Decision-Making with Heterogeneous Sensors - A Copula Based Approach

Statistical decision making has wide ranging applications, from communications and signal processing to econometrics and finance. In contrast to the classical one source-one receiver paradigm, several applications have been identified in the recent past that require acquiring data from multiple sources or sensors. Information from the multiple sensors are transmitted to a remotely located receiver known as the fusion center which makes a global decision. Past work has largely focused on fusion of information from homogeneous sensors. This dissertation extends the formulation to the case when the local sensors may possess disparate sensing modalities. Both the theoretical and practical aspects of multimodal signal processing are considered. The first and foremost challenge is to \u27adequately\u27 model the joint statistics of such heterogeneous sensors. We propose the use of copula theory for this purpose. Copula models are general descriptors of dependence. They provide a way to characterize the nonlinear functional relationships between the multiple modalities, which are otherwise difficult to formalize. The important problem of selecting the `best\u27 copula function from a given set of valid copula densities is addressed, especially in the context of binary hypothesis testing problems. Both, the training-testing paradigm, where a training set is assumed to be available for learning the copula models prior to system deployment, as well as generalized likelihood ratio test (GLRT) based fusion rule for the online selection and estimation of copula parameters are considered. The developed theory is corroborated with extensive computer simulations as well as results on real-world data. Sensor observations (or features extracted thereof) are most often quantized before their transmission to the fusion center for bandwidth and power conservation. A detection scheme is proposed for this problem assuming unifom scalar quantizers at each sensor. The designed rule is applicable for both binary and multibit local sensor decisions. An alternative suboptimal but computationally efficient fusion rule is also designed which involves injecting a deliberate disturbance to the local sensor decisions before fusion. The rule is based on Widrow\u27s statistical theory of quantization. Addition of controlled noise helps to \u27linearize\u27 the higly nonlinear quantization process thus resulting in computational savings. It is shown that although the introduction of external noise does cause a reduction in the received signal to noise ratio, the proposed approach can be highly accurate when the input signals have bandlimited characteristic functions, and the number of quantization levels is large. The problem of quantifying neural synchrony using copula functions is also investigated. It has been widely accepted that multiple simultaneously recorded electroencephalographic signals exhibit nonlinear and non-Gaussian statistics. While the existing and popular measures such as correlation coefficient, corr-entropy coefficient, coh-entropy and mutual information are limited to being bivariate and hence applicable only to pairs of channels, measures such as Granger causality, even though multivariate, fail to account for any nonlinear inter-channel dependence. The application of copula theory helps alleviate both these limitations. The problem of distinguishing patients with mild cognitive impairment from the age-matched control subjects is also considered. Results show that the copula derived synchrony measures when used in conjunction with other synchrony measures improve the detection of Alzheimer\u27s disease onset

Optimal Inference for Distributed Detection

In distributed detection, there does not exist an automatic way of generating optimal decision strategies for non-affine decision functions. Consequently, in a detection problem based on a non-affine decision function, establishing optimality of a given decision strategy, such as a generalized likelihood ratio test, is often difficult or even impossible. In this thesis we develop a novel detection network optimization technique that can be used to determine necessary and sufficient conditions for optimality in distributed detection for which the underlying objective function is monotonic and convex in probabilistic decision strategies. Our developed approach leverages on basic concepts of optimization and statistical inference which are provided in appendices in sufficient detail. These basic concepts are combined to form the basis of an optimal inference technique for signal detection. We prove a central theorem that characterizes optimality in a variety of distributed detection architectures. We discuss three applications of this result in distributed signal detection. These applications include interactive distributed detection, optimal tandem fusion architecture, and distributed detection by acyclic graph networks. In the conclusion we indicate several future research directions, which include possible generalizations of our optimization method and new research problems arising from each of the three applications considered

Optimal Decision Fusion in Multiple Sensor Systems

The problem of optimal data fusion in the sense of the Neyman- Pearson (N-P) test in a centralized fusion center is considered. The fusion center receives data from various distributed sensors. Each sensor implements a N-P test individually and independently of the other sensors. Due to limitations in channel capacity, the sensors transmit their decision instead of raw data. In addition to their decisions, the sensors may transmit one or more bits of quality information. The optimal, in the N-P sense, decision scheme at the fusion center is derived and it is seen that an improvement in the performance of the system beyond that of the most reliable sensor is feasible, even without quality information, for a system of three or more sensors. If quality information bits are also available at the fusion center, the performance of the distributed decision scheme is comparable to that of the centralized N-P test. Several examples are provided and an algorithm for adjusting the threshold level at the fusion center is provided

MAC-PHY Frameworks For LTE And WiFi Networks\u27 Coexistence Over The Unlicensed Band

