Localization of Spatially Distributed Near-Field Sources with Unknown Angular Spread Shape

International audienceIn this paper, we propose to localize and characterize coherently distributed (CD) sources in near-field. Indeed, it appears that in some applications, the more the sources are close to the array of sensors, the more they can seem scattered. It thus appears of the biggest importance to take into account the angular distribution of the sources in the joint direction of arrival (DOA) and range estimation methods. The methods of the literature which consider the problem of distributed sources do not handle with the case of near field sources and require that the shape of the dispersion is known. The main contribution of the proposed method is to estimate the shape of the angular distribution using an additional shape parameter to address the case of unknown distributions. We propose to jointly estimate the DOA, the range, the spread angle and the shape of the spread distribution. Accurate estimation is then achieved even when the shape of the angular spread distribution is unknown or imperfectly known. Moreover, the proposed estimator improves angular resolution of the sources

Security and Prioritization in Multiple Access Relay Networks

In this work, we considered a multiple access relay network and investigated the following three problems: 1- Tradeoff between reliability and security under falsified data injection attacks; 2-Prioritized analog relaying; 3- mitigation of Forwarding Misbehaviors in Multiple access relay network. In the first problem, we consider a multiple access relay network where multiple sources send independent data to a single destination through multiple relays which may inject a falsified data into the network. To detect the malicious relays and discard (erase) data from them, tracing bits are embedded in the information data at each source node. Parity bits may be also added to correct the errors caused by fading and noise. When the total amount of redundancy, tracing bits plus parity bits, is fixed, an increase in parity bits to increase the reliability requires a decrease in tracing bits which leads to a less accurate detection of malicious behavior of relays, and vice versa. We investigate the tradeoff between the tracing bits and the parity bits in minimizing the probability of decoding error and maximizing the throughput in multi-source, multi-relay networks under falsified data injection attacks. The energy and throughput gains provided by the optimal allocation of redundancy and the tradeoff between reliability and security are analyzed. In the second problem, we consider a multiple access relay network where multiple sources send independent data simultaneously to a common destination through multiple relay nodes. We present three prioritized analog cooperative relaying schemes that provide different class of service (CoS) to different sources while being relayed at the same time in the same frequency band. The three schemes take the channel variations into account in determining the relay encoding (combining) rule, but differ in terms of whether or how relays cooperate. Simulation results on the symbol error probability and outage probability are provided to show the effectiveness of the proposed schemes. In the third problem, we propose a physical layer approach to detect the relay node that injects false data or adds channel errors into the network encoder in multiple access relay networks. The misbehaving relay is detected by using the maximum a posteriori (MAP) detection rule which is optimal in the sense of minimizing the probability of incorrect decision (false alarm and miss detection). The proposed scheme does not require sending extra bits at the source, such as hash function or message authentication check bits, and hence there is no transmission overhead. The side information regarding the presence of forwarding misbehavior is exploited at the decoder to enhance the reliability of decoding. We derive the probability of false alarm and miss detection and the probability of bit error, taking into account the lossy nature of wireless links

Parametric array calibration

The subject of this thesis is the development of parametric methods for the calibration of array shape errors. Two physical scenarios are considered, the online calibration (self-calibration) using far-field sources and the offline calibration using near-field sources. The maximum likelihood (ML) estimators are employed to estimate the errors. However, the well-known computational complexity in objective function optimization for the ML estimators demands effective and efficient optimization algorithms. A novel space-alternating generalized expectation-maximization (SAGE)-based algorithm is developed to optimize the objective function of the conditional maximum likelihood (CML) estimator for the far-field online calibration. Through data augmentation, joint direction of arrival (DOA) estimation and array calibration can be carried out by a computationally simple search procedure. Numerical experiments show that the proposed method outperforms the existing method for closely located signal sources and is robust to large shape errors. In addition, the accuracy of the proposed procedure attains the Cram´er-Rao bound (CRB). A global optimization algorithm, particle swarm optimization (PSO) is employed to optimize the objective function of the unconditional maximum likelihood (UML) estimator for the farfield online calibration and the near-field offline calibration. A new technique, decaying diagonal loading (DDL) is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. Numerical simulations demonstrate that the UML estimator optimized by PSO with DDL is optimally accurate, robust to large shape errors, and free of the initialization problem. In addition, the DDL technique is applicable to a wide range of array processing problems where the UML estimator is employed and can be coupled with different global optimization algorithms