Broadband sensor location selection using convex optimization in very large scale arrays

Lai, Yenming Mark; Balan, Radii; Claussen, Heiko; Rosea, J.

doi:10.1109/waspaa.2013.6701889

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…This work extends that of [7] by studying the single frequency case in detail. The performance of the proposed λ-method is compared against the performance of an exhaustive search, showing how close to optimal the λ-method achieves.…”

Section: Relation To Prior Workmentioning

confidence: 63%

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Optimal beam pattern design for very large sensor arrays with sparse sampling

Lai

Bălan

Claussen

et al. 2013

2013 Asilomar Conference on Signals, Systems and Computers

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The main goal of this work is to adaptively employ a large set of microphone sensors distributed in multiple dimensions to scan an acoustic field. Processing data from a large set of sensors will necessarily involve intelligent definition of suitable subsets of sensors active at various times. This paper presents a novel method for optimal beam pattern design for large scale sensor arrays using convex and non-convex optimization techniques to define optimal subsets of sensors capable to select a target location while suppressing a large number of interferences. The first of two optimization techniques we present, uses a LASSO-type approach to convexify the corresponding combinatorial optimization problem. The second approach employs simulated annealing to search for optimal solutions with a fixed size subset of active sensors. Our numerical simulations show that for scenarios of practical interest, the convex optimization solution is almost optimal.Index Terms-array processing, beam pattern design, sparse sampling, very large scale arrays, convex optimization INTRODUCTIONConsider a large scale sensor array having N sensors (think of N in the order 1000's) that monitors a surveillance area. Data could be collected from all sensors, processed locally, and further communicated to a central processing unit. For example, for N = 10000 sensors and a data sampling rate of 100000 samples per second, the bandwidth requirements for a system performing exhaustive sampling is 1Gsamples/sec. Alternatively, the central processing unit could implement a policy of sparse spatial sampling where only a subset of sensors is polled for data at any time. This may be desirable for practical reasons: cost (in power consumption, communication or processing bandwidth) is simply too high for an exhaustive strategy. This paper advocates the need for advanced, dynamic control and signal processing of what data should be manipulated in place of the full extent of the data, and it introduces novel strategies and formal analysis of what can be achieved when only a sparse subset of sensors is sampled simultaneously.Assume a surveillance area that consists of a set of point-like sources that we are ultimately interested to separate. Our task is to sample the spatial field using a maximum of D sensors of the very large array (D N ) and then to process the raw data in order to estimate each source (see also Figure 1 for a setup scenario). We will thus design and analyze sparse spatial sampling strategies with near-optimal performance. Specifically, we target designs that simultaneously maximize target location gain and minimize the largest

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Section: Relation To Prior Workmentioning

confidence: 63%

“…(1) We choose a particular value for λ that produces multiple non-zero components in the sequence gn = |wn(f )|, 1 ≤ n ≤ N for the optimizer of (7). Then this sequence is sorted monotonically decreasing, gn 1 ≥ gn 2 ≥ · · · ≥ gn N .…”

Section: Optimization Strategiesmentioning

confidence: 99%

Optimal beam pattern design for very large sensor arrays with sparse sampling

Lai

Bălan

Claussen

et al. 2013

2013 Asilomar Conference on Signals, Systems and Computers

Self Cite

View full text Add to dashboard Cite

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“…Thanks to the outcome of microphones based on the technology called Microelectromechanical Systems (MEMS), developing a large microphone array with several hundreds of microphones becomes easier and low-cost compared with older technologies (Hafizovic et al, 2012;Koyano et al, 2016;Vanwynsberghe et al, 2015). At the same time, original multichannel processing methods anticipate and rely on the use of a great number of sensors: three examples are dereverberation (Chardon et al, 2015), source separation (FitzGerald et al, 2016) and monitoring (Lai et al, 2013). However for most applications, the position of the sensors must be known with a sufficient accuracy (Chen et al, 2015;Himawan et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Geometric calibration of very large microphone arrays in mismatched free field

Vanwynsberghe

Challande

Ollivier

et al. 2019

The Journal of the Acoustical Society of America

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Large microphone arrays are an efficient means for source localization thanks to a wide aperture and a great number of sensors. When such arrays are deployed in situ, accurate geometric calibration becomes essential to obtain the microphone positions. In free field, the classic procedures rely on measured Times Of Arrival (TOA) or Time Differences of Arrival (TDOA) between the microphones and several controlled sources. However free field model mismatches, such as reflectors, generate outliers which severely deteriorate the positioning accuracy. This paper introduces a unified framework for robust calibration using TOA or TDOA, by exploiting an outlier-aware noise model. Thanks to the largeness of the array, the existing outliers are sparse and can be identified by a Lasso regression. From this, three iterative robust solvers a are proposed: (i) for TOA by Robust Multi Dimensional Unfolding, a particular variation of Robust Multi Dimensional Scaling (ii) for TDOA by data predenoising based on sparse and low-rank matrix decomposition, and (iii) for TDOA by jointly identifying the outliers and the geometry. The relevance of outlier-aware approaches is asserted by numerical and experimental tests. Compared with the baseline least-square approaches, the proposed robust solvers significantly improve the positioning accuracy in a free field mismatched by reflectors.

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Broadband sensor location selection using convex optimization in very large scale arrays

Cited by 2 publications

References 19 publications

Optimal beam pattern design for very large sensor arrays with sparse sampling

Optimal beam pattern design for very large sensor arrays with sparse sampling

Geometric calibration of very large microphone arrays in mismatched free field

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