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Model for a Smart Network Monitoring a Wired sensor Matrix

Model for a Smart Network Monitoring a Wired Sensor Matrix

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sensor array and multichannel signal processing workshop

作者： L. Magagni D. Gennaretti M. Nicolini M. Sergio R. Guerrieri R. Canegallo ARCES University of Bologna Italy STMicroelectronics Italy

Monitoring harsh environments such as underwater scenarios or aircraft external surfaces pertains to important applications like assisted navigation and tactical surveillance; nevertheless, it poses additional challenges compared with standard applications. At Transducers2005 we presented a wired addressing architecture of distributed sensors for monitoring real-time in-situ pressure variations in underwater environment that faces the above-mentioned issues. This architecture consists in a double array of identical and interconnected smart nodes monitoring a matrix of passive sensors. In this paper, we present an analysis of the delay model related to the presented architecture and a calculation of the overall frame-rate of the system as a function of the geometrical topology of the arrays. The topology of the network, i.e. the length of each bus and the number of nodes, can be chosen according to the application, and directly affects the global capacitive load on the serial lines. Each serial line can be schematized with a distributed RC model for the flat cable plus a lumped capacitance for each smart node. Then, a 3 rd -order pi-segmented model of O'Brien-Savarino is calculated for a 16-block line with block length equal to 0.3 m. Thanks to that, the global time per iteration is calculated on each bus as well as the scanning time of the whole matrix and the frame rate for the system as a function of sensor distribution and of the aspect ratio of the matrix. This model can be employed to identify the optimal arrangement for the sensor matrix and smart node arrays

关键词： Monitoring Intelligent sensors sensor arrays Aircraft navigation Network topology Surveillance Delay Solid modeling Power cables Underwater cables

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Analysis of Cross Terms in Toeplitz Rectified Sample Covariance Matrices

Analysis of Cross Terms in Toeplitz Rectified Sample Covaria...

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sensor array and multichannel signal processing workshop

作者： Vaibhav Chavali Kathleen E. Wage Electrical and Computer Engineering Department George Mason University Fairfax USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Toeplitz-rectification of a Sample Covariance Matrix (SCM) is used to enhance the performance of array processing algorithms, particularly those relying on obtaining signal or noise subspace estimates, such as MUSIC Direction of Arrival (DoA) estimation and subspace-based adaptive beamforming. A Toeplitz-Rectified SCM (TR-SCM) is obtained by averaging along the diagonals of the SCM. This paper characterizes subspace estimates obtained using TR-SCMs in environments with a wide range of signal powers. Analysis of a single source in noise reveals a deterministic structure that provides valuable insights regarding the estimated subspaces. This paper derives an analytical prediction of the principal eigenvector of TR-SCMs. The eigenvector has reduced errors in directions orthogonal to the true source, but unlike the SCM, they are non-uniformly distributed. The consequence of this is observed in the noise subspace where eigenvectors corresponding to higher noise eigen-values tend to cluster around the true source. The paper presents a geometric picture of subspace estimates obtained through TR-SCMs. It shows that when estimating DoAs for multiple sources with diverse signal strengths, cross terms between a loud source and noise can significantly increase noise levels around the true source, thereby leading to inaccurate estimations of quieter sources.

关键词： Direction-of-arrival estimation Filters array signal processing Noise Estimation Multiple signal classification Covariance matrices

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Decentralized Non-Smooth Optimization Over the Stiefel Manifold

Decentralized Non-Smooth Optimization Over the Stiefel Manif...

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sensor array and multichannel signal processing workshop

作者： Jinxin Wang Jiang Hu Shixiang Chen Zengde Deng Anthony Man-Cho So Department of Syst. Eng. and Eng. Manag. CUHK Hong Kong SAR Mass General Hospital and Harvard Medical School Harvard University USA School of Mathematical Sciences USTC China ByteDance China

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

We focus on a class of non-smooth optimization problems over the Stiefel manifold in the decentralized setting, where a connected network of many agents cooperatively mini-mize a finite-sum objective function with each component being weakly convex in the ambient Euclidean space. Such optimization problems, albeit frequently encountered in applications, are quite challenging due to their non-smoothness and non-convexity. To tackle them, we propose an iterative method called the decentralized Riemannian sub gradient method (DRSM). When the problem at hand possesses a sharpness property, we show the local linear convergence of DRSM using geometrically di-minishing stepsizes. Numerical experiments are conducted to demonstrate the superior performance of DRSM in different applications.

