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sensor array and multichannel signal processing workshop

作者： Yulei Qian Daiyin Zhu Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

An algorithm is presented in this paper to focus the azimuth periodically gapped SAR (Synthetic Aperture Radar) raw data. The proposed algorithm mainly contains phase multiplication in range frequency domain and sparse reconstruction in range Doppler domain. The phase multiplication in range frequency domain aims to acquire sparser data in range Doppler domain. Then, the complex iterative thresholding algorithm is utilized to reconstruct the complete SAR data in range Doppler domain. The iterative thresholding algorithm is extended to cope with the complex SAR data. Afterwards, the traditional SAR focusing methods are capable of obtaining image from the recovered data. The proposed method performs well on suppressing ghost targets induced by gapping. Point target simulation is implemented to assess the validity of the proposed method. In addition, real SAR data experiment is also utilized to demonstrate the effectiveness of the proposed method.

关键词： Azimuth Frequency-domain analysis signal processing algorithms Focusing Iterative algorithms Radar polarimetry Doppler effect

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Parallel Factor Decomposition Channel Estimation in RIS-Assisted Multi-User MISO Communication

Parallel Factor Decomposition Channel Estimation in RIS-Assi...

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sensor array and multichannel signal processing workshop

作者： Li Wei Chongwen Huang George C. Alexandropoulos Chau Yuen Engineering Product Development Singapore University of Technology and Design Singapore Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low power configuration enabling massive connectivity and low latency communications. Channel estimation in RIS-based systems is one of the most critical challenges due to the large number of reflecting unit elements and their distinctive hardware constraints. In this paper, we focus on the downlink of a RIS-assisted multi-user Multiple Input Single Output (MISO) communication system and present a method based on the PARAllel FACtor (PARAFAC) decomposition to unfold the resulting cascaded channel model. The proposed method includes an alternating least squares algorithm to iteratively estimate the channel between the base station and RIS, as well as the channels between RIS and users. Our selective simulation results show that the proposed iterative channel estimation method outperforms a benchmark scheme using genie-aided information. We also provide insights on the impact of different RIS settings on the proposed algorithm.

关键词： Training Simulation Wireless networks Channel estimation signal processing algorithms MISO communication Benchmark testing

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Sparse Subspace Clustering with Linear Subspace-Neighborhood-Preserving Data Embedding

Sparse Subspace Clustering with Linear Subspace-Neighborhood...

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sensor array and multichannel signal processing workshop

作者： Jwo-Yuh Wu Liang-Chi Huang Wen-Hsuan Li Hau-Hsiang Chan Chun-Hung Liu Rung-Hung Gau Institute of Communications Engineering National Chiao Tung University Taiwan Department of Electrical and Computer Engineering Mississippi State University USA

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Data dimensionality reduction via linear embedding is a typical approach to economizing the computational cost of machine learning systems. In the context of sparse subspace clustering (SSC), this paper proposes a two-step neighbor identification scheme using linear neighborhood-preserving embedding. In the first step, a quadratically- constrained ℓ 1 -minimization algorithm is solved for acquiring the side subspace neighborhood information, whereby a linear neighborhood-preserving embedding is designed accordingly. In the second step, a LASSO sparse regression algorithm is conducted for neighbor identification using the dimensionality- reduced data. The proposed embedding design explicitly takes into account the subspace neighborhood structure of the given data set. Computer simulations using real human face data show that the proposed embedding not only outperforms other existing dimensionality-reduction schemes but also improves the global data clustering accuracy when compared to the baseline solution without data compression.

关键词： Dimensionality reduction Machine learning algorithms Computer simulation signal processing algorithms Clustering algorithms Data compression Euclidean distance

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Feasible Sparse Spectrum Fitting of DOA and Range Estimation for Collocated FDA-MIMO radars

Feasible Sparse Spectrum Fitting of DOA and Range Estimation...

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sensor array and multichannel signal processing workshop

作者： Jingyu Cong Xianpeng Wang Mengxing Huang Guoan Bi State Key Laboratory of Marine Resource Utilization in South China Sea Hainan University Haikou China State Key Laboratory of Marine Resource Utilization in South Hainan University Haikou China School of EEE Nanyang Technological University Singapore

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

The size of an over complete dictionary seriously affects the computation speed of on-grid sparse algorithms. In the case of multi parameter estimation, the required dictionary size increases rapidly by multiplication to ensure the accuracy of the results. Therefore, it becomes infeasible to estimate all of the parameters directly by on-grid methods. In this paper, the feasible sparse spectrum fitting (SpSF) algorithm for computing both the direction of arrival (DOA) and range estimation in collocated FDA-MIMO radars is introduced. Firstly, due to fact that a receive spatial frequency only depends on the angle, a covariance fitting technique for data preprocessing is adopted to reshape the data for DOA estimation. Next, the range is calculated in the transmit-receive spatial frequency domain by the SpSF algorithm. In addition, to improve the computational efficiency for an increased number of targets, the traditional convex optimization is replaced with a one-dimensional peak search approximation. Numerical simulations are carried out to verify the effectiveness of the proposed approach.

