CRAMER-RAO LOWER BOUND FOR LOCALIZATION IN ENVIRONMENTS WITH DYNAMICAL OBSTACLES
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Cramer-Rao Lower Bound (CRLB) of location estimation
under Gaussian distribution is widely used in localization
applications. However, under the environments with
dynamical obstacles, the existing CRLB does not represent the
effect of the non-line-of-sight (NLOS) bias caused by
dynamical obstacles. In this paper, based on received signal
strength (RSS) measurements, a uniform random variable is
used to model the NLOS bias effect. Furthermore, The
corresponding maximum likelihood estimator (MLE) and
CRLB under the joint distribution of Gaussian distribution
and uniform distribution are derived. Numerical results
validate that the proposed MLE and CRLB are effective in
environments with dynamic obstacles.
i'm attaching the paper for reference please go through this and let me know can ayone implement this fully or partially
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