A Survey of Localization in Wireless Sensor Network <s> NLOS Mitigation. <s> Taylor-series estimation gives a least-sum-squared-error solution to a set of simultaneous linearized algebraic equations. This method is useful in solving multimeasurement mixed-mode position-location problems typical of many navigational applications. While convergence is not proved, examples show that most problems do converge to the correct solution from reasonable initial guesses. The method also provides the statistical spread of the solution errors. <s> BIB001 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Mitigation. <s> This paper presents neural network based models for the prediction of propagation path loss in urban environment. The neural networks are designed separately for line-of-sight (LOS) and non-line-of-sight (NLOS) cases. The performance of the neural models is compared to that of the COST231-Walfisch-Ikegami model, the Walfisch-Bertoni model and the single regression model, based on the absolute mean error, standard deviation and the root mean squared error between predicted and measured values. <s> BIB002 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Mitigation. <s> In this paper some wireless location experiments using CDMA pilot channels are carried out and an estimation technique to improve its positioning accuracy is proposed. Together with the GPS (global positioning system), lots of positioning technologies using existing infrastructure, that is, mobile communication networks, are being researched. Because of the shortage of hearable signals, multipath, and NLOS (non-line-of-sight) error, however, CDMA positioning has low positioning accuracy by raw pilot measurements only. In this paper, we introduce an adequate two-stage filter to treat such path-loss errors and develop a path-loss error mitigation algorithm. We apply these to the real positioning system and carry out experiments in the dense urban area. Friedland (1969) proposed the optimal filter to reject unknown bias that is difficult to be estimated. This paper shows a design rule that the filter can be applied to CDMA location and make the positioning accuracy better. <s> BIB003 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Mitigation. <s> We present a novel approach for reducing the effect of non-line-of-sight (NLOS) error that inhibits accurate location of wireless mobile stations (MSs). The location algorithm is developed based on the distribution of times of arrival (TOAs) generated from multipath scattering models that have been published in the literature. By matching the statistics of the measured TOAs of several multipath arrivals with those produced by the scattering models, estimates of the line-of-sight (LOS) TOAs between each base station (BS) and the MS are obtained. The LOS TOAs can then be used in any traditional TOA location algorithm. The algorithm shows a significant improvement in location accuracy for a TOA system in NLOS environment. <s> BIB004 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Mitigation. <s> We present an algorithm for mitigating the non-line-of-sight (NLOS) error that corrupts time of arrival (TOA), or range, measurements. The method uses the available data regarding the ranges and the base station (BS) layout, in conjunction with cell geometry, to adjust the measured ranges to near their true line-of-sight (LOS) values. The range adjustments are obtained by scaling the NLOS ranges using scale factors that are estimated from a constrained nonlinear optimization problem. <s> BIB005 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Mitigation. <s> In nonline-of-sight propagation (NLOS) environment, the time of arrival (TOA) measurement is biased by a positive NLOS noise, causing mobile location errors. In order to mitigate the NLOS influence on the location accuracy, a new method is proposed. By discussing the probability density function of NLOS error based on ring of scatterers model, a TOA reconstruction method is introduced. By using this method, the influence of the NLOS error can be reduced to some extent, which is demonstrated by simulation under different channel environments. Compared with the conventional LS algorithms, it is shown that the proposed method can significantly improve the location accuracy under bad channel environments. <s> BIB006 </s> A Survey of Localization in Wireless Sensor Network <s> NLOS Mitigation. <s> Mobile location estimation has attracted much interest over the past few years. The most challenging issues, which render to reach the required accuracy for the time-based location system, are multipath and non line-of-sight (NLOS) problems. This paper suggests the simple but robust techniques using biased Kalman filter to smooth and mitigate the NLOS effect for TOA measurements. The processed TOAs are then used for DTOA formulation and provided for location estimation. The further tracking stage is shown not to improve the accuracy much but to be necessary to smooth the mobile trajectory. The better accuracy for mobile location is suggested for future work by using the geographical information through searching the match between the path loss measured at multiple BSs and that estimated by ray-tracing techniques. <s> BIB007
Because of the existence of the nonideal channel condition and non-line-of-sight transmission between the unknown nodes and beacon nodes, NLOS error mitigation has become a key technology and hotspot in the research about location estimation in wireless sensor network. The first way attempts to identify the propagation conditions (LOS or NLOS) and then eliminate the measurements in NLOS; they only use the measurements in LOS to locate the unknown node. The propagation modelbased method BIB004 BIB006 either directly employs the existing propagation models or empirically develops a model based on experimental results. The second way uses all NLOS and LOS measurements to estimate the location, but provides weighting or sealing to minimize the effects of the NLOS contributions. The weighting is determined by either the position geometry and beacon nodes layout or the residuals (fitting errors) of individual beacon node. The Taylor series linearization BIB001 (TS-LS), a widely used localization algorithm, should have the prior information of the error statistics which can be used to determine the weights. Chen  develops an algorithm to mitigate the NLOS errors by residual weighting when the range measurements corrupted by NLOS errors are not identifiable. The hypothesis testing  is employed to detect whether the environment is NLOS or LOS along with time of arrival (TOA) and received signal strength (RSS) measurements. And then an extended Kalman filter is used to nonlinear estimation. In a scattering environment, most of the propagation paths between the unknown nodes and beacon nodes are NLOS; the constrained optimization techniques are used to reduce NLOS errors BIB005 . The neural network is employed to predict the NLOS error BIB002 ; Kalman filters BIB007 and modified two-stage Kalman filter BIB003 are used to correct NLOS measurements. All the positioning algorithms in NLOS environment focused more on the cellular network. These methods could be used in some scenarios for wireless sensor network localization. Fewer methods investigate the NLOS mitigation algorithm for Wireless sensor network.