关键词： Direction-of-arrival estimation Dictionaries Frequency-domain analysis Fitting Data preprocessing Estimation signal processing algorithms

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A New Hyperspectral Compressed Sensing Method for Efficient Satellite Communications

A New Hyperspectral Compressed Sensing Method for Efficient ...

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sensor array and multichannel signal processing workshop

作者： Chia-Hsiang Lin José M. Bioucas Dias Tzu-Hsuan Lin Yen-Cheng Lin Chi-Hung Kao Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Directly transmitting the huge amount of typical hyperspectral data acquired on satellite to the ground station is inefficient. This paper proposes a new compressed sensing strategy for hyperspectral imagery on spaceborne sensors systems. As the onboard computing/storage resources are limited, e.g., on CubeSat, the measurement strategy should be computationally very light. Furthermore, considering the limited communication bandwidth, a very low sampling rate is desired. Our encoder accounts for these requirements by separately recording the spatial details and the spectral information, both of which essentially require only simple averaging operators. Our measurement strategy naturally induces a reconstruction criterion that can be elegantly interpreted as a well-known fusion problem in satellite remote sensing, allowing the adoption of a convex optimization method for simple and fast decoding. Our method, termed spatial/spectral compressed encoder (SPACE), is experimentally evaluated on real hyperspectral data, showing superior efficacy in terms of both sampling rate and reconstruction accuracy.

关键词： Image sensors Satellites sensor systems Convex functions sensors Decoding Image reconstruction

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Waveform Design for the Radar-Communications Coexistence With Good Correlation Properties : (Invited Paper)

Waveform Design for the Radar-Communications Coexistence Wit...

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sensor array and multichannel signal processing workshop

作者： Yongzhe Li Ran Tao School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

We deal with the waveform design problem for the radar-communications coexistence (RCC) that ensures low levels of waveform correlations and meanwhile implements efficient information embedding (IE) via the fast-time waveform transmission. To tackle the problem, we propose two unimodular waveform designs, in which the integrated sidelobe level (ISL) is evaluated to find the optimal waveform correlations for the radar transmission and the attributes of waveform phases with large degrees of freedom are exploited to embed communication symbols. The first design is an offline strategy simply using the phase initialization for waveform generation to embed communication symbols. It requires only a predefined database that maps the waveform initialization to the emitted version of it, which gives the lowest ISL of the waveform. The second design serves as an online strategy employing the phase difference between any two waveform elements for the implementation of IE, and its accuracy to approaching desirable phases is elaborately transformed into a quadratic form and jointly designed with the minimization of ISL. We formulate this design as a nonconvex optimization problem, whose compound objective involves the outer product of the waveform vector that is to be designed. Moreover, we apply the majorization-minimization technique to solve the formulated problem, based on which we also devise an efficient algorithm that iterates with a closed-form solution. Simulation results verify the effectiveness of the proposed designs for the RCC.

关键词： Correlation Closed-form solutions Databases Simulation signal processing algorithms Radar Minimization

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MIMO Radar Waveform Joint Optimization Design in Time and Frequency Domain

MIMO Radar Waveform Joint Optimization Design in Time and Fr...

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sensor array and multichannel signal processing workshop

作者： Chun-hua Chu Yi-jun Chen Qun Zhang Ying Luo Institute of Information and Navigation Air Force Enginnering University Xi’an China Mechanical and Electrical Engineering College Hainan University Haikou China College of Information Enginnering Enginnering University of PAP Xi’an People’s Republic of China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

The performance of MIMO radar is directly affected by its transmitting waveforms. The waveform design is one of the critical issues in the design of MIMO radar system. In this paper, a new MIMO radar waveform design method based on time domain and frequency domain joint optimization is proposed. Firstly, a continuous phase coded signal waveform set is chosen to be the optimization objective variable. The design objectives is that all waveforms in the set are orthogonal in time domain, and the power spectral density (PSD) of every waveform approximates to the desired distribution in the frequency domain. According to the requirements, the problems in time domain and frequency domain are analyzed, respectively. Meanwhile, two objective functions based on minimizing the weighted correlation sidelobe level (MWISL) and minimizing the stopband power spectral density (MSPSD) are established. Then, an optimal scale factor is introduced to integrate the time domain and frequency domain into a combined model and a close form solution of code element is deduced through proper simplification and equalization of the time-frequency (T-F) joint optimization model. A recursive algorithm is obtained by summarizing the derivation process. At last, numerical examples prove the effectiveness of the proposed method in matching desired spectrum distribution and decreasing correlation sidelobe.

