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We consider the problem of estimator performance prediction in stochastic systems with Markov switching dynamical models. Following Hernandez et al, a new best-fitting Gaussian performance measure (BFG-PM) for jump Markov systems is proposed. The new BFG-PM matches the moments of the state transition density of the Markov switching system and the approximate uni-modal system. The new BFG-PM has a...
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...
In the sensor-based applications context, sensor reliability is not always taken into account. Due to the uncertain nature of sensors, we must integrate to the problem of belief attached to the sensors data. This paper deals with the dysfunction detection based on a two-level approach. The first level extracts conflict information of the combination of multiple data sources. The second level is based...
Recent attention in quickest change detection in a multi-sensor scenario has been on the case where the densities of the observations at all the sensors change instantaneously at the time of disruption. In this work, we consider a scenario where change propagates across the sensors and its propagation can be modeled as a Markov process. A centralized, Bayesian version of this problem, with a common...
This paper introduces a new approach to solve sensor management problems. Classically sensor management problems can be well formalized as partially-observed Markov decision processes (POMPD). The original approach developped here consists in deriving the optimal parameterized policy based on a stochastic gradient estimation. We assume in this work that it is possible to learn the optimal policy off-line...
This paper describes algorithms for probabilistically evaluating multi-frame data-association hypotheses formed in multiple-hypothesis, multiple-target tracking, using Markov chain Monte Carlo (MCMC) methods (also known as sequential Monte Carlo (SMC) methods). Each algorithm is designed to sequentially, randomly generate multi-frame data association hypotheses, and to converge to a stationary process...
In this paper, considering the problem of collaborative sensor management and data fusion for multitarget tracking, authors propose an altered version of a classical Value Iteration algorithm, one of the most commonly used techniques to calculate the optimal policy for Markov decision processes (MDPs). Dynamic element matching (DEM) algorithms, widely used for reducing harmonic distortion in Digital-to-Analog...
This paper describes a continuous-time, interacting-multiple-model (IMM) extrapolation algorithm. A system state is modeled as a continuous-time, affine-Gaussian stochastic dynamical process driven by a white process noise as well as structural changes modeled by a finite-state, continuous-time Markov process. The system generally assumes multiple models with different system state dimensions and...
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...
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