Indoor positioning based on statistical multipath channel modeling

Yen and Voltz EURASIP Journal on Wireless Communications and Networking 2011, 2011:189 http://jwcn.eurasipjournals.com/content/2011/1/189 RESEARCH Open Access Indoor positioning based on statistical multipath channel modeling Chia-Pang Yen1* and Peter J Voltz2 Abstract In order to estimate the location of an indoor mobile station (MS), estimated time-of-arrival (TOA) can be obtained at each of several access points (APs). These TOA estimates can then be used to solve for the location of the MS. Alternatively, it is possible to estimate the location of the MS directly by incorporating the received signals at all APs in a direct estimator of position. This article presents a deeper analysis of a previously proposed maximum likelihood (ML)-TOA estimator, including a uniqueness property and the behavior in nonline-of-sight (NLOS) situations. Then, a ML direct location estimation technique utilizing all received signals at the various APs is proposed based on the ML-TOA estimator. The Cramer-Rao lower bound (CRLB) is used as a performance reference for the ML direct location estimator. Keywords: indoor positioning, maximum likelihood (ML), time-of-arrival (TOA), direct location estimation 1 Introduction With the emergence of location-based applications and the need for next-generation location-aware wireless networks, location finding is becoming an important problem. Indoor localization has recently started to attract more attention due to increasing demands from security, commercial and medical services. For example, next generation corporate wireless local area networks (WLAN) will utilize location-based techniques to improve security and privacy [1]. The requirement for high accuracy positioning in complex multipath channels and nonline-of-sight (NLOS) situations has made the task of indoor localization very challenging as compared to outdoor environments. Conventionally, the positioning problem is solved via an indirect (two-step) parameter estimation scheme. First, the time-of-arrival (TOA) estimation at each access point (AP) is performed. The TOA estimator estimates the first arriving path delay, which corresponds to the line-of-sight (LOS) distance between the transmitter and the receiver assuming the LOS path exists. Then, these TOA estimates from each AP are transmitted to a central terminal at which the location estimation is carried out * Correspondence: 1 ITRI (Industrial Technology Research Institute), 195, Sec. 4, Chung Hsing Rd., Chutung, Hsinchu 310, Taiwan Full list of author information is available at the end of the article by various algorithms, such as trilateration or least squares fitting, etc. [2,3]. Recently, the direct location estimation method has been proposed as another aspect to the positioning problem [4]. Unlike the indirect methods which split the location estimation efforts between the APs and the central terminal, the direct positioning methods rely only on the central terminal to perform the location estimation task. The APs just relay the received signals to the central terminal for it to estimate the location of the mobile station (MS). It has been shown that the direct method can outperform the indirect method [4]. For the indirect positioning methods, the first step is to obtain an accurate TOA estimation. To separate closely spaced channel paths, super-resolution techniques [5], such as multiple signal classification (MUSIC), etc. [6-8], are reported to be able to significantly improve the TOA estimations as compared to the conventional autocorrelation approach [9]. Maximum likelihood (ML) is a natural approach for TOA estimation but in order to resolve the multiparameter issue that seems natural to the multipath environments, a novel ML-TOA estimator that only requires a one-dimensional search is proposed in [10]. The MLTOA technique estimates only the first arriving path delay based on the observation that this parameter is the only quantity needed for positioning. It was found that in © 2011 Yen and Voltz; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Yen and Voltz EURASIP Journal on Wireless Communications and Networking 2011, 2011:189 http://jwcn.eurasipjournals.com/content/2011/1/189 dense multipath environments, the ML-TOA estimation outperforms the super-resolution methods discussed in [11,12]. The effect of considering only the first arriving path delay in positioning was studied in [13]. Based on the analyses of the Cramer-Rao lower bound (CRLB), the authors showed that if the paths are correlated then including other paths could improve the TOA estimation accuracy, however, they also pointed out that doing so “would not help enhance the accuracy significantly but merely increase the computational complexity.” In this article, several important properties pertaining to the ML-TOA estimator that were previously left unanswered are established. First is the uniqueness of the ML-TOA estimator. For TOA estimation in multipath environments, not only the additive noise but also the multipath channels are random. Therefore, it is not obvious that the estimates converge to the exact parameter when signal-to-noise ratio (SNR) increases. Here, we demonstrate that the ML-TOA estimation provides the unique, correct TOA in the absence of noise provided the channel statistics are known. The effects of the NLOS situations are also discussed. The NLOS situation is another major challenge for indoor positioning for it can cause large TOA estimation bias that in turn result in large location estimation errors [14]. There are optimization methods which can be used to mitigate the error due to NLOS. In [15,16], the optimization is carried out with respect to the unknown mobile location or the NLOS bias. In [13,17,18], statistical estimation methods are proposed in the case that the statistical knowledge such as the propagation scattering models or the NLOS delays statistics are known. In this article, the proposed ML-TOA is shown to be able to incorporate the statistics of NLOS channels automatically and thus reduce the estimation bias due to NLOS path delays. The direct positioning method has just started to emerge as an interesting research topic and has been shown to provide improvement in the location estimation accuracy. Thus, in this article, in addition to the indirect (two-step) method, we also propose a direct ML positioning algorithm based on the ML-TOA estimator. In [19], the authors proposed a direct positioning method for orthogonal-frequency-division-multiplexing (OFDM) signals. There, the APs are assumed to be equipped with antenna arrays, the source is located in the far field and the channel power delay profile has a significant path while the rest paths are ignored. Her (...truncated)