Distributed localization of a RF target in NLOS environments

We propose a novel distributed expectation maximization (EM) method for non-cooperative RF device localization using a wireless sensor network. We consider the scenario where few or no sensors receive line-of-sight signals from the target. In the case of non-line-of-sight signals, the signal path consists of a single reflection between the transmitter and receiver. Each sensor is able to measure the time difference of arrival of the target's signal with respect to a reference sensor, as well as the angle of arrival of the target's signal. We derive a distributed EM algorithm where each node makes use of its local information to compute summary statistics, and then shares these statistics with its neighbors to improve its estimate of the target localization. Since all the measurements need not be centralized at a single location, the spectrum usage can be significantly reduced. The distributed algorithm also allows for increased robustness of the sensor network in the case of node failures. We show that our distributed algorithm converges, and simulation results suggest that our method achieves an accuracy close to the centralized EM algorithm. We apply the distributed EM algorithm to a set of experimental measurements with a network of four nodes, which confirm that the algorithm is able to localize a RF target in a realistic non-line-of-sight scenario.Comment: 30 pages, 11 figure

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Abstract In this paper, we proposed a novel location estimation algorithm based on the concept of space-time signature matching in a moving target environment. In contrast to previous fingerprint-based approaches that rely on received signal strength (RSS) information only, the proposed algorithm uses angle, delay, and RSS information from the received signal to form a signature, which in turn is utilized for location estimation. We evaluated the performance of the proposed algorithm in terms of the average probability of error and the average error distance as a function of target movement. Simulation results confirmed the effectiveness of the proposed algorithm for location estimation even in moving target environment

Applying Rprop Neural Network for the Prediction of the Mobile Station Location

Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor

AML algorithm and NLOS localization by AoA measurements.

Tao Suyi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 51-53).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.2Chapter 1.1.1 --- Mobile Phone Applications --- p.3Chapter 1.1.2 --- Location Methods --- p.4Chapter 1.1.3 --- Location Algorithms --- p.9Chapter 1.2 --- AoA Localization --- p.10Chapter 1.3 --- The NLOS Problem --- p.11Chapter 2 --- AoA Localization --- p.13Chapter 2.1 --- Conventional Approach to AoA Localization --- p.14Chapter 2.2 --- Least Squares Approach to AoA Localization --- p.16Chapter 2.2.1 --- Ordinary Least Squares Approach (OLS) by Pages-Zamora --- p.16Chapter 2.2.2 --- The Weighted Least Squares Approach (WLS) --- p.18Chapter 2.3 --- Approximate Maximum Likelihood Method (AML) for AoA Localization --- p.19Chapter 2.4 --- Simulations --- p.21Chapter 3 --- Analysis and Mitigation of NLoS Effects --- p.26Chapter 3.1 --- The Non-Line-of-Sight (NLoS) Effects --- p.26Chapter 3.2 --- NLoS Mitigation in AoA Localization --- p.27Chapter 3.2.1 --- A Selective Model to Suppress NLOS Errors --- p.27Chapter 3.2.2 --- Dimension Determination and LOS Identification --- p.29Chapter 3.3 --- Simulations --- p.34Chapter 3.3.1 --- Experiment 1 --- p.34Chapter 3.3.2 --- Experiment 2 --- p.38Chapter 4 --- Conclusions and Suggestions for Future Work --- p.42Chapter 4.1 --- Conclusions --- p.42Chapter 4.2 --- Suggestions for future work --- p.44Chapter A --- Derivation of the Cramer Rao Lower Bound (CRLB) for AoA Localization --- p.45Chapter A.1 --- CRLB for all LoS --- p.45Chapter A.2 --- CRLB for both LoS and NLoS --- p.46Chapter B --- Derivation of the Error Covariance for OLS and WLS Estima- tors --- p.48Chapter B.1 --- Error Covariance for OLS Estimator --- p.49Chapter B.2 --- Error Covariance for WLS Estimator --- p.50Bibliography --- p.5