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RATE-DISTRIBUTED BINAURAL LCMV BEAMFORMING FOR ASSISTIVE HEARING IN WIRELESS ACOUSTIC sensor NETWORKS 10

RATE-DISTRIBUTED BINAURAL LCMV BEAMFORMING FOR ASSISTIVE HEA...

引用

10th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Zhang, Jie Heusdens, Richard Hendriks, Richard C. Delft Univ Technol Circuits & Syst CAS Grp Delft Netherlands

ISBN: (纸本)9781538647523

In this paper, we propose a rate-distributed linearly constrained minimum variance (LCMV) beamformer for joint noise reduction and spatial cue preservation for assistive hearing in wireless acoustic sensor networks (WASNs). the WASN can consist of wireless communicating hearing aids, extended with additional wireless microphones. Due to the fact that each sensor node has a limited power budget, it is essential to consider the energy usage when designing algorithms for such WASNs. As the energy usage in terms of data transmission is directly affected by the communication rate, the proposed method optimally distributes the bit rate for each microphone node. the rate distribution is obtained by minimizing the total transmission costs under constraints on the noise reduction performance and spatial cue preservation of interfering sources. In contrast to sensor selection, i.e., binary decisions on the usefulness of a node, rate distribution allows for soft decisions, and, will lead to more degrees of freedom for joint noise reduction and spatial cue preservation than sensor selection. Numerical results show that given a certain noise reduction requirement, the proposed method displays improved energy efficiency and can preserve the spatial cues of more interferers compared to sensor selection approaches.

关键词： Rate allocation sensor selection LCMV noise reduction energy usage binaural cue preservation hearing aids

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Compressive Measurements Exploiting Coprime Frequencies for Direction Finding 10

Compressive Measurements Exploiting Coprime Frequencies for ...

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10th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Guo, Muran Zhang, Yimin D. Chen, Tao Harbin Engn Univ Coll Informat & Commun Engn Harbin Heilongjiang Peoples R China Temple Univ Dept Elect & Comp Engn Philadelphia PA 19122 USA

ISBN: (纸本)9781538647523

Sparse arrays are considered as an effective solution to reduce the system complexity for direction-of-arrival (DOA) estimation as they can resolve more sources than the number of sensors. Inspired by such techniques, the coprime frequency-based array structure was recently proposed to further reduce the number of required sensors. Multiple sensing signals with mutually coprime carrier frequencies are transmitted, and the reflected signals are used to estimate the target DOAs. the cross-correlation obtained from each pair of frequencies provides additional degrees of freedom (DOFs). On the other hand, a compression matrix can be designed to reduce the dimension of the received signal vector, thereby effectively reducing the number of front-end circuit chains. In this paper, we use the compressive measurements of the received signal vector obtained from a coprime frequency-based array structure to estimate the signal DOAs. the proposed scheme can obtain an increased number of DOFs and high estimation accuracy because of the coarray operation and the large array aperture of the coprime frequency-based array structure. Meanwhile, the system complexity is also significantly reduced. the effectiveness of the proposed scheme is validated using simulation results.

关键词： Compressive sensing coprime array coprime frequency DOA estimation group sparsity

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A GRIDLESS MEthOD FOR DOA ESTIMATION UNDER thE COEXISTENCE OF MUTUAL COUPLING AND UNKNOWN NONUNIFORM NOISE 11

A GRIDLESS METHOD FOR DOA ESTIMATION UNDER THE COEXISTENCE O...

引用

ieee 11th sensor array and multichannel signal processing workshop (SAM)

作者： Li, Dan Jiang, Yuanyuan Wu, Xiaohuan Zhu, Wei-Ping Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Networ Nanjing Peoples R China Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781728119465

this paper addresses the issue of direction-of-arrival (DOA) estimation under the coexistence of mutual coupling and nonuniform noise. We first construct a selection matrix based on the structural characteristics of the mutual coupling matrix (MCM) to form a new manifold matrix which is not affected by the mutual coupling effect. then, we employ the covariance fitting criteria to form a semidefinite programming for DOA estimation. Compared to most of existing sparse methods, our method does not require discretization of the angle space, hence is able to achieve super-resolution. Simulations are carried out to verify the effectiveness of the propose method.

关键词： DOA estimation nonuniform noise mutual coupling covariance fitting criteria

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Ordering for Shift-in-Mean of Gauss-Markov Random Fields with Dependent Observations

Ordering for Shift-in-Mean of Gauss-Markov Random Fields wit...

