This letter addresses the problem of target localization in a 3-D space, utilizing combined measurements of received signal strength and angle of arrival (AoA). By using the spherical coordinate conversion and available AoA observations to establish new relationships between the measurements and the unknown target location, we derive a simple closed-form solution method. We then show that the proposed method has straightforward adaptation to the case where the target's transmit power is also not known. Simulation results validate the outstanding performance of the proposed method.
IndexTerms-Wireless localization, received signal strength (RSS), angle of arrival (AoA), weighted least squares (WLS), wireless sensor network (WSN).
This letter addresses the problem of target localization in harsh indoor environments based on range measurements. To mitigate the non-line-of-sight (NLOS) bias, we propose a novel robust estimator by transforming the localization problem into a generalized trust region sub-problem framework. Although still non-convex in general, this class of problems can be readily solved exactly by means of bisection procedure. The new approach does not require to make any assumptions about the statistics of NLOS bias, nor to try to distinguish which links are NLOS and which are not. Unlike the existing algorithms, the computational complexity of the proposed algorithm is linear in the number of reference nodes. Our simulation results corroborate the effectiveness of the new algorithm in terms of NLOS bias mitigation and show that the performance of our estimator is highly competitive with the performance of the stateof-the-art algorithms. In fact, they show that the novel estimator outperforms slightly the existing ones in general, and that it always provides a feasible solution.
This paper addresses target localization problem in a cooperative 3-D wireless sensor network (WSN). We employ a hybrid system that fuses distance and angle measurements, extracted from the received signal strength (RSS) and angle-ofarrival (AoA) information, respectively. Based on range measurement model and simple geometry, we derive a novel non-convex estimator based on the least squares (LS) criterion. The derived non-convex estimator tightly approximates the maximum likelihood (ML) one for small noise levels. We show that the developed non-convex estimator is suitable for distributed implementation, and that it can be transformed into a convex one by applying a second-order cone programming (SOCP) relaxation technique. We also show that the developed non-convex estimator can be transformed into a generalized trust region subproblem (GTRS) framework, by following the squared range (SR) approach.
Abstract:In this paper we consider the optimum detection of OFDM (Orthogonal Frequency Division Multiplexing) signals with strong nonlinear distortion effects. It is shown that the optimum performance with strong nonlinear distortion effects is not as bad as one might expect and can even be better than the performance with conventional, linear transmitters. To achieve these excellent performances we should employ receivers able to take advantage of the information associated to transmitted data symbols that is inherent to the nonlinear distortion component, in opposition to traditional OFDM implementations where nonlinear distortion effects are regarded as an undesirable noise-like component. We study the achievable gains of the optimum receiver both analytically and by simulation. Since the complexity of optimum receivers is extremely high when we have nonlinear distortion effects, even for OFDM signals with a small number of subcarriers, we propose several sub-optimum receivers and evaluate their performance. Our sub-optimal receivers allow remarkable performance improvements, being able to reduce significantly the gap between the optimum performance and the performance of typical OFDM receivers.
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