A Survey on Wireless Position Estimation <s> Introduction <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> Introduction <s> The most common methods for location of communications or radar transmitters are based on measuring a specified parameter such as signal angle of arrival (AOA) or time of arrival (TOA). The measured parameters are then used to estimate the transmitter location. Since the AOA/TOA measurements are done at each base station separately, without using the constraint that all measurements must correspond to the same transmitter, they are suboptimal. We propose a technique that uses exactly the same data as the common methods, except that the estimation of location is based on exact maximum likelihood, and the location determination is direct. Although there are many stray parameters, including the attenuation coefficients and the signal waveform, the method requires only a two-dimensional search. Monte Carlo simulations indicate that the accuracy is equivalent to AOA, TOA, and their combination for high SNR, while for low SNR, the accuracy of the proposed method is superior. <s> BIB002 </s> A Survey on Wireless Position Estimation <s> Introduction <s> UWB technology provides an excellent means for wireless positioning due to its high resolution capability in the time domain. Its ability to resolve multipath components makes it possible to obtain accurate location estimates without the need for complex estimation algorithms. In this article, theoretical limits for TOA estimation and TOA-based location estimation for UWB systems have been considered. Due to the complexity of the optimal schemes, suboptimal but practical alternatives have been emphasized. Performance limits for hybrid TOA/SS and TDOA/SS schemes have also been considered. Although the fundamental mechanisms for localization, including AOA-, TOA-, TDOA-, and SS-based methods, apply to all radio air interface, some positioning techniques are favored by UWB-based systems using ultrawide bandwidths. <s> BIB003 </s> A Survey on Wireless Position Estimation <s> Introduction <s> A process for drying organic waste, such as sewage sludge, by contacting the organic waste with hot vapors wherein the dried material is plasticized and extruded to form compacted granules having good flow characteristics and suitable for application by commercial fertilizer spreaders. A plasticizer and an extrusion aid are added to the dried material prior to extrusion. <s> BIB004
Recently, the subject of positioning in wireless networks has drawn considerable attention. With accurate position estimation, a variety of applications and services such as enhanced-911, improved fraud detection, location sensitive billing, intelligent transport systems and improved traffic management can become feasible for cellular networks BIB001 . For short-range networks, on the other hand, position estimation facilitates applications such as inventory tracking, intruder detection, tracking of fire-fighters and miners, home automation and patient monitoring [2] . These potential applications of wireless positioning have also been recognized by the IEEE, which set up a standardization group 802.15.4a for designing a new physical layer for low data rate communications combined with positioning capabilities  . Also, the Federal Communications Commission (FCC) in the U.S. has required wireless providers to locate mobile users within tens of meters for emergency 911 calls  . In order to realize potential applications of wireless positioning, accurate estimation of position should be performed even in challenging environments with multipath and non-line-of-sight (NLOS) propagation BIB003 . For accurate position estimation, the details of the position estimation process and its theoretical limits should be well-understood. Position estimation can be defined as the process of estimating the position of a node 1 , called the "target" node, in a wireless network by exchanging signals between the target node and a number of reference nodes 2 . The position of the target node can be estimated by the target node itself, which is called self-positioning, or it can be estimated by a central unit that obtains information via the reference nodes, which is called remote-positioning (network-centric positioning)  . Also, depending on whether the position is estimated from the signals traveling between the nodes directly or not, two different position estimation schemes can be considered. Direct positioning refers to the case in which the position estimation is performed directly from the signals traveling between the nodes BIB002 . On the other hand, two-step positioning extracts certain signal parameters from the signals first, and then estimates the position based on those signal parameters ( Figure 1 ). Although the two-step positioning is suboptimal in general, it can have significantly lower complexity than the direct approach. Also, the performance of the two approaches are usually very close for sufficiently high signal bandwidths and/or signal-to-noise ratios (SNRs) BIB004 , BIB002 . Therefore, the two-step positioning is the common technique in most positioning systems, which is the main focus of this paper. In the first step of a two-step positioning algorithm, signal parameters, such as time-of-arrival (TOA) and received signal strength (RSS), are estimated. Then, in the second step, the position of the target node is estimated based on the signal parameters obtained in the first step, as shown in Fig-4 Sinan Gezici A signal traveling from one node to another experiences fast (multipath) fading, shadowing and path-loss  . Ideally, averaging RSS over a sufficiently long time interval excludes the effects of multipath fading and shadowing, which results in the following model BIB003 : where n is the path loss exponent,P (d) is the average received power in dB at a distance d, and P 0 is the received power in dB at a short reference distance d 0 . In practice, the observation interval is not long enough to mitigate the effects of shadowing. Therefore, the received power is commonly modeled to include both path-loss and shadowing effects, the latter of which are modeled as a zero mean Gaussian random variable with a variance of σ 2 sh in the logarithmic scale. Therefore, the received power P (d) in dB can be expressed as whereP (d) is as given in BIB001 . Note that this model can be used in both lineof-sight (LOS) and NLOS scenarios with an appropriate choice of channel parameters. From the received power model in (2), the CRLB for unbiased distance estimators can be expressed as  Var{d} ≥ (ln 10) whered represents an unbiased estimate for the distance d. From (3), it is observed that the RSS estimates get more accurate as the standard deviation of the shadowing decreases, since RSS estimates vary less around the true average power in that case. Also a larger path-loss exponent results in a smaller lower bound, as the average power becomes more sensitive to distance for larger n. Finally, the accuracy deteriorates as the distance between the nodes increases. 