Abstract-In this letter, we obtain the Maximum Likelihood Estimator of position in the framework of Global Navigation Satellite Systems. This theoretical result is the basis of a completely different approach to the positioning problem, in contrast to the conventional two-steps position estimation, consisting of estimating the synchronization parameters of the in-view satellites and then performing a position estimation with that information. To the authors' knowledge, this is a novel approach which copes with signal fading and it mitigates multipath and jamming interferences. Besides, the concept of Position-based Synchronization is introduced, which states that synchronization parameters can be recovered from a user position estimation. We provide computer simulation results showing the robustness of the proposed approach in fading multipath channels. The Root Mean Square Error performance of the proposed algorithm is compared to those achieved with state-of-the-art synchronization techniques. A Sequential Monte-Carlo based method is used to deal with the multivariate optimization problem resulting from the ML solution in an iterative way.

Abstract-This paper addresses the estimation of the code-phase (pseudorange) and the carrier-phase of the direct signal received from a direct-sequence spread-spectrum satellite transmitter. The signal is received by an antenna array in a scenario with interference and multipath propagation. These two effects are generally the limiting error sources in most high-precision positioning applications. A new estimator of the code-and carrier-phases is derived by using a simplified signal model and the maximum likelihood (ML) principle. The simplified model consists essentially of gathering all signals, except for the direct one, in a component with unknown spatial correlation. The estimator exploits the knowledge of the direction-of-arrival of the direct signal and is much simpler than other estimators derived under more detailed signal models. Moreover, we present an iterative algorithm, that is adequate for a practical implementation and explores an interesting link between the ML estimator and a hybrid beamformer. The mean squared error and bias of the new estimator are computed for a number of scenarios and compared with those of other methods. The presented estimator and the hybrid beamforming outperform the existing techniques of comparable complexity and attains, in many situations, the Cramér-Rao lower bound of the problem at hand.

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This paper addresses the problem of determining the Cramér-Rao lower bound (CRLB) for the parameters and breakpoint distance in a Path-Loss Channel model for Received Signal Strength (RSS) measurements. The path loss model is usually assumed for corrupted RSS measurements due to the shadow fading channel feature. In this paper the two-slope path loss model is considered, in which RSS measurements are modeled differently for close and far distances. Closed-form expressions for the CRLB parameters are derived for unknown breakpoint distance. For unknown parameters and breakpoint distance value, a Bayesian estimation method is proposed. The CRLB is then compared with the performance of the herein proposed method. The comparison illustrates convergence and efficiency of the Bayesian estimator.

A Maximum Entropy statistical treatment of an inverse problem concerning frame theory is presented. The problem arises from the fact that a frame is an overcomplete set of vectors that de nes a mapping with no unique inverse. Although any vector in the concomitant space can be expressed as linear combination of frame elements, the coe cients of the expansion are not unique. Frame theory guarantees the existence of a set of coe cients which is \optimal" in a Minimum Norm sense. We s h o w here that these coe cients are also \optimal" from a Maximum Entropy viewpoint.

Received Signal Strength (RSS) localization is widely\ud used due to its simplicity and availability in most mobile devices.\ud The RSS channel model is defined by the propagation losses\ud and the shadow fading. These parameters might vary over time\ud because of changes in the environment. In this paper, the problem\ud of tracking a mobile node by RSS measurements is addressed,\ud while simultaneously estimating a two-slope RSS model. The\ud methodology considers a Kalman filter with Interacting Multiple\ud Model architecture, coupled to an on-line estimation of the\ud observation’s variance. The performance of the method is shown\ud through numerical simulations in realistic scenarios.Peer ReviewedPostprint (published version