Blind Beamforming on a Randomly Distributed Sensor Array System

We consider a digital signal handling sensor array system, in light of haphazardly dispersed sensor node, for observation and source localization applications. In most array handling system, the sensor array geometry is settled and known and the steering array vector/complex data is utilized as a part of beam- formation. In this system, the array adjustment may be illogical because of obscure situation and introduction of the sensors with obscure frequency/spatial responses.In this project work a blind beamforming method is used by utilizing just the deliberate sensor information, to shape either an example information or a sample correlation matrix. The greatest power accumulation measure is utilized to acquire array weights from the predominant eigenvector connected with the largest eigenvalue of a matrix eigenvalue issue. A productive blind beamforming time delay appraisal of the predominant source is proposed. Source localization in light of a least squares (LS) technique for time delay estimation is additionally given. Results taking into account investigation, simulation, and measured acoustical sensor information demonstrate the viability of this beamforming system for sign upgrade and spacetime filtering

Collaborative Beamforming for Distributed Wireless Ad Hoc Sensor Networks

The performance of collaborative beamforming is analyzed using the theory of random arrays. The statistical average and distribution of the beampattern of randomly generated phased arrays is derived in the framework of wireless ad hoc sensor networks. Each sensor node is assumed to have a single isotropic antenna and nodes in the cluster collaboratively transmit the signal such that the signal in the target direction is coherently added in the far- eld region. It is shown that with N sensor nodes uniformly distributed over a disk, the directivity can approach N, provided that the nodes are located sparsely enough. The distribution of the maximum sidelobe peak is also studied. With the application to ad hoc networks in mind, two scenarios, closed-loop and open-loop, are considered. Associated with these scenarios, the effects of phase jitter and location estimation errors on the average beampattern are also analyzed.Comment: To appear in the IEEE Transactions on Signal Processin

Connectivity of confined 3D Networks with Anisotropically Radiating Nodes

Nodes in ad hoc networks with randomly oriented directional antenna patterns typically have fewer short links and more long links which can bridge together otherwise isolated subnetworks. This network feature is known to improve overall connectivity in 2D random networks operating at low channel path loss. To this end, we advance recently established results to obtain analytic expressions for the mean degree of 3D networks for simple but practical anisotropic gain profiles, including those of patch, dipole and end-fire array antennas. Our analysis reveals that for homogeneous systems (i.e. neglecting boundary effects) directional radiation patterns are superior to the isotropic case only when the path loss exponent is less than the spatial dimension. Moreover, we establish that ad hoc networks utilizing directional transmit and isotropic receive antennas (or vice versa) are always sub-optimally connected regardless of the environment path loss. We extend our analysis to investigate boundary effects in inhomogeneous systems, and study the geometrical reasons why directional radiating nodes are at a disadvantage to isotropic ones. Finally, we discuss multi-directional gain patterns consisting of many equally spaced lobes which could be used to mitigate boundary effects and improve overall network connectivity.Comment: 12 pages, 10 figure

SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization

Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field