We implement and experimentally demonstrate a distributed, phase-coherent, mesh relay network that executes spatiotemporal beamforming on a communications signal. Each single-antenna node of this mesh network amplifie...
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We implement and experimentally demonstrate a distributed, phase-coherent, mesh relay network that executes spatiotemporal beamforming on a communications signal. Each single-antenna node of this mesh network amplifies, predistorts, and forwards its reception to a receiver. In this configuration, an incoherent network of N nodes enhances the received power of a signal of interest by a factor of N compared to a single-input single-output communications link. By synchronizing these distributed nodes and constructing a spatiotemporal beamformer, we increase this factor to a maximum of N-2 and enable significant interference rejection capabilities. To achieve phase-coherence across the network elements, we execute a distributed synchronization algorithm using training data from the source node. We construct spatiotemporal beamformers by solving an MMSE optimization, which we continually reoptimize using new observations of training sequences and updated channel estimates. We present results from two over-the-air experimental demonstrations, one without and one with an external interferer. In the former, we demonstrate a 17.4 dB signal-to-noise ratio (SNR) improvement compared to the 18.1 dB theoretical bound for an eight-element network. In the latter, we demonstrate an 11.3 dB SNR improvement and a 14.6 dB interference reduction.
Accurate, least-squares based bias free direction-of-arrival (DOA) estimation from targets using a circular array is discussed. The proposed estimation method is based on a recently proposed decorrelation technique th...
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ISBN:
(纸本)9781728103976
Accurate, least-squares based bias free direction-of-arrival (DOA) estimation from targets using a circular array is discussed. The proposed estimation method is based on a recently proposed decorrelation technique that uses regression sum of squares. The estimation meets Cramer-Rao bounds for any circular array geometry that uses either a large or a small number of sensors. The method is versatile as it can be used in accurate angle estimation even when sensors are placed sparsely and non-uniformly on the circle. The method is computationally efficient since matrix inversions can be avoided. Possibility of DOA estimation from extremely close-by targets with very small angles of separation is demonstrated.
Computationally efficient methods for accurate, bias free DOA estimation from a source signal impinging on a sparsearray are presented. In particular, the presence of I/Q mismatch and D.C. offsets are discussed. Sinc...
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ISBN:
(纸本)9781509041657
Computationally efficient methods for accurate, bias free DOA estimation from a source signal impinging on a sparsearray are presented. In particular, the presence of I/Q mismatch and D.C. offsets are discussed. Since the methods meet Cramer-Rao bounds and able to cope array imperfections such as non-uniform gains, element failure, they are useful in short sparsearray implementations with simplified processing hardware.
In order to resolve multiple closely spaced sources moving in a tight formation using unattended acoustic sensors, the array aperture must be extended using a sparsearray geometry. Traditional sparsearray algorithms...
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ISBN:
(纸本)0819462578
In order to resolve multiple closely spaced sources moving in a tight formation using unattended acoustic sensors, the array aperture must be extended using a sparsearray geometry. Traditional sparsearray algorithms rely on the spatial invariance property often leading to inaccurate Direction of Arrival (DOA) estimates due to the large side-lobes present in the power spectrum. Many problems of traditional sparsearrays can be alleviated by forming a sparsearray using randomly distributed single microphones. The power spectrum of a random sparsearray will almost always exhibit low side-lobes, thus increasing the ability of the beamforming algorithm to accurately separate and localize sources. This paper examines the robustness of randomly distributed sparsearray beamforming in situations where the exact sensor location is unknown and benchmark its performance with that of traditional baseline sparsearrays. We will also use a realistic acoustic propagation model to study fading effects as a function of range and its influence on the beamforming process for various sparsearray configurations.
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