Passive RF Localization Based on RSSI Using Non-linear Bayesian Estimation




Palvai, Anoop Kumar

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RF localization has gained prominence because of its potential for supporting various position based applications. Passive RF Localization based on Received Signal Strength Indicator (RSSI) uses the strength of received signal from a target by passive listening to infer the range, which is subsequently used for position estimation. The thesis undertakes a study of localization techniques and addresses the problem of accuracy of position estimation. State space model developed for localization is nonlinear and hence does not have a closed form solution. Posterior density for state vector has been derived and simulated using a variant of Kalman Filter and Monte Carlo methods to obtain respective sub-optimal solutions. Least Squared Error approach tries to obtain an estimate that minimizes the squared error whereas and does not reveal any statistical information about the target location. Extended Kalman filter approach tries to estimate the posterior density of target employing approximations of Gaussian state probability distribution and linear state space model and observed to provide better results compared to that of Least Squared Error approach. As the localization model is nonlinear, Extended Kalman filter approximates it with a linear one by employing Taylor series approximation and if the nonlinearity is severe the accuracy of the algorithm suffers. Particle filter approach also tries to estimate the state posterior density with no restrictions and hence is applicable for any generalized system. In this approach probability density function is approximated using a weighted set of particles drawn using Monte Carlo methods and will enable in computing the all the moments of distribution. Recursive Least Squared Error, Extended Kalman and Sampling Importance Resampling Particle Filtering algorithms are designed for localization and their performances are compared. The performance of Particle filter using Sampling Importance Resampling algorithm is found to be superior to that of Recursive Least Squared Error approach and Extended Kalman filter.



Localization, Particle Filter, RSSI, Extended Kalman Filter, Non-Liner Bayesian Estimation, Sequential Importance Sampling