A Survey of Localization in Wireless Sensor Network <s> NLOS Identification/Classification. As shown in <s> We consider the problem of tracking mobile stations using the ranging measurements from multiple base stations (BSs), without a priori knowledge of which BSs (if any) have a direct line of sight (LOS) range measurement. The two key contributions of this paper are to show that (1) it is possible to discriminate between LOS versus non-LOS (NLOS) measurements at each BS by using the time history of its range measurements in a simple hypothesis testing problem and (2) it is possible to correct the NLOS ranging error by exploiting a priori knowledge of the statistical characteristics of the system's standard measurement noise. Simulation examples are presented and the RMS error is compared to the Cramer Rao lower bound on location estimation. <s> BIB001 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Identification/Classification. As shown in <s> This paper presents the problem of identifying whether a received signal at a base station is due to a line-of-sight (LOS) transmission or not (NLOS). This is a first step towards estimating the mobile station's location. We formulate the NLOS identification problem as a binary hypothesis test where the range measurements are modeled as being corrupted by additive noise, with different probability distributions depending on the hypothesis. We solve the binary hypothesis test under several assumptions, proposing appropriate decision criteria. <s> BIB002 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Identification/Classification. As shown in <s> One of the major challenges to finding accurate mobile terminal location is the non-line-of-sight (NLOS) propagation caused by the blocking of the signal's direct path by obstacles. Traditional location algorithms perform poorly in NLOS environments and this has made it necessary to develop techniques to identify NLOS transmissions and then employ suitable error mitigation techniques. It is extremely difficult to characterize NLOS error because it varies with time and is location specific. We present statistical based techniques to distinguish LOS and NLOS propagation for various scenarios and present simulation results to validate them. <s> BIB003 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Identification/Classification. As shown in <s> A mobile station (MS) location can be estimated from the measurement of the time of arrival of the signals that travel between each base station and the MS. In this scenario, the existence of non-line-of-sight (NLOS) propagation paths has been considered to be the main drawback to achieve high precision in positioning, since NLOS propagation introduces large and unpredictable errors in the time estimates that are obtained from the measurements. In this paper, we propose a new technique, called prior NLOS measurement correction (PNMC), to effectively correct the measurements from NLOS propagation in a previous stage to the positioning process. PNMC is based on a statistical processing of a record of measurements taken over a time window. This processing relies on the statistical estimate of the NLOS measurement ratio present in our record. This estimate is used to range the NLOS recorded measurements into segments. Finally, the correction is carried out by subtracting the expected NLOS errors for each segment. Several simulations have been conducted to show the increase in accuracy obtained by the usage of PNMC and the great improvement that this prior measurement correction means to subsequent wireless location and positioning techniques. <s> BIB004 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Identification/Classification. As shown in <s> Nonline-of-sight (NLOS) propagation is one of the challenges in radio positioning. Significant attention has been drawn to the mitigation of the NLOS effect in recent years. This paper focuses on the identification of NLOS conditions by employing the statistical decision theory. A Neyman-Pearson (NP) test method is first derived for scenarios where either 1-D or 2-D angular measurements are provided. A time-of-arrival (TOA) based method is then developed under idealized conditions to provide a performance reference. In the presence of both TOA and received signal strength (RSS) measurements, a joint identification method is derived to efficiently exploit the TOA and RSS measurements. Analytical expressions of the probability of detection (POD) and the probability of false alarm (PFA) are derived for all the scenarios considered. Two theorems and one corollary regarding the line-of-sight (LOS) conditions based on the angle of arrival (AOA) are also presented, and the proofs are provided. Simulation results demonstrate that the proposed methods perform well, and the joint TOA- and RSS-based method considerably outperforms the TOA-based methods. The proposed methods are robust to the model errors, as demonstrated through simulations. It is also shown that the analytical results agree well with the simulated ones. <s> BIB005
Wylie and Holtzman BIB001 proposed a method based on parameter hypothesis test which determines the measurements whether belong to NLOS by comparing the NLOS variance with the LOS variance. It has a simple criterion but needs the detailed environment parameter and a prior knowledge. Borras et al. BIB002 investigated NLOS identification by using binary hypothesis test and generalized likelihood ratio for identifying NLOS error. It proposed a proper decision criteria and its premise is that the NLOS error is Gaussian distributed with a large variance. Based on Wylie-Holtzman algorithm, Mazuelas et al. BIB004 proposed an improvement by using NLOS ratio estimation and it will be able to correct the NLOS measurements from the previous knowledge of this ratio. The aforementioned methods need a mount of priori knowledge and historical data. Chan et al.  proposed a residual test (RT) method to overcome the shortage which needs lots of priori knowledge. It determines the measurements whether belong to NLOS by measuring the samples appropriate to central Chi-distribution. The principle of this method is that if all measurements are LOS, and if the localization technique gives maximum likelihood estimates, then the residuals, normalized by the Cramer Rao Lower Bound (CRLB), will have a central χ 2 distribution. And if the measurements contain the NLOS error, the distribution is noncentral χ 2 distribution. Venkatraman and Caffery Jr. BIB003 investigated NLOS identification for moving targets by using a time series of range measurements. Gezici et al.  proposed a nonparameter-based hypothesis test method which used a distance metric between a known measurement error distribution and a nonparametrically estimated distance measurement distribution. Yu and Guo BIB005 also proposed a nonparameter-based hypothesis test method by using generalized likelihood ratio to establish the relationship between LOS and NLOS. Then it used Neyman-Pearson (NP) test method for NLOS identification. A sequential probability ratio test  which is tolerant to the parameters fluctuations is employed to identify whether the measurement contains the non-line-of-sight (NLOS) errors.