Tarig Ballal

High-Accuracy Reference-Free Ultrasonic Location Estimation

This paper presents a novel reference-free ultrasonic indoor location system. Unlike most previous proposals, the mobile device (MD) determines its own position based only on ultrasonic signals received at a compact sensor array and sent by a fixed independent beacon. No radio frequency or wired timing reference signal is used. Furthermore, the system is privacy aware and one way in that the receive-only MD determines its own position based on ultrasonic signals received from fixed transmit-only beacons. The MD uses a novel hybrid angle of arrival (AoA)-time of flight (ToF) with timing lock algorithm to determine its location relative to the beacons with high accuracy. The algorithm utilizes an AoA-based location method to obtain an initial estimate of its own location. Based on this, it estimates the timing offsets (TOs) between the MD clock and the beacon transmissions. The average TO and the known periodicities of the beacon signals are then used to obtain a second more accurate MD location estimate via a ToF method. The system utilizes wideband spread spectrum ultrasonic signaling in order to achieve a high update rate and robustness to noise and reverberation. A prototype system was constructed, and the algorithm was implemented in software. The experimental results show that the method provides 3-D accuracy better than 9.5 cm in 99% of cases, an 80% accuracy improvement over the conventional AoA-only method.

Phase-Difference Ambiguity Resolution for a Single-Frequency Signal

The problem of ambiguity in the phase-difference of a signal received by widely spaced receivers is considered. It is shown that a collinear receiver triplet with a specific configuration combined with a proposed algorithm can be utilized for phase-difference disambiguation. The identifiability condition is that the difference of the two smaller inter-receiver spacings is not greater than a half-wavelength of the impinging signal and is greater than zero. The effect of the emitter location relative to the receiver array and the effect of noise are studied. Analytic formulae for the mean-square error (MSE) under Gaussian white noise are obtained and are used to directly determine performance versus signal-to-noise ratio (SNR) given the emitter location and the receiver configuration. Performance is found to exhibit an SNR threshold effect that depends on the emitter location and the sensor configuration. The analytic performance predictions are found to be close to the performance obtained in simulation.

Phase-Difference Ambiguity Resolution for a Single-Frequency Signal in the Near-Field Using a Receiver Triplet

In this letter, we propose a novel method to disambiguate the phase-difference of a single-frequency signal observed between a pair of spatially separated sensors, with inter-sensor spacing exceeding half the wavelength (lambda/2) of the signal. We mathematically prove that, in a noiseless case, the true phase-difference can unambiguously be estimated utilizing a third collinear sensor, provided that the absolute difference between the two smaller inter-sensor spacings is less than lambda/2. The performance of the method is characterized by estimating the probability of failure in noisy cases.

Constrained Perturbation Regularization Approach for Signal Estimation Using Random Matrix Theory

In this work, we propose a new regularization approach for linear least-squares problems with random matrices. In the proposed constrained perturbation regularization approach, an artificial perturbation matrix with a bounded norm is forced into the system model matrix. This perturbation is introduced to improve the singular-value structure of the model matrix and, hence, the solution of the estimation problem. Relying on the randomness of the model matrix, a number of deterministic equivalents from random matrix theory are applied to derive the near-optimum regularizer that minimizes the mean-squared error of the estimator. Simulation results demonstrate that the proposed approach outperforms a set of benchmark regularization methods for various estimated signal characteristics. In addition, simulations show that our approach is robust in the presence of model uncertainty.

Improved linear least squares estimation using bounded data uncertainty

This paper addresses the problemof linear least squares (LS) estimation of a vector x from linearly related observations. In spite of being unbiased, the original LS estimator suffers from high mean squared error, especially at low signal-to-noise ratios. The mean squared error (MSE) of the LS estimator can be improved by introducing some form of regularization based on certain constraints. We propose an improved LS (ILS) estimator that approximately minimizes the MSE, without imposing any constraints. To achieve this, we allow for perturbation in the measurement matrix. Then we utilize a bounded data uncertainty (BDU) framework to derive a simple iterative procedure to estimate the regularization parameter. Numerical results demonstrate that the proposed BDU-ILS estimator is superior to the original LS estimator, and it converges to the best linear estimator, the linear-minimum-mean-squared error estimator (LMMSE), when the elements of x are statistically white.

