Multi Detector Fusion of Dynamic TOA Estimation using Kalman Filter

In this paper, we propose fusion of dynamic TOA (time of arrival) from multiple non-coherent detectors like energy detectors operating at sub-Nyquist rate through Kalman filtering. We also show that by using multiple of these energy detectors, we can achieve the performance of a digital matched filter implementation in the AWGN (additive white Gaussian noise) setting. We derive analytical expression for number of energy detectors needed to achieve the matched filter performance. We demonstrate in simulation the validity of our analytical approach. Results indicate that number of energy detectors needed will be high at low SNRs and converge to a constant number as the SNR increases. We also study the performance of the strategy proposed using IEEE 802.15.4a CM1 channel model and show in simulation that two sub-Nyquist detectors are sufficient to match the performance of digital matched filter

Statistical Characterization and Mitigation of NLOS Errors in UWB Localization Systems

In this paper some new experimental results about the statistical characterization of the non-line-of-sight (NLOS) bias affecting time-of-arrival (TOA) estimation in ultrawideband (UWB) wireless localization systems are illustrated. Then, these results are exploited to assess the performance of various maximum-likelihood (ML) based algorithms for joint TOA localization and NLOS bias mitigation. Our numerical results evidence that the accuracy of all the considered algorithms is appreciably influenced by the LOS/NLOS conditions of the propagation environment

Accurate Positioning in Ultra-Wideband Systems

Cataloged from PDF version of article.Accurate positioning systems can be realized via ultra-wideband signals due to their high time resolution. In this article, position estimation is studied for UWB systems. After a brief introduction to UWB signals and their positioning applications, two-step positioning systems are investigated from a UWB perspective. It is observed that time-based positioning is well suited for UWB systems. Then time-based UWB ranging is studied in detail, and the main challenges, theoretical limits, and range estimation algorithms are presented. Performance of some practical time-based ranging algorithms is investigated and compared against the maximum likelihood estimator and the theoretical limits. The trade-off between complexity and accuracy is .observe

Statistics of the MLE and Approximate Upper and Lower Bounds - Part 1: Application to TOA Estimation

In nonlinear deterministic parameter estimation, the maximum likelihood estimator (MLE) is unable to attain the Cramer-Rao lower bound at low and medium signal-to-noise ratios (SNR) due the threshold and ambiguity phenomena. In order to evaluate the achieved mean-squared-error (MSE) at those SNR levels, we propose new MSE approximations (MSEA) and an approximate upper bound by using the method of interval estimation (MIE). The mean and the distribution of the MLE are approximated as well. The MIE consists in splitting the a priori domain of the unknown parameter into intervals and computing the statistics of the estimator in each interval. Also, we derive an approximate lower bound (ALB) based on the Taylor series expansion of noise and an ALB family by employing the binary detection principle. The accurateness of the proposed MSEAs and the tightness of the derived approximate bounds are validated by considering the example of time-of-arrival estimation

Map-Aware Models for Indoor Wireless Localization Systems: An Experimental Study

The accuracy of indoor wireless localization systems can be substantially enhanced by map-awareness, i.e., by the knowledge of the map of the environment in which localization signals are acquired. In fact, this knowledge can be exploited to cancel out, at least to some extent, the signal degradation due to propagation through physical obstructions, i.e., to the so called non-line-of-sight bias. This result can be achieved by developing novel localization techniques that rely on proper map-aware statistical modelling of the measurements they process. In this manuscript a unified statistical model for the measurements acquired in map-aware localization systems based on time-of-arrival and received signal strength techniques is developed and its experimental validation is illustrated. Finally, the accuracy of the proposed map-aware model is assessed and compared with that offered by its map-unaware counterparts. Our numerical results show that, when the quality of acquired measurements is poor, map-aware modelling can enhance localization accuracy by up to 110% in certain scenarios.Comment: 13 pages, 11 figures, 1 table. IEEE Transactions on Wireless Communications, 201

Discrete fourier transform-based TOA estimation in UWB systems

In this paper, we propose two time of arrival estimators for ultra wideband signals based on the phase difference between the discrete Fourier transforms (DFT) of the transmitted and received signals. The first estimator is based on the slope of the unwrapped phase and the second one on the absolute unwrapped phase. We derive the statistics of the unwrapped phase. We show that slope-based estimation almost achieves asymptotically the baseband Cramer-Rao lower bound (CRLB), while the absolute-phase-based estimator achieves asymptotically the passband CRLB. We compare the proposed estimators to the time-domain maximum likelihood estimator (MLE). We show that the MLE achieves the CRLB faster than the DFT-based estimator, while the DFT-based estimator outperforms the MLE for low signal to noise ratios. We describe also how to use the proposed estimators in multipath UWB channels