Distributed Adaptive Signal Estimation in Wireless Sensor Networks with Partial Prior Knowledge of the Desired Sources Steering Matrix
Abstract : In the past two decades, wireless sensor networks (WSNs) and their applications have been the topic of many studies. Different multi-sensor nodes are used to collect, process and distribute data over wireless links to perform different tasks such as smart detection, target tracking, node localization, etc. In this article, the problem of distributed adaptive estimation of node-specific signals for signal enhancement or noise reduction is addressed. First the centralized rank R generalized eigenvalue decomposition (GEVD) based multichannel Wiener filter (MWF) with prior knowledge for node-specific signal estimation in a WSN is introduced, where (some of) the nodes have partial prior knowledge of the desired sources steering matrix. A distributed adaptive estimation algorithm for a fully-connected WSN is then proposed demonstrating that this MWF can be obtained by letting the nodes work on compressed (i.e. reduced-dimensional) sensor signals compared to the centralized algorithm. The distributed algorithm can be used in applications such as speech enhancement in a wireless acoustic sensor network (WASN), where (some of) the nodes have prior knowledge on the location of the desired speech sources and on their local microphone array geometry or have access to clean noise reference signals. Foundations for a proof of convergence using a Lagrangian framework, are given, since convergence is observed in batch-mode simulations. Finally, numerical simulation results are provided for a speech enhancement scenario.
? The sensor nodes may be deployed over an area where a large number of communication transmission multipaths exist due to the presence of man-made structures or natural obstacles.
? Various adaptive algorithms exist, such as the least mean squares (LMS), recursive least squares (RLS) and constant modulus algorithm (CMA).
? Some of the location discovery techniques that can be used to determine the locations of the sensor nodes, assuming that one or more beacon nodes exist.
? A basic two-input-two-output narrowband MIMO system where four transmission paths exist between the transmitter and the receiver antenna arrays.
? The problem formulation and the centralized approach to the node specific signal estimation problem with prior knowledge.
? These differences make the task of distributed beamforming using sensor clusters a challenging problem compared to that of conventional arrays, yet the benefits derived from the higher signal-to-noise ratio (and capacity) gains due to beamforming are significant
? In order to address these issues, a simulation model was developed and implemented in the MATLAB programming language, and the results of the simulation are presented
? The main issues in the hierarchical arrangement include how to select the primary node and organize the cluster.
• A distributed beamforming approach was proposed whereby the sensor nodes are grouped into clusters and their transmission are coordinated in order to form a distributed antenna array that directs a beam towards the UAV.
• Some of the common clustering algorithms that researchers have proposed to address these issues.
• Several approaches have been proposed to track the AOA of moving targets using passive arrays.
• Another approach proposed by uses a maximum likelihood algorithm while that proposed by uses a recursive tracking algorithm that uses the AOA estimates from the most recent array data to update the existing one.
? This generally leads to superior estimation performance compared to that of the stand-alone estimation, where each node uses only local sensor signals.
? The goal for every node is to obtain the same performance as if all the sensor signals were collected in a fusion center (FC), but in a distributed fashion while minimizing the local computations and communication with the other nodes.
? The algorithms in exploit this common signal subspace, to significantly compress the sensor signals that are communicated between the nodes, without compromising performance.
? However in low SNR scenarios, this might result in a poor estimation of the signal correlation matrix, deteriorating the node-specific signal estimation performance.
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