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检索条件"任意字段=2008 IEEE Sensor Array and Multichannel Signal Processing Workshop"

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Low-cost Beamforming-based DOA Estimation with Model Order Determination

Low-cost Beamforming-based DOA Estimation with Model Order D...

引用

sensor array and multichannel signal processing workshop

作者： Elias Aboutanios Aboulnasr Hassanien School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Department of Electrical Engineering Wright State University Dayton OH USA

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Direction of Arrival (DOA) estimation algorithms generally assume knowledge of the number of sources. This crucial parameter is either determined by the problem or estimated from the available observations prior to the application of the DOA estimators. Model order estimation (MOE) strategies via information theoretic criteria such as the Akaike Information Criterion (AIC), Minimum Description Length (MDL), and Hannan-Quinn Criterion (HQC), are usually implemented using the singular value decomposition (SVD) which is computationally expensive. In this work, we incorporate the information theoretic criteria directly into the recently proposed Fast Iterative Interpolation Beamformer (FIIB), thus avoiding the SVD. We derive the expressions for the likelihood function as well as the penalty parameters of the three criteria in terms of the number of sources. The resulting FIIB with MOE algorithm is then able to at once determine the number of sources and estimate their parameters. Simulation results demonstrate that the FIIB-based MOE outperforms the SVD-based MOE. Furthermore the FIIB with MDL achieves a performance that is very close to the original FIIB algorithm.

关键词： Interpolation Direction-of-arrival estimation Simulation Computational modeling Estimation signal processing algorithms Frequency estimation

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LPI-based Optimal Radar Power Allocation for Target Time Delay Estimation in Joint Radar and Communications System

LPI-based Optimal Radar Power Allocation for Target Time Del...

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sensor array and multichannel signal processing workshop

作者： Yijie Wang Chenguang Shi Fei Wang Jianjiang Zhou Key Laboratory of Radar Imaging and Microwave Photonics (Nanjing Univ. Aeronaut. Astronaut.) Ministry of Education Naijing China

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

This paper explores the low probability of intercept (LPI)-based optimal radar power allocation for target time delay estimation in joint radar and communications system. The basis of LPI-based optimal radar power allocation is to minimize the total power consumption of radar system under the constraints of a specified target time delay estimation accuracy and a quality of service (QoS) of communications base station. The expression for Cramér-Rao lower bound (CRLB) is analytically derived and applied to gauge target time delay estimation accuracy. The resulting optimization problem is non-convex, which can be solved by the approach of linear programming. Several numerical results are provided to verify the superiority of the proposed radar power allocation in terms of the LPI performance of radar system. It is also shown that the LPI performance of radar benefits from cooperation with the communication system by reducing the total radiated energy of radar.

关键词： Delay effects Estimation Radar Quality of service Radar scattering Radar signal processing Resource management

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Nonlinear Multiview Analysis: Identifiability and Neural Network-based Implementation

Nonlinear Multiview Analysis: Identifiability and Neural Net...

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sensor array and multichannel signal processing workshop

作者： Qi Lyu Xiao Fu School of EECS Oregon State University Corvallis OR USA

ISBN: (数字)9781728119465

ISBN: (纸本)9781728119472

Multiview analysis aims to extract common information from data entities across different domains (e.g., acoustic, visual, text). Canonical correlation analysis (CCA) is one of the classic tools for this problem, which estimates the shared latent information via linear transforming the different views of data. CCA has also been generalized to the nonlinear regime, where kernel methods and neural networks are introduced to replace the linear transforms. While the theoretical aspects of linear CCA are relatively well understood, nonlinear multiview analysis is still largely intuition-driven. In this work, our interest lies in the identifiability of shared latent information under a nonlinear multiview analysis framework. We propose a model identification criterion for learning latent information from multiview data, under a reasonable data generating model. We show that minimizing this criterion leads to identification of the latent shared information up to certain indeterminacy. We also propose a neural network based implementation and an efficient algorithm to realize the criterion. Our analysis is backed by experiments on both synthetic and real data.

关键词： Science - general Visualization Neural networks signal processing algorithms US Government Transforms Tools

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Suppression of Ghost Targets in Focusing Azimuth Periodically Gapped SAR Raw Data with Complex Iterative Thresholding Algorithm

Suppression of Ghost Targets in Focusing Azimuth Periodicall...

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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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