the proceedings contain 94 papers. the topics discussed include: federated channel learning for intelligent reflecting surfaces with fewer pilot signals;statistical analyses of measured forward-looking sonar echo data...
ISBN:
(纸本)9781665406338
the proceedings contain 94 papers. the topics discussed include: federated channel learning for intelligent reflecting surfaces with fewer pilot signals;statistical analyses of measured forward-looking sonar echo data in a shallow water environment;passive angle-doppler profile estimation for narrowband digitally modulated wireless signals;dynamic TDD enabled distributed antenna array massive MIMO system;joint source enumeration and direction finding without eigendecomposition for satellite navigation receiver;sparse signal recovery using a binary program;gradient-descent adaptive filtering using gradient adaptive step-size;non-coherent source localization with distributed sensorarray networks;and joint location and channel error optimization for beamforming design for multi-RIS assisted MIMO system.
the proceedings contain 162 papers. the topics discussed include: 2D DOA estimation for uniform rectangular array with one-bit measurement;3D parametric channel estimation for multi-user massive-MIMO OFDM systems;a bl...
ISBN:
(纸本)9781728119465
the proceedings contain 162 papers. the topics discussed include: 2D DOA estimation for uniform rectangular array with one-bit measurement;3D parametric channel estimation for multi-user massive-MIMO OFDM systems;a blind direction of arrival and mutual coupling estimation scheme for nested array;a compressive sensing approach for single-snapshot adaptive beamforming;a general ESPRIT method for noncircularity-based incoherently distributed sources;a general framework for the robustness of structured difference coarrays to element failures;a gridless method for DOA estimation under the coexistence of mutual coupling and unknown nonuniform noise;a gridless wideband doa estimation based on atomic norm minimization;a new hyperspectral compressed sensing method for efficient satellite communications;a note on the maximum number of sources in DOA estimation by mode;and a novel NLOS target localization method with a synthetic bistatic MMW radar.
Jun.8～11,2020 Hangzhou,China Submission deadline:Dec.15,2019 ABOUT CONFERENCE the SAM workshop is an important ieeesignalprocessing Society event dedicated to sensorarray and multichannelsignalprocessing with ab...
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Jun.8～11,2020 Hangzhou,China Submission deadline:Dec.15,2019 ABOUT CONFERENCE the SAM workshop is an important ieeesignalprocessing Society event dedicated to sensorarray and multichannelsignalprocessing with about 200 *** organizing committee invites the international community to contribute with state-of-the-art developments in the field.
Graph signalprocessing (GSP) can be applied as a modeling tool to study and optimally configure IoT sensor networks. Such networks are often characterized by stringent power requirements and a high probability of sen...
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ISBN:
(纸本)9781665406338
Graph signalprocessing (GSP) can be applied as a modeling tool to study and optimally configure IoT sensor networks. Such networks are often characterized by stringent power requirements and a high probability of sensor fault. In this scenario, understanding and governing the response on a sample of the sensors is critical to maximizing network lifetime and spreading out maintenance time. the aim of this paper is to verify the ability of the GSP to model and provide answers to these goals.
the following topics are dealt with: arraysignalprocessing; direction-of-arrival estimation; minimisation; MIMO radar; radar signalprocessing; MIMO communication; acoustic signalprocessing; optimisation; covarianc...
the following topics are dealt with: arraysignalprocessing; direction-of-arrival estimation; minimisation; MIMO radar; radar signalprocessing; MIMO communication; acoustic signalprocessing; optimisation; covariance matrices; computational complexity.
In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-tointerference-plus-noise ratio (SINR). the ...
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ISBN:
(纸本)9781665406338
In this paper, we devise a sparse array design algorithm for adaptive beamforming. Our strategy is based on finding a sparse beamformer weight to maximize the output signal-tointerference-plus-noise ratio (SINR). the proposed method uses the alternating direction method of multipliers (ADMM), and admits closed-form solutions at each ADMM iteration. Numerical results exhibit excellent performance of the proposed method, which is comparable to that of the exhaustive search approach.
In this paper, we improve estimation of the Doppler shift present in signals that originate from multiple wireless digital communication transmitters. We deploy a uniform linear array (ULA) that overhears the frequenc...
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ISBN:
(纸本)9781665406338
In this paper, we improve estimation of the Doppler shift present in signals that originate from multiple wireless digital communication transmitters. We deploy a uniform linear array (ULA) that overhears the frequency band of interest, hence an unauthorized wireless receiver (URx), and propose an algorithm for extracting the symbol duration from the unknown modulated data. the baseband wireless modulated signal is stripped from its data-induced phase shifts, allowing us to calculate high quality estimates of the periodogram, the spectrogram, and the angle-Doppler profile. Performance results show that high quality results without artifacts from digital modulation can be obtained.
Sparsity based signal recovery has seen great success in solving underdetermined systems of equations. this success is due, in large part, to a relaxation: the l0-norm is replaced by the l1-norm, which results in a co...
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ISBN:
(纸本)9781665406338
Sparsity based signal recovery has seen great success in solving underdetermined systems of equations. this success is due, in large part, to a relaxation: the l0-norm is replaced by the l1-norm, which results in a convex problem. However, such a relaxation may lead to a suboptimal solution, one that may even be asymptotically biased. We propose formulating sparse signal recovery as a binary program, and we derive the conditions under which such a formulation perfectly recovers the signal support. this derivation equips us with a constraint on the dictionary's spectrum. However, designing such a dictionary is a combinatorial problem, so we suggest a heuristic which can be readily satisfied using the alternating projections algorithm. Applied to angle of arrival (AoA) estimation using a sensorarray, we show how this paradigm outperforms boththe basis pursuit l1-norm relaxation and the Matrix Pencil method.
the non-coherent source localization problem based on distributed sensorarrays can be formulated into a group sparsity based phase retrieval problem where only the magnitude (absolute value) of the received signals i...
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ISBN:
(纸本)9781665406338
the non-coherent source localization problem based on distributed sensorarrays can be formulated into a group sparsity based phase retrieval problem where only the magnitude (absolute value) of the received signals is available. Under such a framework, a two-dimensional localization method is proposed. Unlike traditional source localization methods, random phase errors at sensors of the distributed array will not affect estimation results by the proposed method. Simulation results indicate that the proposed non-coherent source localization method outperforms the traditional one in the presence of large phase errors, while still maintains an acceptable accuracy in the absence of phase errors.
the problem of the decentralized Direction-ofArrival (DoA) estimation and tracking is addressed, where the sample covariance matrix is represented as a recursive update of rank-one components. Due to the fact that the...
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ISBN:
(纸本)9781665406338
the problem of the decentralized Direction-ofArrival (DoA) estimation and tracking is addressed, where the sample covariance matrix is represented as a recursive update of rank-one components. Due to the fact that the conventional decentralized power method is a batch approach and involves high communication costs, we propose a distributed DoA estimation method combining the decentralized online eigenvalue decomposition and the ESPRIT algorithm. the Push-Sum protocol is applied to realize the local interactions among neighboring subarrays. the simulation results show that the Root Mean Square Error (RMSE) performance of our distributed algorithm surpasses that of the power method based d-ESPRIT algorithm in the DoA estimation with lower total communication cost, and that of the decentralized Normalized Oja (d-NOja) method in DoA tracking applications with faster convergence speed.
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