ABSTRACT
In this paper, we devise a sparse array design algorithm for frequency-invariant (FI) beam pattern synthesis. Our approach is still based on considering the optimization process as a sequence of weighted <Formula format="inline"><TexMath><?TeX $\ell 1$ ?></TexMath><File name="a00--inline1" type="gif"/></Formula> optimizations under multiple convex constraints. The proposed method uses the alternating direction penalty method (ADPM) to solve the weighted <Formula format="inline"><TexMath><?TeX $\ell 1{\rm{\ }}$ ?></TexMath><File name="a00--inline2" type="gif"/></Formula>norm problem, and admits closed-form solutions at each ADPM iteration. A set of examples for the synthesis of FI patterns with the multiple simultaneous crossover frequency-invariant (FI) patterns, are presented to validate the effectiveness and advantages of the proposed method. Simulation results exhibit excellent performance of the proposed method, which is comparable to that of the previous methods. The synthesized 6 and 11 cross FI beams took 0.095 and 0.258 days, respectively.
- D. B. Ward, R. A. Kennedy, and R. C. Williamson, “Theory and design of broadband sensor arrays with frequency invariant far-field beam patterns,” J. Acoust. Soc. Amer., vol. 97, no. 2, pp. 1023–1034, Feb. 1995.Google ScholarCross Ref
- J. Benesty, J. Chen, Y. Huang, J. Dmochowski, On microphone-array beamforming from a MIMO acoustic signal processing perspective, IEEE Trans. Audio,Speech, Lang. Process. 15 (3) (2007) 1053–1065, doi:10.1109/TASL.2006.885251.Google ScholarDigital Library
- E. Stytsenko, M. Poletti, M. Meijer, N. Scott, Frequency invariant beamformers for underwater sound, in: Oceans 2019 MTS/IEEE Seattle, 2019, pp. 1–9, doi:10.23919/OCEANS40490.Google ScholarCross Ref
- X. Zhao, G. Huang, J. Chen, J. Benesty, On the design of 3D steerable beamformers with uniform concentric circular microphone arrays, IEEE/ACM Trans.Audio, Speech, Lang. Process. 29 (2021) 2764–2778, doi:10.1109/TASLP.2021.3103129.Google ScholarDigital Library
- E. J. Candès, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted l1 minimization,” J. Fourier Analy. Appl., vol. 14, pp. 877–905, Dec. 2008.Google ScholarCross Ref
- A. Trucco and V. Murino, “Stochastic optimization of linear sparse arrays,” IEEE J. Oceanic Eng., vol. 24, no. 3, pp. 291–299, Jul. 1999.Google ScholarCross Ref
- M. B. Hawes and W. Liu, “Robust sparse antenna array design via compressive sensing,” presented at the Int. Conf. Digit. Signal Process., Fira, Greece, Jul. 2013.Google ScholarCross Ref
- Y. Liu, Q. H. Liu, and Z. Nie, “Reducing the number of elements in the synthesis of shaped-beam patterns by the forward-backward matrix pencil method,” IEEE Trans. Antennas Propag., vol. 58, no. 2, pp.604–608, Feb. 2010.Google ScholarCross Ref
- Y. Liu, L. Zhang, L. Ye, Z. Nie, and Q. H. Liu, “Synthesis of sparse arrays with frequency-invariant-focused beam patterns under accurate sidelobe control by iterative second-order cone programming,” IEEE Trans. Antennas Propag., vol. 63, no. 12, pp. 5826–5832, Dec. 2015.Google ScholarCross Ref
- Y Liu, J Cheng, K D Xu, S W Yang , Q H Liu , and Y. J Guo ,” Reducing the Number of Elements in the Synthesis of a Broadband Linear Array With Multiple Simultaneous Frequency-Invariant Beam Patterns” IEEE Trans. Antennas Propag., VOL. 66, NO. 11,PP.5838-5749, NOVEMBER 2018.Google ScholarCross Ref
- D. Gabay, “Applications of the method of multipliers to variational inequalities,” in Augmented Lagrangian Methods: Applications to the Solution of Boundary-Value Problems. Amsterdam, The Netherlands: North Holland, 1983.Google ScholarCross Ref
- Y. Wang, J. Yang, W. Yin, and Y. Zhang, “A new alternating minimization algorithm for total variation image reconstruction,” SIAM J. Imag. Sci., vol. 1, no. 3, pp. 248–272, Aug. 2008.Google ScholarDigital Library
- S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1–122, Jan. 2011.Google ScholarDigital Library
- T. Erseghe, “A distributed and maximum-likelihood sensor network localization algorithm based upon a nonconvex problem formulation,” IEEE Trans. Signal Inf. Process. Netw., vol. 1, no. 4, pp. 247–258, Dec. 2015.Google ScholarCross Ref
- J. Liang, G. Yu, B. Chen, and M. Zhao, “Decentralized dimensionality reduction for distributed tensor data across sensor networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 27, no. 11, pp. 2174–2186, Nov. 2016.Google ScholarCross Ref
- J. Liang, H. C. So, J. Li, and A. Farina, “Unimodular sequence design based on alternating direction method of multipliers,” IEEE Trans. Signal Process., vol. 64, no. 20, pp. 5367–5381, Oct. 2016.Google ScholarDigital Library
- X. Yu, G. Cui, J. Yang, J. Li, L. Kong, Quadratic optimization for unimodular sequence design via an ADPM framework, IEEE Trans. Signal Process. 68 (2020)3619–3634, doi:10.1109/TSP.2020.2998637.Google ScholarCross Ref
- J Zhang, P Gong, Y Wu , L Li, L Yu, “Frequency-invariant beamformer design via ADPM approach” Signal Processing 204 (2023) 108814, 25 October 2022Google ScholarDigital Library
- J. Liang, X. Zhang, “Sparse Array Beampattern Synthesis via Alternating Direction Method of Multipliers,” IEEE Trans. Antennas Propag., VOL. 66, NO. 5, MAY 2018Google Scholar
- Atul Adya, Paramvir Bahl, Jitendra Padhye, Alec Wolman, and Lidong Zhou. 2004. A multi-radio unification protocol for IEEE 802.11 wireless networks. In Proceedings of the IEEE 1st International Conference on Broadnets Networks (BroadNets’04) . IEEE, Los Alamitos, CA, 210–217. https://doi.org/10.1109/BROADNETS.2004.8Google ScholarDigital Library
Index Terms
- Sparse Broadband Array Synthesis via ADPM
Recommendations
Frequency-invariant beamformer design via ADPM approach
Highlights- Under the constraints of mean-square error, we formulate a new beampattern matching model of microphone based on the superdirective beamforming.
AbstractFrequency-invariant (FI) beamformers play an important role in suppressing speech waveform distortions. Due to the perfect FI beampattern (FIB), differential microphone arrays (DMAs) have been widely used in practical applications like ...
Pattern synthesis with minimum mainlobe width via sparse optimization
AbstractThis paper presents a pattern synthesis algorithm achieving minimum mainlobe width via sparse optimization. The problem of synthesizing a pattern with minimum mainlobe width is formulated as a sparse optimization problem with l 0 norm ...
Highlights- Achieving minimum mainlobe width is formulated as sparse optimization problem with l 0 norm.
Array pattern synthesis via sparse weight vector finding and convex programming
WiCOM'09: Proceedings of the 5th International Conference on Wireless communications, networking and mobile computingIn array pattern synthesis, smaller aperture or fewer elements are expected to obtain the desired array performance. In this paper, a novel approach is proposed to design a non-uniform array with a desired pattern. First, this problem is formulated as ...
Comments