Low-Complexity Bayesian Estimation of Cluster-Sparse Channels

This paper addresses the problem of channel impulse response estimation for cluster-sparse channels under the Bayesian estimation framework. We develop a novel low-complexity minimum mean squared error (MMSE) estimator by exploiting the sparsity of the received signal profile and the structure of the measurement matrix. It is shown that, due to the banded Toeplitz/circulant structure of the measurement matrix, a channel impulse response, such as underwater acoustic channel impulse responses, can be partitioned into a number of orthogonal or approximately orthogonal clusters. The orthogonal clusters, the sparsity of the channel impulse response, and the structure of the measurement matrix, all combined, result in a computationally superior realization of the MMSE channel estimator. The MMSE estimator calculations boil down to simpler in-cluster calculations that can be reused in different clusters. The reduction in computational complexity allows for a more accurate implementation of the MMSE estimator. The proposed approach is tested using synthetic Gaussian channels, as well as simulated underwater acoustic channels. Symbol-error-rate performance and computation time confirm the superiority of the proposed method compared to selected benchmark methods in systems with preamble-based training signals transmitted over cluster-sparse channels.

Low-sampling-rate ultra-wideband digital receiver using equivalent-time sampling

In this paper, we propose an all-digital scheme for ultra-wideband symbol detection. In the proposed scheme, the received symbols are sampled many times below the Nyquist rate. It is shown that when the number of symbol repetitions, P, is co-prime with the symbol duration given in Nyquist samples, the receiver can sample the received data P times below the Nyquist rate, without loss of fidelity. The proposed scheme is applied to perform channel estimation and binary pulse position modulation (BPPM) detection. Results are presented for two receivers operating at two different sampling rates that are 10 and 20 times below the Nyquist rate. The feasibility of the proposed scheme is demonstrated in different scenarios, with reasonable bit error rates obtained in most of the cases.

GNSS instantaneous ambiguity resolution and attitude determination exploiting the receiver antenna configuration

A novel instantaneous method for Global Navigation Satellite System (GNSS) attitude determination using a new phase-difference ambiguity resolution approach is presented. A triple-antenna receiver configuration with baseline constraints is exploited for ambiguity resolution. It is shown that the ambiguity resolution and attitude determination problems can be solved using simple closed and semiclosed form solutions, without using GNSS codes. Simulation results demonstrate high success rates (>90%) in most cases, even when the number of visible satellite vehicles is small.

Low-complexity Wireless Monitoring of Respiratory Movements Using Ultra-wideband Impulse Response Estimation

Abstract In this paper, we present a comprehensive scheme for wireless monitoring of the respiratory movements in humans. Our scheme overcomes the challenges low signal-to-noise ratio, background clutter and high sampling rates. It is based on the estimation of the ultra-wideband channel impulse response. We suggest techniques for dealing with background clutter in situations when it might be time variant. We also present a novel methodology for reducing the required sampling rate of the system significantly while achieving the accuracy offered by the Nyquist rate. Performance results from simulations conducted with pre-recorded respiratory signals demonstrate the robustness of our scheme for tackling the above challenges and providing a low-complexity solution for the monitoring of respiratory movements.

3D location and orientation estimation using Angle of Arrival

This paper discusses the problem of joint location and orientation estimation of a receiver using only Angle Of Arrival (AOA) information. Conventional formulations of the problem consist of a number of nonlinear equations where the number of unknowns exceeds the number of equations. However, formulations presented in this paper simplifies the problem in a way that leads of efficient solutions. Two solutions are presented and their performance is compared via simulations using an indoor application as an example. Results emphasize the effectiveness of the the proposed methods.