Impulse radio ultra-wideband ranging based on maximum likelihood estimation

• Published in: IEEE transactions on wireless communications Vol. 8; no. 12; pp. 5852 - 5861
• Main Authors: Hai Zhan, Ayadi, J., Farserotu, J., Le Boudec, J.-Y.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additive white noise; Algorithms; Amplitude estimation; Amplitudes; Applied sciences; Channels; Delay; Delay estimation; Detection, estimation, filtering, equalization, prediction; Economic models; Estimates; Estimating techniques; Exact sciences and technology; Field programmable gate arrays; Impulses; Information, signal and communications theory; Iterative algorithms; Mathematical models; Maximum likelihood; Maximum likelihood estimation; Miscellaneous; Noise reduction; pseudo-quadratic maximum likelihood; Radio; ranging; Receivers; Signal and communications theory; Signal processing; Signal, noise; Studies; Systems, networks and services of telecommunications; Telecommunications; Telecommunications and information theory; Testing; Transmission and modulation (techniques and equipments); Ultra wideband technology; UWB; Performance evaluation; Parameter estimation; High resolution; AWGN; Noise reduction; Data transmission; Iterative method; Numerical method; Optimization; Channel estimation; pseudo-quadratic maximum likelihood; Time hopping communication; Delay time; Impulse; Gradient method; UWB; Wide band transmission; Pseudo likelihood; IR; Nonlinear problems; Algorithm; Ultra wide band; ranging; Descent method; Signal processing; Distance measurement; Maximum likelihood; Quadratic estimation
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2009.12.080748

We propose a high-resolution ranging algorithm for impulse radio (IR) ultra-WideBand (UWB) communication systems in additive white Gaussian noise. We formulate the ranging problem as a maximum- likelihood (ML) estimation problem for the channel delays and amplitudes at the receiver. Then we translate the obtained delay estimates into an estimate of the distance. The ML estimation problem is a non-linear problem and is hard to solve. Some previous works focus on finding alternative estimation procedures, for example by denoising. In contrast, we tackle the ML estimation problem directly. First, 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. Second, we solve the remaining optimization problem with a gradient descent approach (pseudo-quadratic maximum likelihood (PQML) algorithm). To demonstrate the good performance of the proposed estimator, we present the numerical evaluations under the IEEE 802.15.4a channel model. We show that our algorithm performs significantly better than previously published heuristics. We also derive a reduced complexity version of the algorithm algorithm, which will be implemented on the Xinlix field-programmable gate array (FPGA) board in the future. We test the approach in a real weak line of sight (LOS) propagation environment and obtained good accuracy for the ranging.