A Survey on Wireless Position Estimation <s> Mapping Techniques <s> From the Publisher: ::: Wireless Location in CDMA Cellular Radio Systems investigates methods for wireless location in CDMA networks and analyzes their performances. Techniques for measuring location parameters (AoAs, ToAs, etc.) are presented along with algorithms for calculating position from those parameters. Several impairments to accurate location are covered and analyzed including multipath propagation, non-line-of-sight propagation, and multiple-access interference. Many of the topics in this book are also applicable to FDMA- and TDMA-based communication networks. <s> BIB001 </s> A Survey on Wireless Position Estimation <s> Mapping Techniques <s> The location of mobile terminals has recently become a topic of much research. One of the more popular methods of radio location is the time difference of arrival (TDoA) method where the location of the mobile terminal is estimated using measurements of the differences in propagation times from the mobile terminal to three or more base stations. The traditional approach used to perform the location estimation is the least squares algorithm assuming zero mean Gaussian measurement error distributions. In the urban microcell radio propagation environment with non line of sight (NLOS) propagation, this assumption is often violated causing large location estimation errors. This paper demonstrates an estimation algorithm based on non-parametric density approximation of the measurements using a survey of time difference measurements. This method gives high accuracy location estimates even in the presence of NLOS propagation. <s> BIB002 </s> A Survey on Wireless Position Estimation <s> Mapping Techniques <s> Recently, there has been much interest in accurate determination of mobile user locations in cellular environments. A general approach to this geolocation problem is to gather measurements from a number of base stations and to estimate user locations using a least squares approach [1]. However, in non-line-of-sight (NLOS) situations, measurements are significantly biased. Hence, very large errors in location estimation may be introduced. In this paper, an approach developed in learning theory, namely, Support Vector Regression (SVR), is applied to the geolocation problem with the addition of KalmanBucy filtering to smooth location estimates in a mobile tracking scenario. <s> BIB003 </s> A Survey on Wireless Position Estimation <s> Mapping Techniques <s> The location of people, mobile terminals and equipment is highly desirable for operational and safety enhancements in the mining industry. In an indoor environment such as a mine, the multipath caused by reflections, diffraction and diffusion on the rough sidewall surfaces, and the non-line of sight (NLOS) due to the blockage of the shortest path between transmitter and receiver are the main sources of range measurement errors. Due to the harsh mining environment, unreliable measurements of location metrics such as RSS, AOA and TOA/TDOA result in the deterioration of the positioning performance. Hence, alternatives to the traditional parametric geolocation techniques have to be considered. In this paper, we present a novel method for mobile station location using wideband channel measurement results applied to an artificial neural network (ANN). The proposed system, the Wide Band Neural Network-Locate (WBNN-Locate), learns off-line the location 'signatures' from the extracted location-dependent features of the measured channel impulse responses data for LOS and NLOS situations. It then matches on-line the observation received from a mobile station against the learned set of 'signatures' to accurately locate its position. The location accuracy of the proposed system, applied in an underground mine, has been found to be 2 meters for 90% and 80% of trained and untrained data, respectively. Moreover, the proposed system may also be applicable to any other indoor situation and particularly in confined environments with characteristics similar to those of a mine (e.g. rough sidewalls surface). <s> BIB004
The main idea behind position estimation via mapping techniques is to determine a regression scheme based on a set of training data, and then to estimate position of a given node according to that regression function. Let the training data be expressed as where l i is the position (location) vector for the ith training data, which is given by l i = [x i y i ] T for two-dimensional positioning, m i represents the vector of estimated parameters for the ith position, and N T is the total number of elements in the training set (i.e., "size" of the database). Depending on signal parameters employed in the positioning algorithm, m i consists of a number of position related parameters related to the reference nodes; e.g., each element of m i can be an RSS estimate at a reference node when the target node is at location l i . Given the training set in (11), a mapping technique first determines a position estimation rule (pattern matching algorithm/regression function), and then estimates the position l of a given target node based on a parameter vector m related to that target node. Some common mapping techniques employed in position estimation include k-nearest-neighbor (k-NN) estimation, support vector regression (SVR) and neural networks BIB004 , BIB002 , BIB003 ,  ,  . Due to its simplicity, the k-NN estimation technique is considered in this section in order to provide intuition on mapping based position estimation. In its simplest form, the k-NN estimation technique estimates the position of the target node as the position vector in the training set T corresponding to the parameter vector that has the shortest distance to the given (estimated) parameter vector m. That is, the position is estimated as l j , where with m − m i representing the Euclidean distance between m and m i . This scheme is called the 1-NN, or simply the NN, estimation technique. In general, the k-NN scheme estimates the position of the target node according to the k parameter vectors in T that have the smallest distances to the given parameter vector m. The position estimatel is obtained by a weighted sum of the positions corresponding to those nearest parameter vectors; i.e.,l where l . Various weighting functions can be employed, as studied in BIB002 . For example, for the uniform weighting scheme, the position estimate is the sample mean of the positions l BIB001 , . . . , l (k) ; i.e., The main advantage of mapping techniques is that they can provide very accurate position estimation in challenging environments with multipath and NLOS propagation. In other words, they have a certain degree of inherent robustness against undesired propagation conditions. However, the main disadvantage is the requirement that the training database should be large enough and representative of the current environment for accurate position estimation. In other words, the database should be updated frequently enough so that the channel characteristics in the training and position estimation phases do not differ significantly. Such an update requirement can be very costly for positioning systems operating in dynamic environments, such as for an outdoor positioning system.