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ieee 7th sensor array and multichannel signal processing workshop (SAM)

作者： Zhang, Jiangfan Wei, Chuanming Blum, Rick S. Lehigh Univ Bethlehem PA 18015 USA

ISBN: (纸本)9781467310710

Previous work on ordered transmission approaches showed significant transmission savings but focused entirely on cases with statistically independent observations at a set of sensor nodes. Here we take the first steps toward applying ordering to cases with statistically dependent observations. While we focus on a particular signal detection problem, we choose one of the most well studied problems, detection of a shift-in-mean for a multivariate Gaussian distribution. We employ the well developed theory of decomposable graphical models, and focus on cases where the observations are taken at a set of sensor nodes which can be grouped into a set of cliques. We assume the nodes within a clique are physically close, so that inner-clique communications can be considered extremely inexpensive. We present the computation of the overall likelihood ratio as a new sum, distinctly different from the sum over a set of independent variables, which implies it is possible to employ ordering over the cliques in an attempt to limit the number of communications from each clique to the place where the clique data will be combined. We present results that imply we can often save a significant portion of these transmission, which is lower bounded by half of the number of cliques. We describe necessary conditions for the result to hold and provide numerical results indicating these conditions are satisfied in many cases of practical interest.

关键词： sensor nodes

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Microphone array speech enhancement by Bayesian estimation of spectral amplitude and phase

Microphone array speech enhancement by Bayesian estimation o...

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2nd ieee sensor array and multichannel signal processing workshop

作者： Balan, R Rosca, J Siemens Corp Res Multimedia & Video Technol Dept Princeton NJ 08540 USA

ISBN: (纸本)0780375513

Microphone arrays provide new opportunities for noise reduction and speech enhancement. this paper presents a novel decomposition of the estimation problems for short-time spectral amplitude (STSA), log STSA, and phase in the Bayesian estimation framework. the decomposition is based on the notion of sufficient statistics for the microphone array case. It nicely generalizes the well-known single-channel Ephraim-Malah estimators [4, 5] to the microphone array case. We also compare noise reduction obtained in the single channel with the two- and four-channel cases on real data.

关键词： Speech enhancement

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Vertical-array-based Contour Reconstruction Algorithm for Airborne Weather Radar 11

Vertical-Array-based Contour Reconstruction Algorithm for Ai...

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ieee 11th sensor array and multichannel signal processing workshop (SAM)

作者： Wang, Yu Wu, Di Zhu, Daiyin Meng, Fanwang Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Nanjing 211106 Peoples R China Avic Leihua Elect Technol Res Inst Wuxi Jiangsu Peoples R China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119465

Aiming at meteorological target, in this paper, a new contour reconstruction algorithm is proposed against the disastrous weather. the algorithm is capable of reconstructing the specific contour of meteorological target by exploiting vertical array of airborne weather radar system. First of all, Burg algorithm is utilized for the enhancement of beam spatial resolution in elevation dimension. then, multi-beam algorithm is employed to form multiple beams and generate multiangle echoes, which further obtains more vertical contour information about weather scenario. Consequently the proposed algorithm, combining Burg algorithm and multi-beam algorithm, distinctly retrieves the vertical contour information and delivers a super-resolution behavior simultaneously. Using simulated radar data, the processing results demonstrate the validity of the presented algorithm. Compared with the so-called interpolation-based conventional methods, the proposed algorithm can achieve more comprehensive and accurate vertical contour reconstruction.

关键词： Vertical array airborne weather radar multi-beam vertical contour reconstruction

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Guaranteed Deep Learning for Reliable Radar signal processing 12

Guaranteed Deep Learning for Reliable Radar Signal Processin...

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ieee 12th sensor array and multichannel signal processing workshop (SAM)

作者： Khobahi, Shahin Mostajeran, Ali Emadi, Mohammad Wang, Pu Soltanalian, Mojtaba Univ Illinois Chicago IL 60607 USA Zadar Labs Inc Santa Clara CA USA Mitsubishi Elect Res Labs Cambridge MA USA

ISBN: (纸本)9781665406338

Recently, there has been a significant level of attention paid to the application of deep learning in radar signal processing. Despite its flexibility, deep learning imposes new challenges in guaranteeing the performance of signal processing systems and in establishing trust with regard to their outcome. this represents a critical bottleneck in the application of deep learning in radar signal processing, where having trust in radar inferences is crucial. In this work, we present a novel implicit deep learning model for learning fast and highly-scalable solvers for a general family of optimization problems commonly encountered in the radar signal processing area, encompassing applications in waveform design and target parameter estimation. Specifically, the proposed methodology is based on generalizing the feed-forward neural networks by introducing implicit layers derived from the dynamics of a fixed-point geometric series. Unlike its black-box data-driven counterparts, the implicit and model-based nature of the proposed neural solver significantly reduces the parameters of the network and implementation cost, and most importantly, makes the network amenable to robustness analysis, as well as deriving performance guarantees/bounds on the output of the model. this presents considerable potential for the adoption of our proposed models in radar applications, be it in the classical settings or emerging applications in autonomous vehicles and automotive radar where a large number of reliable radars are projected to be on the road in the near future.

