Impulse Radio Ultra-WideBand ranging and localization for sensor networks

In this thesis, we focus on Impulse Radio (IR) Ultra-WideBand (UWB) ranging and positioning techniques under indoor propagation environments. IR-UWB, a new carrierless communication scheme using impulses, is a candidate technology for future communication, ranging and positioning applications. Recent progress on both the technical and regulatory side of this technology has made this possible [1][2][3]. The fine time resolution of UWB signals has created a vision of novel ranging and positioning applications to augment existing narrowband systems operating in dense multipath environments [4][5][6]. We propose a high-resolution IR-UWB ranging algorithm based on Maximum Likelihood (ML) when the noise is additive Gaussian noise or multi-user interference. First, we pose the ranging problem as an ML estimation problem for the channel delays and their amplitudes at the receiver. We evaluate the ranging by translating the received delay estimates into an estimate of the distance. Then, we use the same transformation as the first step of Iterative Quadratic Maximum Likelihood (IQML) and we transform the ML problem into another optimization problem that avoids the estimation of the amplitude coefficients. We solve the remaining optimization problem with a gradient descent approach (Pseudo-Quadratic Maximum Likelihood (PQML) algorithm). Most previous works assume the distribution of the targeted distance is a uniform distribution (for example [7], [8] and [9]). In contrast to the previous works, we propose that the distribution of the targeted distance, which is not necessarily a uniform distribution, should depend on the geometry of the indoor environments of interest. We propose a Bayesian detection algorithm where the prior distribution of the channel follows the IEEE 802.15.4a channel model to identify whether the received signal is Line-Of-Sight (LOS) signal or a Non-Line-Of-Sight (NLOS) signal. If it is a LOS signal, we use a Bayesian estimation approach to estimate the joint posterior probability density function (pdf) of the channel and the targeted distance. We use this pdf function to estimate the ranging with Minimum Mean Square Error Estimator (MMSE). For computing the joint posterior pdf of the channel and the targeted distance, we derive a novel algorithm based on Sampling and Importance Resampling (SIMR) and Expectation Maximum (EM) techniques. Furthermore, we propose a reduced-complexity architecture of an IR-UWB ranging system by using our proposed algorithms. We also implement the Bayesian ranging algorithm on the Xinlix ML410 Field-Programmable Gate Array (FPGA) board. The used FPGA resources show that the Bayesian ranging algorithm is a low-complexity algorithm. We derive a novel Ziv-Zakai lower bound for IR-UWB ranging error that depends on the geometry of the indoor environments of interest. In contrast to the work in [7], we do not introduce, during our derivation process, any approximation for our log-likelihood function. Therefore,