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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...
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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