关键词： Implicit Deep Learning Inference Guarantees Model-Based Deep Learning Radar signal processing Robustness and Reliability

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Gridless Sparsity-Based Localization for Near-Field Sources with Symmetric Linear array 11

Gridless Sparsity-Based Localization for Near-Field Sources ...

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ieee 11th sensor array and multichannel signal processing workshop (SAM)

作者： Zuo, Weiliang Xin, Jingmin Xiao, Tong Zheng, Nanning Sano, Akira Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Xi An Jiao Tong Univ Natl Engn Lab Visual Informat Proc & Applicat Xian 710049 Peoples R China Keio Univ Dept Syst Design Engn Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781728119465

In this paper, we investigate the problem of estimating the directions-of-arrival (DOAs) and ranges of multiple near-field narrowband sources impinging on a symmetric uniform linear array (ULA). By forming a Toeplitz-like correlation matrix from the anti-diagonal elements of the array covariance matrix, a convex optimization problem for the resultant Toeplitz-like matrix reconstruction is established and further a gridless sparsity-based localization for near-field sources is proposed. the DOAs can then be retrieved by using the recovered correlation matrix according to root-MUSIC or Vandermonde decomposition theorem. Additionally, the ranges are obtained through a subspace-based estimator with the corresponding estimated DOAs, while the association of the estimated DOAs and ranges are completed at the same time. Finally, the numerical examples are provided to substantiate the performance of our proposed method, and the simulation results demonstrate that the proposed method provides remarkable and satisfactory estimation performance.

关键词： Near-field DOA estimation source localization Toeplitz covariance matrix gridless method uniform linear array

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An efficient ISAR imaging method based on sliding window STAP 11

An efficient ISAR imaging method based on sliding window STA...

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ieee 11th sensor array and multichannel signal processing workshop (SAM)

作者： Li, Haodong Liao, Guisheng Xu, Jingwei Zhang, Jun Xidian Univ Natl Lab Radar Signal Proc Xian Peoples R China Xian Inst Elect Engn Business Div Air Borne Imaging Radar Xian Peoples R China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119465

ISAR imaging for a marine moving target faces a series of challenges in the airborne radar system, especially the strong clutter interference with Doppler frequency spreading. To alleviate such problems, this paper proposes an efficient ISAR imaging method based on sliding window STAP. In the method, the whole CPI is divided into a series of coherent processing sub-intervals (CPSIs). those CPSIs are generated with sliding window technique and they have identical length. In each CPSI, sub-CPI STAP is adopted to suppress the clutter. After that, the target signal is enhanced in terms of signal-to-clutter-plus-noise ratio (SCNR). Meanwhile, the Doppler frequency linear differences with respect to the azimuth dimension is still maintained, which contributes to the further ISAR imaging by Range-Doppler (RD) algorithm after range migration correction. Comparing with existing full-CPI STAP based method, the proposed method improves ISAR imaging performance while requiring less computational complexity. the simulation experiments are carried out to verify the effectiveness of the proposed method.

关键词： sliding window technique space-time adaptive processing long coherent processing interval strong clutter interference inverse synthetic aperture radar

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A Method to Suppress the Main-beam Deceptive Jamming in FDA-MIMO Radar with Random Polyphase Codes 10

A Method to Suppress the Main-beam Deceptive Jamming in FDA-...

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10th ieee sensor array and multichannel signal processing workshop (SAM)

作者： Lan, Lan Liao, Guisheng Xu, Jingwei Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781538647523

the main-beam deceptive jamming reduces the estimation precision and tracking accuracy of the target, which brings huge resources consume and severe challenges along with threats. Besides, it is impossible to obtain perfectly orthogonal waveforms for all Doppler and delay pairs in the multiple-input and multiple-output (MIMO) radar. In this paper, based on the frequency diverse array (FDA)-MIMO radar with random polyphase codes, we have addressed the problem of the main-beam range deceptive jamming suppression with two steps. Firstly, the true and false targets are distinguished in the joint transmit-receive spatial frequency domain according to the time delay difference in the false target generation. Subsequently, due to the range mismatch, the main-beam deceptive jamming can be suppressed through a range-angle adaptive matched filter. Simulation results are provided to demonstrate the effectiveness of the proposed approach.

关键词： Main-beam deceptive jamming orthogonal waveforms frequency diverse array radar joint range-receive spatial frequency domain range-angle adaptive matched filter

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