Abstract-Sensor networks can benefit greatly from locationawareness, since it allows information gathered by the sensors to be tied to their physical locations. Ultra-wide bandwidth (UWB) transmission is a promising technology for location-aware sensor networks, due to its power efficiency, fine delay resolution, and robust operation in harsh environments. However, the presence of walls and other obstacles presents a significant challenge in terms of localization, as they can result in positively biased distance estimates. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-of-sight (NLOS) propagation. From these channel pulse responses, we extract features that are representative of the propagation conditions. We then develop classification and regression algorithms based on machine learning techniques, which are capable of: (i) assessing whether a signal was transmitted in LOS or NLOS conditions; and (ii) reducing ranging error caused by NLOS conditions. We evaluate the resulting performance through Monte Carlo simulations and compare with existing techniques. In contrast to common probabilistic approaches that require statistical models of the features, the proposed optimization-based approach is more robust against modeling errors.

Network localization and navigation give rise to a new paradigm for communications and contextual data collection, enabling a variety of new applications that rely on position information of mobile nodes (agents). The performance of such networks can be significantly improved via the use of cooperation. Therefore, a deep understanding of information exchange and cooperation in the network is crucial for the design of location-aware networks. This article presents an exploration of cooperative network localization and navigation from a theoretical foundation to applications, covering technologies and spatiotemporal cooperative algorithms

Location-awareness is becoming increasingly important in wireless networks. Indoor localization can be enabled through wideband or ultra-wide bandwidth (UWB) transmission, due to its fine delay resolution and obstacle-penetration capabilities. A major hurdle is the presence of obstacles that block the line-of-sight (LOS) path between devices, affecting ranging performance and, in turn, localization accuracy. Many techniques have been proposed to address this issue, most of which make modifications to the localization algorithm. Since many localization algorithms work with distance or angle estimates, rather than received waveforms, information inherent in the wideband waveform is lost, leading to sub-optimal ranging error mitigation. To avoid this information loss, we present a novel approach to mitigate ranging errors directly in the physical layer. In contrast to existing techniques, which detect the non-line-of-sight (NLOS) condition, our approach directly mitigates the bias incurred in both LOS and non-LOS conditions. In particular, we apply two classes of non-parametric regressors to form an estimate of the ranging error. Our work is based on, and validated by, an extensive indoor measurement campaign with FCC-compliant UWB radios. The results show that the proposed regressors provide significant performance improvements in various practical localization scenarios, compared to conventional approaches.

Abstract-In this paper, we present a framework for evaluating the bit error probability of N d -branch diversity combining in the presence of non-ideal channel estimates. The estimator structure presented is based on the maximum-likelihood (ML) estimate and arises naturally as the sample mean of N p pilot symbols. The framework presented requires only the evaluation of a single integral involving the moment generating function of the norm square of the channel-gain vector, and is applicable to channels with arbitrary distribution, including correlated fading. Our analytical results show that the practical ML channel estimator preserves the diversity order of an N d -branch diversity system, contrary to conclusions in the literature based upon a model that assumes a fixed correlation between the channel and its estimate. Finally, we investigate the asymptotic signal-to-noise ratio penalty due to estimation error and reveal a surprising lack of dependence on the number of diversity branches.

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Ultra-wide bandwidth (UWB) transmission is a promising technology for indoor localization due to its fine delay resolution and obstacle-penetration capabilities. However, the presence of walls and other obstacles introduces a positive bias in distance estimates, severely degrading localization accuracy. We have performed an extensive indoor measurement campaign with FCC-compliant UWB radios to quantify the effect of non-line-ofsight (NLOS) propagation. Based on this campaign, we extract key features that allow us to distinguish between NLOS and LOS conditions. We then propose a nonparametric approach based on support vector machines for NLOS identification, and compare it with existing parametric (i.e., model-based) approaches. Finally, we evaluate the impact on localization through Monte Carlo simulation. Our results show that it is possible to improve positioning accuracy relying solely on the received UWB signal.

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