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In this paper, we propose a new maximum-likelihood (ML) target location estimator which uses quantized sensor data and wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that imperfect channel statistics between wireless sensors and the fusion center are incorporated in the localization algorithm. We call this approach "channel-aware target...
In this paper, a method based on maximum likelihood to estimate the target true height in 2-D radar network is presented, and it is mathematically proved that the estimator is unbiased The Cramer-Rao low bound (CRLB) of estimation error is also derived Some conclusions are drawn based on simulations. It's concluded that the bearing error only has a bounded influence on CRLB, however, the influence...
In this paper, we propose a method of combining some interacting multiple model-extended Viterbi (IMM-EV) algorithms for target tracking. The objective of the proposed scheme is to take the maximum advantage of the combined strengths of some IMM-EV algorithms so as to achieve better performance and/or computational efficiency than the IMM and some tracking algorithms. Simulation results demonstrate...
Given an area where an unknown number of unaccounted radioactive sources potentially exist, and using gamma- radiation count measurements collected at known locations within this area, the problem is to estimate the number of sources as well as their locations and intensities. Two approaches are investigated. The first is based on the maximum likelihood estimation and the generalised maximum likelihood...
An alternating directions method is presented for joint maximum a posteriori estimation of target track and sensor field using bistatic range data. The algorithm cycles over two sub-algorithms: one improves the target state estimate conditioned on sensor field state, and the other improves the sensor field state estimate conditioned on target state. Nonlinearities in the sub-algorithms are mitigated...
In this paper, we present a novel approach to parametric density estimation from given samples. The samples are treated as a parametric density function by means of a Dirac mixture, which allows for applying analytic optimization techniques. The method is based on minimizing a distance measure between the integral of the approximation function and the empirical cumulative distribution function (EDF)...
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