Position Report Enhancement Using Bayesian Estimator

Abstract

In this article, a Bayesian estimator for a target position report is proposed. It is based on a maximum a priori algorithm, where the user's device knowledge about its location is used to deduce the prior probability density function. The algorithm does not require knowledge about the signal and noise levels, meaning, noninformative priors are used. It is well known that with a higher number of antenna elements in an array, narrower beams can be formed. In beamforming, task narrow beams are useful for serving many users simultaneously, whereas in direction-of-arrival (DOA) estimation, we are not interested in narrow beams as such; instead, estimation accuracy is important. So, reducing the number of antenna elements used for DOA estimation is beneficial from a system complexity point of view. In this article, signal source location report is used to enhance the estimated DOA, for the task, MAP estimator is developed. We will show that in the case of small array size and large array covariance matrix error values, the proposed estimator is the only one capable of improving the prior knowledge about the transmitter when comparing it with other popular algorithms, such as the maximum likelihood estimator and Root MUSIC. The algorithm could be used in a variety of different scenarios, but its advantages emerge in the case of complex signal propagation environments, such as urban canyons and large airports.