of probabilistically keeping track of the association between target tracks and target identities, based on observations made by sensors. Updates of the belief state can happen because of new sensor observations reflecting on target identity, or because targets come near each other so that their identities become confused or mixed. Since the space of...
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 paper presents two methods of updating the weights of a Gaussian mixture to account for the density propagation within a data assimilation setting. The evolution of the first two moments of the Gaussian components is given by the linearized model of the system. When observations are available, both the moments and the weights are updated to obtain a better approximation to the a posteriori probability...
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...
An algorithm for scheduling and control of passive sensors is proposed. This algorithm is based on a partially observed Markov decision process and an expected short- or long-term reward given by the sum of Renyi information divergences between Gaussian densities. This allows effective and efficient implementations and is demonstrated on simulations of situation scenarios of practical interest. The...
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...
Linear filtering in the presence of timing uncertainty is considered. In the model assumed here the true measurement times are intermittently available and noisy measurement times are always available. The estimation problem involves jointly estimating the state and the timing error parameters. The optimal Bayesian estimator cannot be found in closed-form so three approximations are proposed. The...
For estimation and fusion tasks it is inevitable to approximate a Gaussian mixture by one with fewer components to keep the complexity bounded. Appropriate approximations can be typically generated by exploiting the redundancy in the shape description of the original mixture. In contrast to the common approach of successively merging pairs of components to maintain a desired complexity, the novel...
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...
The multitarget intensity filter is derived from a Bayesian first principles approach using a Poisson point process approximation at one step. The prior multitarget model is assumed to be a Poisson point process. The Bayes multitarget posterior probability density function is first defined on the Poisson event space, and then reformulated in terms of the intensity functions that characterize all Poisson...
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 expectation-maximization for handling association conflicts. Linearity in the number of targets and measurements is the main motivation for a further development and extension of this methodology. However,...
The problem of tracking targets, where measurements may occasionally be masked by the Doppler blind zone of the sensor, arises in Ground Moving Target Indicator tracking and aerial surveillance. For such problems, no target return is registered when the range rate (Doppler) of the target falls below a sensor-specific threshold in magnitude. For this reason, possible missed detections provide kinematic...
Considering latest improvements, there are different applications for data fusion techniques. In food transportation systems, measuring environmental conditions like temperature and humidity is necessary for monitoring and controlling quality of products. Application of data fusion on measured data increases reliability of food transportation system. This paper introduces application of data fusion...
The problem of decentralized changepoint detection in a distributed multisensor setting with binary quantization (BQ) is addressed. Attention is drawn to the case of composite post-change hypotheses when the post-change parameter is unknown. A multichart CUSUM detection procedure with binary quantization, called the M-BQ-CUSUM test, is proposed. The methodology is based on using Mges2 putative values...
This paper addresses the efficient state estimation for mixed linear/nonlinear dynamic systems with noisy measurements. Based on a novel density representation - sliced Gaussian mixture density - the decomposition into a (conditionally) linear and nonlinear estimation problem is derived. The systematic approximation procedure minimizing a certain distance measure allows the derivation of (close to)...
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...
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