Optimizing and Post Processing of a Smart Beamformer for Obstacle Retrieval

Lay-Ekuakille, A.; Vergallo, P.; Saracino, D.; Trotta, A.

doi:10.1109/jsen.2011.2169782

Cited by 39 publications

(15 citation statements)

References 16 publications

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“…Moreover, some specific findings have been included for shape detection and recovery. The research is a follow up of the previous one [18] where a constructed architecture has been tested. The system, as summarized in Figure 4, contains an array of nxm (n=5, m=5) microphones, n buffers, an array for signal conditioning, a driving circuit, a beamformer, a dsPIC control, a transmitting circuit based on 555 timer, a ceramic transducer 328ST160 of Prowave, and a backscattered receiving circuit.…”

Section: Previous Work and Assumptionsmentioning

confidence: 99%

Modeling and Designing a Full Beamformer for Acoustic Sensing and Measurement

Lay-Ekuakille

Giannoccaro

Casciaro³

et al. 2017

International Journal on Smart Sensing and Intelligent Systems

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-Acoustic sensing is a viable approach for solving issues related to many applications, namely, biomedical, distance measurements, mechanical, health infrastructure monitoring, etc. It is generally sustainable and of no negative impact on the object under test. The use of acoustic sensing under beamforming technique is an important asset to be exploited, especially for the aforementioned applications. This paper illustrates a generalized approach of modeling and designing a full beamfomer using two specific classes: LCMP (Linear Constrained Minimum Power) beamformers that are used to overcome robustness limitations and MVDR (Minimum Variance Distortionless Response) beamformers. Any aspect of modeling and designing is always related to the DOA (Direction of Arrival). The obtained results are based on assumptions extracted from an actual case of constructed system. Index terms: Acoustic sensing, Beamforming, Ultrasonic shape detection, DOA, Distance measurement, array of sensors, interference-to-noise ratio (INR), signal-to-noise ratio (SNR).

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Section: Previous Work and Assumptionsmentioning

confidence: 99%

Modeling and Designing a Full Beamformer for Acoustic Sensing and Measurement

Lay-Ekuakille

Giannoccaro

Casciaro³

et al. 2017

International Journal on Smart Sensing and Intelligent Systems

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“…In the first test case, we consider an operating frequency of 32.8 kHz, in accordance to ultrasonic sensor presented in [12]. The linear array consists of 10 sensors, and we suppose that two signals coming from different spatial directions are picked up by the sensor array.…”

Section: Beamforming and Music Limitationsmentioning

confidence: 99%

“…The distance sensor-point is often measured by means of time of flight [12]. The azimuth and elevation angles are obtained from the measurements of direction of arrival (DOA).…”

Section: Final Commentsmentioning

confidence: 99%

“…It depends on the geometry of the array and the number of elements, but large-size arrays are often not desirable in many applications. In [12] we have presented a technique for improving the accuracy in the estimate of the DOAs in order to retrieve more information about the scanning objects, such as their shape. The concept of spatial sparsity is useful in DOA estimation problem because it allows to hold only the information of interest and to set to zero unnecessary one.…”

Section: Introductionmentioning

confidence: 99%

“…This sparsity must be set a priori in the field signal, that is the signal incident on the array. The method presented here is an enhanced version of the method proposed in [12]. The estimate approach is optimized by an iterative method based on the minimization of two costs, one related to goodness of data fitting, and other dealing with maintenance of the spatial sparsity of the signal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sparsity of the Field Signal-Based Method for Improving Spatial Resolution in Antenna Sensor Array Processing

Vergallo¹,

Lay-Ekuakille²,

Caratelli³

2013

PIER

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Abstract-The goal of array processing is to gather information from propagating radio-wave signals, as their Direction Of Arrival (DOA). The estimation of the DOA can be carried out by extracting the information of interest from the steering vector relevant to the adopted antenna sensor array. Such task can be accomplished in a number of different ways. However, in source estimation problems, it is essential to make use of a processing algorithm which feature not only good accuracy under ideal working conditions, but also robustness against non-idealities such as noise, limitations in the amount of collectible data, correlation between the sources, and modeling errors. In this work particular attention is devoted to spectrum estimation approaches based on sparsity. Conventional algorithms based on Beamforming fail wherein the radio sources are not within Rayleigh resolution range which is a function of the number of sensors and the dimension of the array. DOA estimation techniques such as MUSIC (Multiple Signal Classifications) allow having a larger spatial resolution compared to Beamforming-based procedures, but if the sources are very close and the Signal to Noise Ratio (SNR) level is low, the resolution turns to be low as well. A better resolution can be obtained by exploiting sparsity: if the number of sources is small, the power spectrum of the signal with respect to the location is sparse. In this way, sparsity can enhance the accuracy of the estimation. In this paper, an estimation procedure based on the sparsity of the radio signals and useful to improve the conventional MUSIC method is presented and analyzed. The sparsity level is set in order to focus the signal energy only along the actual direction of arrival. The obtained numerical results have shown an improvement of the spatial resolution as well as a reduced error in DOA estimation with respect to conventional techniques.

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