Traditional received signal strength indicators (RSSI‘s) based moving target localization and tracking using wireless sensor networks (WSN‘s) generally employs lateration/angulation techniques. Although this method is a very simple technique but it creates significant errors in localization estimations due to nonlinear relationship between RSSI and distance. The Generalized Regression Neural Network (GRNN) being a one-pass learning algorithm is well known for its ability to train quickly on sparse data sets. This paper proposes an implementation of GRNN as an alternative to this traditional RSSI based approach, to obtain first location estimates of single target moving in 2-D in WSN, which are then further refined using kalman filtering (KF) framework. Two algorithms namely, GRNN + kalman filter (KF) and GRNN + unscented kalman filter (UKF) are proposed in this research work. The GRNN is trained with the simulated RSSI values received at moving target from beacon nodes and the corresponding actual target 2-D locations. The precision of the proposed algorithms are compared against traditional RSSI based, GRNN based approach as well as other models in the literature such as traditional RSSI + KF and traditional RSSI + UKF algorithms. The proposed algorithms demonstrate superior tracking performance (tracking accuracy in the scale of few centimeters) irrespective of nonlinear system dynamics as well as environmental dynamicity.

Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.

Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signal strength is dynamicity in target motion and surrounding environment. In addition, the significant localization error is induced during each iteration step during trilateration, which gets propagated in the next iterations. To address this problem, this article presents an improved trilateration-based architecture named Trilateration Centroid Generalized Regression Neural Network. The proposed Trilateration Centroid Generalized Regression Neural Network–based localization algorithm inherits the simplicity and efficiency of three concepts namely trilateration, centroid, and Generalized Regression Neural Network. The extensive simulation results indicate that the proposed Trilateration Centroid Generalized Regression Neural Network algorithm demonstrates superior localization performance as compared to trilateration, and Generalized Regression Neural Network algorithm.

Moving target localization & tracking has been currently widely addressed area of research because of its lot of significance in broad varying applications. The major approaches for target localization & tracking in a defined WSN are Range Based, Range Free, Prediction Filter based Approaches aimed at achieving either Localization & Tracking accuracy or Energy Efficiency or Security or combination of these depending upon application at hand. Besides other range based techniques (TOA, TDOA, AOA) aimed at localization/tracking, RSSI-based approach is most widely used in spite of various challenges associated with it. The most important reason is that it does not need the extra hardware as most of electronic gadgets have built-in RSSI Circuitry to read RSS. By exploiting this RSSI measurement, one can find distance between transmitter & receiver using a suitable mathematical model of signal attenuation. However due to high dynamicity of wireless environment, the RSSI measurements are notoriously unpredictable. This paper discusses the RSSI based localization & tracking problem in detail & also surveys various other solutions proposed by the research community in the domain. Recent strategies to improve localization accuracy & tracking accuracy in RSSI based systems especially for dynamic environments & open research issues are also discussed as well.

Maneuvering target tracking is recent hot topic in location based services. In this paper a kalman filtering based approach of mobile target tracking with constant acceleration motion, is presented to deal with uncertainties in measurement noises and abrupt changes in target velocity. Two algorithms namely, RSSI + kalman filter (KF) and RSSI + unscented kalman filter (UKF) are proposed to refine estimates of traditional RSSI based approach to obtain a smoothed target trajectory. The performance of the proposed algorithms are validated over simulated RSSIs and compared with traditional RSSI based approach in the context of tracking accuracy. The simulation results of proposed algorithms show the tracking accuracy of the order of centimeter scale, irrespective of dynamicity in RSSIs due hostile wireless environment.