Multipath and shadow fading are the primary cause for positioning errors in a Received Signal Strength Indicator (RSSI) based localization scheme. While fading, in general, is detrimental to localization accuracy, cross-correlation and divergence properties of shadow fading residuals may be utilized to improve localization and tracking accuracy of mobile IEEE 802.15.4 transmitters. Therefore, this paper begins by presenting a stochastic filter that models the fast changing multipath fading as a mean reverting Ornstein-Uhlenbeck (OU) process followed by a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) filtering to isolate the slow changing shadow fading residuals from measured RSSI values. Subsequently, a novel wireless transmitter localization scheme that combines the measured cross-correlation in shadow fading residuals between adjacent receivers using a Student-t Copula likelihood function is proposed. However, the long convergence time for this highly non-convex copula function might render our method unsuitable for tracking applications. Therefore, we present a faster tracking method where the velocity and heading of a mobile transmitter are estimated from α-Divergence between shadow fading signals and an onboard gyroscope respectively. To bind the localization error in this tracking method, the transmitter location estimates are smoothed by a Bayesian particle filter. The performance of our proposed localization and tracking method is validated over simulations and hardware experiments.

This paper presents a novel localization scheme in the 3D wireless domain that employs cross correlation in backscattered signal power from a cluster of radio frequency identification (RFID) tags to estimate their location. Spatially co-located RFID tags, energized by a common tag reader, exhibit correlation in their received signal strength indicator (RSSI) values. Hence, for a cluster of RFID tags, the posterior distribution of their unknown radial separation is derived as a function of the measured RSSI correlations between them. The global maxima of this posterior distribution represent the actual radial separation between the RFID tags. The radial separations are then utilized to obtain location estimates of the tags. However, due to the nonconvex nature of the posterior distribution, deterministic optimization methods that are used to solve true radial separations between tags provide inaccurate results due to local maxima, unless the initial radial separation estimates are within the region of attraction of its global maximum. The proposed RFID localization algorithm called LOCalization Using Stochastic Tunneling (LOCUST) utilizes constrained simulated annealing with tunneling transformation to solve this nonconvex posterior distribution. The tunneling transformation allows the optimization search operation to circumvent or “tunnel” through ill-shaped regions in the posterior distribution resulting in faster convergence to the global maximum. Finally, simulation results of our localization method are presented to demonstrate the theoretical conclusions.

While fading, in general, is detrimental to accurately localizing a target, fading correlation between adjacent receivers may be exploited to improve localization accuracy. Therefore, this paper presents a novel wireless localization scheme that employs a combination of cross-correlation between shadow fading noise and copula technique to recursively estimate the location of a transmitter. A stochastic filter that models multipath fading as an Ornstein-Uhlenbeck process followed by a Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) filtering is proposed to extract shadow fading residuals from measured RSSI values. Subsequently, Student-t Copula function is used to create the log likelihood function, which acts as the cost function for localization, by combining spatial shadow fading correlation arising among adjacent receivers due to pedestrian traffic in the area. Maximum Likelihood Estimate (MLE) is used for position estimation as it inherits the statistical consistency and asymptotic normality. The performance of our proposed localization method is validated over simulations and hardware experiments.

This paper presents a novel wireless localization scheme in the three dimensional domain that employs stochastic optimization with tunneling transformation to recursively estimate the location of wireless tags in a network from pair wise signal strength measurements. Spatially co-located wireless tags, receiving signals from a common transmitter, exhibit correlation in their Received Signal Strength Indicator (RSSI) values. Hence in a network of wireless tags, with pair wise correlation coefficients available, posterior distribution of the unknown tag separation is used to relatively localize them using maximum a posteriori (MAP) Estimator. However, due to the non-convex/non-tractable nature of this posterior distribution, deterministic optimization methods will end in one of the many local maxima unless the initial guess is close to the region of attraction of the global maximum. In this paper, a novel stochastic localization method called LOCalization Using Stochastic Tunneling (LOCUST) is proposed which utilizes constrained simulated annealing with tunneling transformation to solve this non-tractable posterior distribution. The tunneling transformation allows the optimization search operation to circumvent or “tunnel” through ill-shaped regions in the posterior distribution resulting in faster convergence to global maximum. Finally, simulation results of our localization method are presented.

In this paper, a sub-optimal solution to the receiver placement and number of receiver determination problem is introduced. To achieve this overall goal, first, localization error for a received signal strength indicator (RSSI)-based N-receiver system localizing a transmitter is estimated. Subsequently, this estimator error along with the 2D-tessellation techniques such as Delaunay refinement are used to position candidate receivers not only to minimize their number needed to meet the location error threshold but also to reduce the dilution of localization accuracy due to the layout of receivers. Rigorous mathematical analysis indicates that the receiver count generated by our Delaunay refinement-based sub-optimal solution using triangular tiles is indeed bounded from the optimal count by a constant which in turn depends upon the workspace layout. Finally, the sub-optimal scheme is demonstrated by using experimental data.

The fundamental cause of localization error in an indoor environment is fading and spreading of the radio signals due to scattering, diffraction, and reflection. These effects are predominant in regions where there is no-line-of-sight (NLoS) between the transmitter and the receiver. Efficient algorithms are needed to identify the subset of receivers that provide better localization accuracy. This paper introduces a new parameter called the R-Factor to indicate the extent of radial distance estimation error introduced by a receiver and to select a subset of receivers that result in better accuracy in real-time location determination systems (RTLS). In addition, it was demonstrated that location accuracy improves with R-factor reduction. Further, it was shown that for a given R-factor threshold, localization accuracy is enhanced either by increasing the number of receivers that fall below this threshold or by increasing the diversity channel count with appropriate combining of signals from diversity. Therefore, existing localization algorithms can utilize R-factor and diversity through selection combining to improve accuracy. Both analytical and experimental results are included to justify the theoretical results in terms of improvement in accuracy by using R-factor.