关键词： Measurement Time-frequency analysis Correlation MIMO radar Design methodology signal processing algorithms Numerical models

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Improved Model-Based Channel Tracking for Underwater Acoustic Communications

Improved Model-Based Channel Tracking for Underwater Acousti...

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sensor array and multichannel signal processing workshop

作者： Yuxing Wang Jun Tao Le Yang Fei Yu Chunguo Li Xiao Han Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China Department of ECE University of Canterbury Christchurch New Zealand Acoustic Science and Technology Laboratory Harbin Engineering University Harbin China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

For tracking time-varying underwater acoustic (UWA) channels, a state-space model based scheme generally outperforms a direct adaptive method. The success for the former depends on the choice of a proper state transition model as well as accurate estimation of the model parameters. The autoregressive (AR) transition model has proven to be useful and the key is to determine the AR coefficients so as to achieve a good channel tracking performance. In this paper, we revisit the problem of determining the AR coefficients via Yule-Walker equation, for which the required autocorrelations are estimated as an ensemble average of estimated channel impulse responses (CIRs). Different from existing scheme employing least squares (LS) channel estimation, we propose to obtain a sequence of CIR estimations via adaptive schemes. The advantage is twofold: first, complexity reduction is achieved and the saving can be significant for a UWA channel with extensive delay spread; second, improved tracking performance is achieved as the implicit assumption by the LS method that the channel remains constant over a block is not required. We also propose to dynamically update the autocorrelations and AR coefficients as the channel tracking progresses, such that the variation in the channel statistical property can be captured. Both simulations and experimental results verify the performance gain of the proposed model-based channel tracking scheme.

关键词： Adaptation models Correlation Computational modeling Channel estimation Estimation Delays Complexity theory

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Deterministic Coherence-Based Performance Guarantee for Noisy Sparse Subspace Clustering using Greedy Neighbor Selection

Deterministic Coherence-Based Performance Guarantee for Nois...

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sensor array and multichannel signal processing workshop

作者： Jwo-Yuh Wu Wen-Hsuan Li Liang-Chi Huang Yen-Ping Lin Chun-Hung Liu Rung-Hung Gau Institute of Communications Engineering Department of Electrical and Computer Engineering and College of Electrical Engineering National Chiao-Tung University Taiwan Department of Electrical and Computer Engineering Mississippi State University USA

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Sparse subspace clustering (SSC) using greedy- based neighbor selection, such as matching pursuit (MP) and orthogonal matching pursuit (OMP), has been known as a popular computationally-efficient alternative to the conventional ℓ 1 -minimization based solutions. Under deterministic bounded noise corruption, in this paper we derive coherence-based sufficient conditions guaranteeing correct neighbor identification using MP/OMP. Our analyses exploit the maximum/minimum inner product between two noisy data points subject to a known upper bound on the noise level. The obtained sufficient condition clearly reveals the impact of noise on greedy-based neighbor recovery. Specifically, it asserts that, as long as noise is sufficiently small and the resultant perturbed residual vectors stay close to the desired subspace, both MP and OMP succeed in returning a correct neighbor subset. Extensive numerical experiments are used to corroborate our theoretical study. A striking finding is that, as long as the ground truth subspaces are well-separated from each other, MP-based iterations, while enjoying lower algorithmic complexity, yields smaller perturbed residuals, thereby better able to identify correct neighbors and, in turn, achieving higher global data clustering accuracy.

关键词： Sufficient conditions Upper bound Matching pursuit algorithms signal processing algorithms sensors Noise measurement Standards

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Blind Gain and Phase Calibration via Sparse Spectral Methods

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IEEE TRANSACTIONS ON INFORMATION THEORY 2019年第5期65卷 3097-3123页

作者： Li, Yanjun Lee, Kiryung Bresler, Yoram Univ Illinois Urbana IL 61801 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

Blind gain and phase calibration (BGPC) is a bilinear inverse problem involving the determination of unknown gains and phases of the sensing system, and the unknown signal, jointly. BGPC arises in numerous applications, e.g., blind albedo estimation in inverse rendering, synthetic aperture radar autofocus, and sensor array auto-calibration. In some cases, sparse structure in the unknown signal alleviates the illposedness of BGPC. Recently, there has been renewed interest in solutions to BGPC with careful analysis of error bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem and propose to solve it via power iteration, or in the sparsity or joint sparsity case, via truncated power iteration. Under certain assumptions, the unknown gains, phases, and the unknown signal can be recovered simultaneously. Numerical experiments show that power iteration algorithms work not only in the regime predicted by our main results, but also in regimes where theoretical analysis is limited. We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e.g., with noisy measurement or with a bad initial estimate.

关键词： Auto-calibration greedy algorithm inverse rendering multichannel blind deconvolution nonconvex optimization power method SAR autofocus sensor array processing truncated power iteration

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