关键词： Manifolds Gradient methods array signal processing Conferences Linear programming Iterative methods Optimization

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A Probabilistic Algorithm for Meg Source Reconstruction

A Probabilistic Algorithm for Meg Source Reconstruction

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sensor array and multichannel signal processing workshop

作者： J.M. Zumer H.T. Attias K. Sekihara S.S. Nagarajan Biomagnetic Imaging Lab Department of Radiology University of California San Francisco CA USA Golden Metallic Inc. San Francisco CA USA Department of Systems Design and Engineering Tokyo Metropolitan University Tokyo Japan

We present a novel algorithm for source localization based on probabilistic modeling of stimulus-evoked MEG/EEG data. This algorithm localizes multiple dipoles with the computational complexity equivalent to a single dipole scan, and is therefore more efficient than traditional multidipole fitting procedures. The algorithm assumes that the activity of multiple dipolar sources can be characterized by a linear combination of known temporal basis functions with unknown coefficients. We model the sensor data as arising from activity in each voxel of interest, plus background activity. We estimate temporal basis functions from the data using a probabilistic algorithm called partitioned-factor analysis, previously developed in our lab. We model background activity outside the voxel of interest as an unknown linear mixture of unobserved background factors plus diagonal sensor noise. We use an expectation-maximization algorithm to calculate MAP estimates of unknown basis function coefficients, background mixing matrix, sensor noise covariance and the likelihood of a dipole in each voxel of interest. In simulations, the algorithm is able to accurately localize several simultaneously-active dipoles, at SNRs typical for averaged MEG data. The algorithm performs well even in configurations that include deep sources and highly correlated sources, and thus is superior to MUSIC and beamforming techniques which are sensitive to correlated sources. The algorithm also correctly localizes real somatosensory and auditory evoked fields to the postcentral sulcus and lower bank of the lateral sulcus, respectively

关键词： Brain modeling Background noise Electroencephalography Computational complexity sensor phenomena and characterization Partitioning algorithms Algorithm design and analysis Expectation-maximization algorithms Covariance matrix Multiple signal classification

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Reduced Dimensional 2-D DOA Estimation via Least Partial Search with Automatic Pairing for Parallel Co-prime arrays*

Reduced Dimensional 2-D DOA Estimation via Least Partial Sea...

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sensor array and multichannel signal processing workshop

作者： Fenggang Sun Shengqi Ouyang Peng Lan Fengdi Li College of Information Science and Engineering Shandong Agricultural University Tai’an China International Education Institute North China Electric Power University Beijing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

In this paper, we address the problem of two-dimensional (2-D) direction-of-arrival (DOA) estimation for parallel co-prime arrays. To alleviate the tremendous computation burden caused by spectral search and angle pairing, in this paper, we propose an efficient reduced dimensional least spectral search based estimation method with automatic pairing. Specifically, we first utilize the cross-covariance matrix to decouple the 2-D DOA estimation problem into a one-dimensional (1-D) one, and then design a least spectral search based 1-D DOA estimation method according to the relations between true and ambiguous angles. Finally, we estimate the remaining 1-D DOAs via least square criterion with automatic pairing. We evaluate the complexity and present the simulation results to show the effectiveness of the proposed method.

关键词： Direction-of-arrival estimation array signal processing Simulation Conferences Two dimensional displays Estimation Search problems

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Near or Far: On Determining the Appropriate Channel Estimation Strategy in Cross-Field Communication

Near or Far: On Determining the Appropriate Channel Estimati...

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sensor array and multichannel signal processing workshop

作者： Simon Tarboush Anum Ali Tareq Y. Al-Naffouri Independent Researcher Dubai UAE Standards and Mobility Innovation Lab Samsung Research America Texas USA Department of CEMSE Information Science Lab King Abdullah University of Science and Technology Thuwal KSA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The use of ultra-massive multiple-input multiple-output and high-frequency large bandwidth systems is likely in the next-generation wireless communication systems. In such systems, the user moves between near- and far-field regions, and consequently, the channel estimation will need to be carried out in the cross-field scenario. Channel estimation strategies have been proposed for both near- and far-fields, but in the cross-field problem, the first step is to determine whether the near- or far-field is applicable so that an appropriate channel estimation strategy can be employed. In this work, we propose using a hidden Markov model over an ensemble of region estimates to enhance the accuracy of selecting the actual region. The region indicators are calculated using the pair-wise power differences between received signals across the subarrays within an array-of-subarrays architecture. Numerical results show that the proposed method achieves a high success rate in determining the appropriate channel estimation strategy.

关键词： Wireless communication Measurement array signal processing Channel estimation Hidden Markov models Parallel processing Next generation networking

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Outage Minimization for Intelligent Reflecting Surface Aided MISO Communication Systems via Stochastic Beamforming

Outage Minimization for Intelligent Reflecting Surface Aided...