The main focus of this dissertation is to address these issues and to analyze the interactions between LTE and WiFi coexisting on the unlicensed spectrum. This can be done by providing some improvements in the first two communication layers in both technologies. Regarding the physical (PHY) layer, efficient spectrum sensing and data fusion techniques that consider correlated spectrum sensing readings at the LTE/WiFi users (sensors) are needed. Failure to consider such correlation has been a major shortcoming of the literature. This resulted in poorly performing spectrum sensing systems if such correlation is not considered in correlated-measurements environments

Estimation sous contraintes de communication (algorithmes et performances asymptotiques)

L'essor des nouvelles technologies de télécommunication et de conception des capteurs a fait apparaître un nouveau domaine du traitement du signal : les réseaux de capteurs. Une application clé de ce nouveau domaine est l'estimation à distance : les capteurs acquièrent de l'information et la transmettent à un point distant où l'estimation est faite. Pour relever les nouveaux défis apportés par cette nouvelle approche (contraintes d'énergie, de bande et de complexité), la quantification des mesures est une solution. Ce contexte nous amène à étudier l'estimation à partir de mesures quantifiées. Nous nous concentrons principalement sur le problème d'estimation d'un paramètre de centrage scalaire. Le paramètre est considéré soit constant, soit variable dans le temps et modélisé par un processus de Wiener lent. Nous présentons des algorithmes d'estimation pour résoudre ce problème et, en se basant sur l'analyse de performance, nous montrons l'importance de l'adaptativité de la dynamique de quantification pour l'obtention d'une performance optimale. Nous proposons un schéma adaptatif de faible complexité qui, conjointement, estime le paramètre et met à jour les seuils du quantifieur. L'estimateur atteint de cette façon la performance asymptotique optimale. Avec 4 ou 5 bits de résolution, nous montrons que la performance optimale pour la quantification uniforme est très proche des performances d'estimation à partir de mesures continues. Finalement, nous proposons une approche à haute résolution pour obtenir les seuils de quantification non-uniformes optimaux ainsi qu'une approximation analytique des performances d'estimation.With recent advances in sensing and communication technology, sensor networks have emerged as a new field in signal processing. One of the applications of his new field is remote estimation, where the sensors gather information and send it to some distant point where estimation is carried out. For overcoming the new design challenges brought by this approach (constrained energy, bandwidth and complexity), quantization of the measurements can be considered. Based on this context, we study the problem of estimation based on quantized measurements. We focus mainly on the scalar location parameter estimation problem, the parameter is considered to be either constant or varying according to a slow Wiener process model. We present estimation algorithms to solve this problem and, based on performance analysis, we show the importance of quantizer range adaptiveness for obtaining optimal performance. We propose a low complexity adaptive scheme that jointly estimates the parameter and updates the quantizer thresholds, achieving in this way asymptotically optimal performance. With only 4 or 5 bits of resolution, the asymptotically optimal performance for uniform quantization is shown to be very close to the continuous measurement estimation performance. Finally, we propose a high resolution approach to obtain an approximation of the optimal nonuniform quantization thresholds for parameter estimation and also to obtain an analytical approximation of the estimation performance based on quantized measurements.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Decentralized Narrowband and Wideband Spectrum Sensing with Correlated Observations

This dissertation evaluates the utility of several approaches to the design of good distributed sensing systems for both narrowband and wideband spectrum sensing problems with correlated sensor observations

Combined Soft Hard Cooperative Spectrum Sensing in Cognitive Radio Networks

Providing some techniques to enhance the performance of spectrum sensing in cognitive radio systems while accounting for the cost and bandwidth limitations in practical scenarios is the main objective of this thesis. We focus on an essential element of cooperative spectrum sensing (CSS) which is the data fusion that combines the sensing results to make the final decision. Exploiting the advantage of the superior performance of the soft schemes and the low bandwidth of the hard schemes by incorporating them in cluster based CSS networks is achieved in two different ways. First, a soft-hard combination is employed to propose a hierarchical cluster based spectrum sensing algorithm. The proposed algorithm maximizes the detection performances while satisfying the probability of false alarm constraint. Simulation results of the proposed algorithm are presented and compared with existing algorithms over the Nakagami fading channel. Moreover, the results show that the proposed algorithm outperforms the existing algorithms. In the second part, a low complexity soft-hard combination scheme is suggested by utilizing both one-bit and two-bit schemes to balance between the required bandwidth and the detection performance by taking into account that different clusters undergo different conditions. The scheme allocates a reliability factor proportional to the detection rate to each cluster to combine the results at the Fusion center (FC) by extracting the results of the reliable clusters. Numerical results obtained have shown that a superior detection performance and a minimum overhead can be achieved simultaneously by combining one bit and two schemes at the intra-cluster level while assigning a reliability factor at the inter-cluster level