A linear regression based cost function for WSN localization

Localization with Wireless Sensor Networks (WSN) creates new opportunities for location-based consumer communication applications. There is a great need for cost functions of maximum likelihood localization algorithms that are not only accurate but also lack local minima. In this paper we present Linear Regression based Cost Function for Localization (LiReCoFuL), a new cost function based on regression tools that fulfills these requirements. With empirical test results on a real-life test bed, we show that our cost function outperforms the accuracy of a minimum mean square error cost function. Furthermore we show that LiReCoFuL is as accurate as relative location estimation error cost functions and has very few local extremes

Position estimation and tracking of an autonomous mobile sensor using received signal strength

In this paper, an algorithm for approximating the path of a moving autonomous mobile sensor with an unknown position location using Received Signal Strength (RSS) measurements is proposed. Using a Least Squares (LS) estimation method as an input, a Maximum-Likelihood (ML) approach is used to determine the location of the unknown mobile sensor. For the mobile sensor case, as the sensor changes position the characteristics of the RSS measurements also change; therefore the proposed method adapts the RSS measurement model by dynamically changing the pass loss value alpha to aid in position estimation. Secondly, a Recursive Least-Squares (RLS) algorithm is used to estimate the path of a moving mobile sensor using the Maximum-Likelihood position estimation as an input. The performance of the proposed algorithm is evaluated via simulation and it is shown that this method can accurately determine the position of the mobile sensor, and can efficiently track the position of the mobile sensor during motion.<br /

Distributed Cooperative Localization in Wireless Sensor Networks without NLOS Identification

In this paper, a 2-stage robust distributed algorithm is proposed for cooperative sensor network localization using time of arrival (TOA) data without identification of non-line of sight (NLOS) links. In the first stage, to overcome the effect of outliers, a convex relaxation of the Huber loss function is applied so that by using iterative optimization techniques, good estimates of the true sensor locations can be obtained. In the second stage, the original (non-relaxed) Huber cost function is further optimized to obtain refined location estimates based on those obtained in the first stage. In both stages, a simple gradient descent technique is used to carry out the optimization. Through simulations and real data analysis, it is shown that the proposed convex relaxation generally achieves a lower root mean squared error (RMSE) compared to other convex relaxation techniques in the literature. Also by doing the second stage, the position estimates are improved and we can achieve an RMSE close to that of the other distributed algorithms which know \textit{a priori} which links are in NLOS.Comment: Accepted in WPNC 201

The Quantum Car

I explore the use of quantum information as a security enabler for the future driverless vehicle. Specifically, I investigate the role combined classical and quantum information can have on the most important characteristic of the driverless vehicle paradigm - the vehicle location. By using information-theoretic verification frameworks, coupled with emerging quantum-based location-verification procedures, I show how vehicle positions can be authenticated with a probability of error simply not attainable in classical-only networks. I also discuss how other quantum applications can be seamlessly encapsulated within the same vehicular communication infrastructure required for location verification. The two technology enablers required for the driverless quantum vehicle are an increase in current quantum memory timescales (likely) and wide-scale deployment of classical vehicular communication infrastructure (underway). I argue the enhanced safety features delivered by the `Quantum Car' mean its eventual deployment is inevitable.Comment: 4 pages, 1 figur

Emitter Location Finding using Particle Swarm Optimization

Using several spatially separated receivers, nowadays positioning techniques, which are implemented to determine the location of the transmitter, are often required for several important disciplines such as military, security, medical, and commercial applications. In this study, localization is carried out by particle swarm optimization using time difference of arrival. In order to increase the positioning accuracy, time difference of arrival averaging based two new methods are proposed. Results are compared with classical algorithms and Cramer-Rao lower bound which is the theoretical limit of the estimation error