Search query: Array signal processing

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abstract: Memristor crossbars offer reconfigurable non volatile resistance states and could remove the speed and energy efficiency bottleneck in vector matrix multiplication, a core computing task in signal and image processing. Using such systems to multiply an analogue voltage amplitude vector by an analogue conductance matrix at a reasonably large scale has, however, proved challenging due to difficulties in device engineering and array integration. Here we show that reconfigurable memristor crossbars composed of hafnium oxide memristors on top of metal oxide semiconductor transistors are capable of analogue vector matrix multiplication with array sizes of up to 128 x 64 cells. Our output precision (5 8 bits, depending on the array size) is the result of high device yield (99.8% and the multilevel, stable states of the memristors, while the linear device current voltage characteristics and low wire resistance between cells leads to high accuracy. With the large memristor crossbars, we demonstrate signal processing, image compression and convolutional filtering, which are expected to be important applications in the development of the Internet of Things (IoT) and edge computing.MainMemristor crossbars with array sizes of up to 128 x 64 cells are capable of analogue vector matrix multiplication and can be used for signal processing, image compression and convolutional filtering.
author_names: Can Li

Miao Hu

Yunning Li

Hao Jiang

Ning Ge

Eric Montgomery

Jiaming Zhang

Wenhao Song

Noraica Davila

Catherine E Graves

Zhiyong Li

John Paul Strachan

Peng Lin

Zhongrui Wang

Mark D Barnell

Qing Wu

R Stanley Williams

Jianhua Joshua Yang

Qiangfei Xia
title: Analogue signal and image processing with large memristor crossbars
year: 2018

abstract: We present pyroomacoustics, a software package aimed at the rapid development and testing of audio array processing algorithms. The content of the package can be divided into three main components: an intuitive Python object oriented interface to quickly construct different simulation scenarios involving multiple sound sources and microphones in 2D and 3D rooms; a fast C implementation of the image source model for general polyhedral rooms to efficiently generate room impulse responses and simulate the propagation between sources and receivers; and finally, reference implementations of popular algorithms for beamforming, direction finding, and adaptive filtering. Together, they form a package with the potential to speed up the time to market of new algorithms by significantly reducing the implementation overhead in the performance evaluation step.
author_names: Robin Scheibler

Eric Bezzam

Ivan Dokmanic
title: Pyroomacoustics: A Python Package for Audio Room Simulation and Array Processing Algorithms
venue: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
year: 2018

abstract: Microphone array signal processing based on time frequency masks has been applied successfully to various tasks including source separation, denoising, source localization, and source counting. Aiming to improve the performance of these techniques, here we propose a mask estimation method based on a complex Angular Central Gaussian Mixture Model (cACGMM) for multichannel observed signals. Compared to a conventional complex Watson Mixture Model (cWMM) the proposed cACGMM can model not only rotationally symmetrical but also elliptical distributions. Therefore, the cACGMM can better approximate the distribution of observed data, which is generally not rotationally symmetrical. In source separation simulations with real recorded impulse responses, the cACGMM resulted in an average 1.2 dB improvement of the Signal to Distortion Ratio (SDR) over the cWMM.
author_names: Nobutaka Ito

Shoko Araki

Tomohiro Nakatani
title: Complex angular central Gaussian mixture model for directional statistics in mask based microphone array signal processing
venue: 2016 24th European Signal Processing Conference (EUSIPCO)
year: 2016

abstract: One of the main vulnerabilities of GNSS receivers is their exposure to intentional or unintentional jamming signals, which could even cause service unavailability. Several alternatives to counteract these effects were proposed in the literature, being the most promising those based on multiple antenna architectures. This is specially the case for high grade receivers used in applications requiring reliability and robustness. This article provides an overview of the possible receiver architectures encompassing antenna arrays and the associated signal processing techniques. Emphasis is also put on the most typical implementation issues found when dealing with such technology. A thorough survey is complemented with a set of experiments, including real data processing by a working prototype, which exemplifies the above ideas.
author_names: Carles Fernandez-Prades