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sensor array and multichannel signal processing workshop

作者： Wenzhi Fang Min Fu Yuanming Shi Yong Zhou School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Intelligent reflecting surface (IRS) has the potential to significantly enhance the network performance by reconfiguring the wireless propagation environments. It is however difficult to obtain the accurate downlink channel state information (CSI) for efficient beamforming design in IRS-aided wireless networks. In this article, we consider an IRS-aided downlink multiple-input single-output (MISO) network, where the base station (BS) is not required to know the underlying channel distribution. We formulate an outage probability minimization problem by jointly optimizing the beamforming vector at the BS and the phase-shift matrix at the IRS, while taking into account the transmit power and unimodular constraints. The formulated problem turns out to be a non-convex non-smooth stochastic optimization problem. To this end, we employ the sigmoid function as the surrogate to tackle the non-smoothness of the objective function. In addition, we propose a data-driven efficient alternating stochastic gradient descent (SGD) algorithm to solve the problem by utilizing the historical channel samples. Simulation results demonstrate the performance gains of the proposed algorithm over the benchmark methods in terms of minimizing the outage probability.

关键词： array signal processing Simulation signal processing algorithms Probability MISO communication Minimization Linear programming

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Direct Adaptive Equalization with CFO Pre-compensation for Single-Carrier Underwater Acoustic Communications

Direct Adaptive Equalization with CFO Pre-compensation for S...

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sensor array and multichannel signal processing workshop

作者： Jun Tao Fengzhong Qu Hongtao Zhang Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Zhejiang University Zhoushan China Hangzhou Applied Acoustics Research Institute Hangzhou China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

For single-carrier underwater acoustic (UWA) communications, phase correction is critical to the symbol detection on the receiver side. Existing receiver schemes either run a phase- locked loop (PLL) in parallel with an equalizer or perform the phase correction at the output of an equalizer. Both parallel and serial phase correction methods suffer limitations in practical use though. In this work, we propose to introduce a carrier frequency offset (CFO) pre-compensation module for existing receivers, with the CFO estimated with an m-sequence. The so-obtained receiver scheme was tested by real data collected in an at-sea UWA communication trial. Experimental results verified the extra performance gain brought by the CFO precompensation. In particular, when the CFO is the main source of phase rotation, conventional CFO correction modules like the PLL can be dropped without performance degradation.

关键词： Degradation array signal processing Conferences Receivers Performance gain Adaptive equalizers Frequency estimation

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High-Resolution DOA Estimation Using Single-Snapshot Music for Automotive Radar with Mixed-ADC Allocations

High-Resolution DOA Estimation Using Single-Snapshot Music f...

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sensor array and multichannel signal processing workshop

作者： Moritz Kahlert Lifan Xu Tai Fei Markus Gardill Shunqiao Sun HELLA GmbH & Co. KGaA Lippstadt Germany Department of Electrical and Computer Engineering University of Alabama Tuscaloosa AL USA University of Applied Sciences and Arts Dortmund Germany Brandenburg University of Technology Cottbus Germany

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Fine direction of arrival (DOA) estimations are required for accurate target detections in automotive radar systems. To address this issue, most spectral estimation methods assume many snapshots of measurements. However, due to the dynamic nature of automotive scenarios, methods using multiple snapshots are impractical for DOA estimation in automotive radars. Furthermore, to relax the hardware requirements on modern automotive radar systems, mixed-analog-to-digital converter (ADC) allocations, i.e., the coexistence of 1-bit and high-resolution ADCs, have gained more attention recently. In this work, we introduce a high-resolution DOA estimation approach based on single-snapshot multiple signal classification (MUSIC) estimation and evaluate the performance with various ADC allocations. The results show that mixed-ADC allocations can perform comparably to high-resolution ADC allocations.

关键词： Direction-of-arrival estimation array signal processing Estimation Radar detection Radar Multiple signal classification Resource management

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Joint channel estimation and detection for interference cancellation in multi-channel systems

Joint channel estimation and detection for interference canc...

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ieee workshop on Statistical signal and array processing

作者： C. Martin B. Ottersten Department of Signals Sensors & Systems Royal Institute of Technology Stockholm Sweden

Interference from other users and interference due to multipath propagation limit the capacity of wireless communication networks. As the number of users and the demand for new services in the networks increases, co-channel interference will be a limiting factor. This paper proposes an iterative structured multichannel receiver algorithm that jointly estimates the communication channels and desired data while canceling interference. A general way of adding training redundancy to a data frame is also introduced. From simulations the proposed method is shown to achieve low bit error rates also in the presence of strong interference. These simulations also show that by distributing the training information in a data burst elaborately further improvements in performance are achievable.

关键词： Channel estimation Interference cancellation Intersymbol interference Radio transmitters Intelligent networks Wireless sensor networks Iterative algorithms Antenna arrays Receiving antennas Transmitting antennas

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