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Quasi-Monte Carlo (QMC)-based particle filters can obtain more accurate estimation than the general particle filters, with formidable computational complexity, however. Spatial-domain multiresolutional particle filters are more efficient by reducing the number of particles, but unevenly samples may cause estimation error. Aiming at these, we combine QMC numerical technique and multiresolutional methodology...
The problem of estimating a spectral representation of damped sinusoidal signals from a gapped data set is of considerable interest in several applications. In this paper, we propose a new iterative adaptive approach, named dIAA, that provides such an estimate also in the case of irregularly sampled data, a common scenario in, for instance, spectroscopical data measurements. Numerical examples illustrate...
Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance of the minimum description length (MDL) criterion based on biased and unbiased estimation and compare it...
Via jointly processing multiple (sum, azimuth- and elevation-difference) monopulse matched filter samples it is possible to extract and localize several (more than two) targets spaced more closely than the classical interpretation of radar resolution. This paper derives the Cramer-Rao lower bound (CRLB) for sampled monopulse radar data.
In this paper, we proposed an algorithm for estimating the power of the system noise for adaptive filters. The proposed algorithm is developed by using the relationship between mean square error (MSE) and excess MSE (EMSE). Recursive calculations for the MSE and the EMSE are introduced, and an estimated power of system noise is derived by using the values. Through computer simulations, we show that...
In this paper we study the outage diversity in distributed estimation over parallel fading channels.We find tight upper and lower bounds on the diversity order and show that they are arbitrarily close under certain conditions. Our results show that the diversity order does not always equal to the number of sensors, but also depends on sensing quality of the sensors.
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