Javier Arribas

Pau Closas
title: Robust GNSS Receivers by Array Signal Processing: Theory and Implementation
venue: Proceedings of the IEEE
year: 2016

abstract: This paper proposes a practical channel estimation for 60 GHz indoor systems with the massive uniform rectangular array at base station. Through antenna array theory, the parameters of each channel path can be decomposed into the angular information and the channel gain information. We first prove that the true direction of arrivals of each uplink path can be extracted via an efficient array signal processing method. Then, the channel gain information could be obtained linearly with small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, the proposed scheme unifies the uplink/downlink channel estimations for both the time duplex division and frequency duplex division systems, making itself particularly suitable for protocol design. Compared with the existing channel estimation algorithms, the newly proposed one does not require any knowledge of channel statistics and can be efficiently deployed by the 2 D fast Fourier transform. Meanwhile, the number of user terminals simultaneously served can be increased from a sophisticatedly designed angle division multiple access scheme. Simulation results are provided to corroborate the proposed studies.
author_names: Dian Fan

Feifei Gao

Gongpu Wang

Zhangdui Zhong

Arumugam Nallanathan
title: Angle Domain Signal Processing Aided Channel Estimation for Indoor 60 GHz TDD/FDD Massive MIMO Systems
venue: IEEE Journal on Selected Areas in Communications
year: 2017

abstract: Abstract Human robot interaction in natural settings requires filtering out the different sources of sounds from the environment. Such ability usually involves the use of microphone arrays to localize, track and separate sound sources online. Multi microphone signal processing techniques can improve robustness to noise but the processing cost increases with the number of microphones used, limiting response time and widespread use on different types of mobile robots. Since sound source localization methods are the most expensive in terms of computing resources as they involve scanning a large 3D space, minimizing the amount of computations required would facilitate their implementation and use on robots. The robot's shape also brings constraints on the microphone array geometry and configurations. In addition, sound source localization methods usually return noisy features that need to be smoothed and filtered by tracking the sound sources. This paper presents a novel sound source localization method, called SRP PHAT HSDA, that scans space with coarse and fine resolution grids to reduce the number of memory lookups. A microphone directivity model is used to reduce the number of directions to scan and ignore non significant pairs of microphones. A configuration method is also introduced to automatically set parameters that are normally empirically tuned according to the shape of the microphone array. For sound source tracking, this paper presents a modified 3D Kalman (M3K) method capable of simultaneously tracking in 3D the directions of sound sources. Using a 16 microphone array and low cost hardware, results show that SRP PHAT HSDA and M3K perform at least as well as other sound source localization and tracking methods while using up to 4 and 30 times less computing resources respectively.
author_names: Francois Grondin

Francois Michaud
title: Lightweight and Optimized Sound Source Localization and Tracking Methods for Open and Closed Microphone Array Configurations
venue: Robotics Auton. Syst.
year: 2019

abstract: Time and frequency modulated arrays have numerous application areas including radar, navigation, and communications. Specifically, a time modulated array can create a beampattern with low sidelobes via connecting and disconnecting the antenna elements from the feed network, while the frequency modulated frequency diverse array produces a range dependent pattern. In this paper, we aim to introduce these advanced arrays to the signal processing community so that more investigations in terms of theory, methods, and applications, can be facilitated. The research progress of time/frequency modulated array studies is reviewed and the most recent advances are discussed. Moreover, potential applications in radar and communications are presented, along with their technical challenges, especially in signal processing aspects.
author_names: Wen-qin Wang

