On localization and tracking using received signal strength measurements /

Yılmaz, Alptekin
In this study, first, some received signal strength (RSS) based localization techniques, including maximum likelihood estimation (MLE), multidimensional scaling (MDS) and weighted least squares (WLS), are investigated and compared to each other via a simulation study within the perspective of a collaborative localization scenario. MLE using RSS measurement model, called RSS-MLE is known in the literature to be significantly biased. An important observation of this work is that the aforementioned bias can be clearly reduced in some collaborative localization scenarios when the non-connectivity information is incorporated into maximum likelihood (ML) cost function. We refer to the ML algorithm including the non-connectivity information as hybrid RSS-MLE (h-RSS-MLE). In order to support the reduced bias observation and determine the conditions in which h-RSS-MLE can mitigate the bias, we derive an analytical expression for the bias of the ML estimator based on a second order Taylor series expansion of MLE cost function by incorporating connectivity constraints into the problem. Since this analysis gives results which do not match the simulation results in a 2-D scenario, we also derive another expression based on a Taylor series expansion of the RSS measurements. The latter analysis is validated under some 2-D non-collaborative localization scenarios through a simulation study for MLE optimized by a grid-search. Finally, we make simulations as well as an experimental study to compare the localization algorithms with some conventional tracking methods including Kalman filters and a particle filter. It is observed in the experiments that the tracking methods can increase the accuracy about one meter compared to the localization algorithms for a non-collaborative case.


Indoor localization and tracking based on RSSI and accelerometer measurements
Doğan, Melih; Yılmaz, Ali Özgür; Orguner, Umut; Department of Electrical and Electronics Engineering (2015)
In this study, first, received signal strength (RSS) based indoor localization and tracking techniques including maximum likelihood estimation (MLE), Kalman Filter (KF), serial and parallel extended Kalman Filter (EKF) are investigated and their performances compared to each other via a simulation study. Later, sensor fusion with RSS and inertial measurement unit (IMU) for target tracking is discussed to improve accuracy of RSS-based tracking by using KF and EKF as fusion algorithms. Effects of channel para...
Performance of a non-linear adaptive beamformer algorithm for signal-of-interest extraction /
Oğuz, Özkan; Tuncer, Temel Engin; Department of Electrical and Electronics Engineering (2015)
In this thesis a non-linear adaptive beamforming technique, Adaptive Projections Subgradient Method [1] (APSM) is considered. This method uses projections over convex sets in Reproducing Kernel Hilbert Space. Main advantage of this method is observed if the signal-of-interest is due to digital modulation and when there are more jammers than the number of antennas. The performance of this non-linear beamforming technique is compared with well-known methods including Minimum Variance Distortionless Response [...
An Experimental investigation on indoor RSSI-based localization
Karakurt, Meriç Koray; Yılmaz, Ali Özgür; Department of Electrical and Electronics Engineering (2013)
In this study, received signal strength indicator (RSSI) based indoor localization is investigated in sparse-anchor-deployment environments. Multipath propagation, dynamical variations in propagation model parameters and antenna patterns which are three of many potential error sources of indoor RSSI-based localization are experimentally analyzed. Possible enhancements so as to minimize the effects of mentioned problems are examined. A multichannel maximum-likelihood estimation (MLE) algorithm is proposed an...
On the Eigenstructure of DFT Matrices
Candan, Çağatay (Institute of Electrical and Electronics Engineers (IEEE), 2011-03-01)
The discrete Fourier transform (DFT) not only enables fast implementation of the discrete convolution operation, which is critical for the efficient processing of analog signals through digital means, but it also represents a rich and beautiful analytical structure that is interesting on its own. A typical senior-level digital signal processing (DSP) course involves a fairly detailed treatment of DFT and a list of related topics, such as circular shift, correlation, convolution operations, and the connectio...
A Comparison of sparse signal recovery and approximate bayesian inference methods for sparse channel estimation
Uçar, Ayla; Candan, Çağatay; Department of Electrical and Electronics Engineering (2015)
The concept of sparse representation is one of the central methodologies of modern signal processing and it has had significant impact on numerous application fields such as communications and imaging. Sparsity expresses the idea that the information rate of a continuous time signal may be much smaller than suggested by its bandwidth, or that a discrete time signal depends on a number of degrees of freedom which is comparably much smaller than its (finite) length. With recent advances in sparse signal estim...
Citation Formats
A. Yılmaz, “On localization and tracking using received signal strength measurements /,” M.S. - Master of Science, Middle East Technical University, 2015.