Hing Cheung So

Alfonso Farina
title: An Overview on Time/Frequency Modulated Array Processing
venue: IEEE Journal of Selected Topics in Signal Processing
year: 2017

abstract: Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection, and wireless human device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off the shelf (COTS) mobile devices. The key insight is to involve users' natural rotation to formulate a virtual spatial temporal antenna array and conduce a relative incident signal of measurements at two orientations. Then by taking the differential phase, it is feasible to remove the phase offsets and derive the accurate AoA of the equivalent incoming signal, while the rotation angle can also be captured by built in inertial sensors. On this basis, we propose Differential MUSIC <italic>D MUSIC</italic> a relative form of the standard MUSIC algorithm that eliminates the unknown phase offsets and achieves accurate AoA estimation on COTS mobile devices with only one rotation. We further extend <italic>D MUSIC</italic> to 3 D space, integrate extra measurements during rotations for higher estimation accuracy, and fortify it in multipath rich scenarios. We prototype <italic>D MUSIC</italic> on commodity WiFi infrastructure and evaluate it in typical indoor environments. Experimental results demonstrate a superior performance with average AoA estimation errors of 13<inline formula> <tex math notation="LaTeX"\circ}/tex math><alternatives><inline graphic xlink:href="qian ieq1 2778155.gif" /alternatives>/inline formula> with only three measurements and 5<inline formula><tex math notation="LaTeX" \circ}/tex math><alternatives><inline graphic xlink:href="qian ieq2 2778155.gif"/alternatives> /inline formula> with at most 10 measurements. Requiring no modifications or calibration, <italic>D MUSIC</italic> is envisioned as a promising scheme for practical AoA estimation on COTS mobile devices.
author_names: Kun Qian

Chenshu Wu

Zheng Yang

Zimu Zhou

Xu Wang

Yunhao Liu
title: Enabling Phased Array Signal Processing for Mobile WiFi Devices
venue: IEEE Transactions on Mobile Computing
year: 2018

abstract: This article investigates a kind of method to measure the wind speed and the wind direction, which is based on arc ultrasonic sensor array and combined with array signal processing algorithm. In the proposed method, a new arc ultrasonic array structure is introduced and the array manifold is derived firstly. On this basis, the measurement of the wind speed and the wind direction is analyzed and discussed by means of the basic idea of the classic MUSIC (Multiple Signal Classification) algorithm, which achieves the measurements of the 360deg wind direction with resolution of 1deg and 0 60m/s wind speed with resolution of 0.1m/s. The implementation of the proposed method is elaborated through the theoretical derivation and corresponding discussion. Besides, the simulation experiments are presented to show the feasibility of the proposed method. The theoretical analysis and simulation results indicate that the proposed method has superiority on anti noise performance and improves the wind measurement accuracy.
author_names: Xinbo Li

Haixin Sun

Wei Gao

Yaowu Shi

Guojun Liu

Yue Wu
title: Wind speed and direction measurement based on arc ultrasonic sensor array signal processing algorithm.
venue: ISA transactions
year: 2016

abstract: In this paper, we address the problem of sparsity inducing direction of arrival (DOA) estimation in the context of array covariance vectors when a recently proposed nested array structure is utilized. The proposed approach is based on a Sparse Bayesian learning (SBL) perspective while the off grid effects inherent in traditional sparse recovery algorithms are taken into account simultaneously. Specifically, the notable advantages of the developed approach can be attributed to two aspects. First, through a linear transformation, the unknown noise variance can be eliminated effectively and the estimation error between the actual and sample covariance matrix can be normalized to an identity matrix, thereby facilitating a noise free sparse representation. Second, a computationally tractable polynomial rooting procedure is introduced to calculate the discretized sampling grid error one by one based on the reconstructed spatial power spectrum, thus this novel algorithm can maintain high estimation accuracy even under a coarse sampling grid. Extensive simulation results finally demonstrate the superiority of the proposed approach in terms of DOA estimation precision and computational efficiency over off the shelves techniques. A sparsity inducing DOA estimation method applied in nested array is proposed.The proposed method is based on a Sparse Bayesian learning perspective.The off grid effects inherent in sparse recovery framework are taken into account.The unknown noise variance can be eliminated easily.The off grid distances can be calculated by using a polynomial rooting procedure.
author_names: Jie Yang

Guisheng Liao

Jun Yu Li
title: An efficient off grid DOA estimation approach for nested array signal processing by using sparse Bayesian learning strategies
venue: Signal Process.
year: 2016