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Single-Source DOA Estimation for Wideband LFM signal: Time-Delay Mixing and Enhanced Self-Mixing MUSIC Methods

CIRCUITS SYSTEMS AND signal processing 2025年第1期44卷 218-238页

作者： Zhang, Wentao Miao, Chen Jiang, Mengjie Wu, Wen Southeast Univ State Key Lab Millimetre Waves Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Key Lab Near Range RF Sensing ICs & Microsyst Nanjing 210094 Peoples R China

Accurately estimating the direction of arrival (DOA) of wideband signals with a sensor array is critical in communications, radar, and the Internet of Things. This paper proposes two single-source DOA estimation methods for wideband linear frequency modulation signals: time-delay mixing multiple signal classification (TDM-MUSIC) and enhanced self-mixing MUSIC (ESM-MUSIC). TDM-MUSIC employs time-delay mixing of the received signal to construct an equivalent single-frequency signal model, thereby enhancing estimation accuracy while maintaining reasonable computational efficiency. ESM-MUSIC improves the conventional self-mixing model by adding frequency correction steps, resulting in excellent DOA estimation performance at the expense of computational complexity. Unlike conventional methods that rely on approximate models, our methods establish more accurate equivalent models. A key advantage of our methods is that they allow flexible adjustment of the optimal sensor inter-element spacing in arrays based on the equivalent signal model rather than the actual signal model, simplifying engineering fabrication and reducing mutual coupling between sensors. The paper establishes the Cram & eacute;r-Rao bounds for both proposed methods and demonstrates their superiority over existing methods through comprehensive numerical simulations. Further, the experiment using a TI-AWR2243 multi-sensor array radar system confirms that our methods are feasible for practical engineering applications.

关键词： sensor inter-element array spacing sensor array signal processing Wideband signal processing Direction of arrival (DOA) estimation Cram & eacute r-Rao lower bound (CRLB)

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An Improved Constrained LMS Algorithm for Fast Adaptive Beamforming Based on a Low Rank Approximation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第8期69卷 3605-3609页

作者： Vadhvana, Sanket Yadav, Shekhar Kumar Bhattacharjee, Sankha Subhra George, Nithin V. Indian Inst Technol Gandhinagar Dept Elect Engn Gandhinagar 382355 India

Adaptive beamformers use data from sensor arrays to capture signal from a desired direction without any distortion, in the presence of interfering signals from other directions in a noisy environment. Most beamformers achieve this goal by minimizing their variance while applying distortionless and null constraints in the direction of the desired and interfering signals, respectively. Constrained least-mean-square (CLMS) algorithms have been developed to iteratively update the weights of such beamformers. In this brief, we propose a novel and improved CLMS beamforming algorithm based on a low rank approximation technique called the nearest Kronecker product decomposition. By decomposing the weight vector into a sequence of Kronecker products of smaller vectors, the original weight update process is converted into updates of smaller vectors. The decomposition allows us to control the trade-off between steady-state performance and faster convergence based on the rank of the beamforming system. We derive the update rules of the proposed algorithm, tabulate its computational complexity and perform simulation study to show its superiority.

关键词： array signal processing signal processing algorithms Steady-state Circuits and systems sensor arrays Convergence Optimization Adaptive beamforming nearest Kronecker product constrained LMS sensor array signal processing

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Underdetermined Wideband DOA Estimation Utilizing Noncircular Complex Gaussian Distribution

IEEE SENSORS LETTERS

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IEEE sensorS LETTERS 2020年第5期4卷 1页

作者： Hu, Nan Wang, Tianyun Wei, Qiang Hu, Jianling Soochow Univ Sch Elect & Informat Engn Suzhou 215006 Peoples R China China Satellite Maritime Tracking & Controlling D Jiangyin 214431 Peoples R China

With the emerging of sparsely spaced sensor arrays, the study on underdetermined direction-of-arrival (DOA) estimation methods has drawn much attention. In most of the existing methods, the vectorized sample covariance matrix was considered as circularly symmetric Gaussian by default. However, the sample covariance vector is in fact noncircular, and its pseudo covariance matrix has not yet been utilized for underdetermined DOA estimation. On account of the wide usage of wideband signals nowadays, in this letter, the underdetermined DOA estimation problem for wideband signals is addressed, where the noncircularity of the sample covariance vectors is exploited. Moreover, a hierarchical Bayesian model is established, modeling the noncircular sample covariance vectors, the nonnegative group-sparse signal variance vectors, the auto-estimated regularization parameter and the off-grid difference, which were not considered simultaneously in the existing methods. A sparse Bayesian learning algorithm is derived via expectation-maximization, and numerical simulations show that the proposed method outperforms the methods that do not consider the noncircularity of sample covariance vectors.

关键词： sensor signal processing noncircular complex Gaussian distribution sensor array signal processing sparse Bayesian learning (SBL) underdetermined wideband direction-of-arrival (DOA) estimation

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Logarithmic Cost Based Constrained Adaptive Filtering Algorithms for sensor array Beamforming

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IEEE sensorS JOURNAL 2018年第14期18卷 5897-5905页

作者： Gogineni, Vinay Chakravarthi Mula, Subrahmanyam Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

This paper introduces a novel family of constrained adaptive filtering algorithms for sensor array beamforming. These algorithms, namely, constrained least mean logarithmic square (CLMLS), l(1)-norm CLMLS (l(1)-CLMLS) and its weighted version l(1)-WCLMLS, are developed based on a relative logarithmic cost function. The proposed algorithms gracefully adjust cost function depending on the amount of the error thereby achieving better performance compared to constrained least mean square (CLMS) family of algorithms. The transient and steady-state performance analysis of the proposed CLMLS algorithm is presented and these analytical results are validated through extensive simulations. Proposed CLMLS algorithm is then extended to sparse system identification problem by incorporating the l(1)-norm penalty into CLMLS cost function. We show that the resultant l(1)-CLMLS and l(1)-WCLMLS algorithms outperform their CLMS counterparts in sparse system identification. When applied to sparse sensor array synthesis, these algorithms achieve desired beampattern with lesser number of sensor elements compared to state-of-the-art algorithms.

关键词： sensor array signal processing constrained adaptive filters adaptive beamforming interference cancelation thinned sensor arrays

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ESPRIT Angles-of-Arrival Estimation with Missing sensor Data 27

ESPRIT Angles-of-Arrival Estimation with Missing Sensor Data

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27th European signal processing Conference (EUSIPCO)

作者： White, Langford B. Jackson, Trevor Univ Adelaide Sch Elect & Elect Engn Adelaide SA Australia Def Sci & Technol Grp Maritime Div Canberra ACT Australia

ISBN: (纸本)9789082797039

This paper describes a procedure for Angles-of-Arrival (AoA) estimation for a uniform linear array (ULA) with missing sensors. The novelty of the approach is that, rather than using AoA estimates obtained from contiguous subarrays, we use estimates for the corresponding signal subspaces. The report shows that the ESPRIT invariance equations for each contiguous subarray define an operator that "propagates" the signal sub-space beyond the physical array. Care needs to be taken to ensure the same bases are used for each subarray. The estimates of the signal subspaces for the missing array elements are appropriately combined to yield an estimate of the signal subspace for the complete ULA. The paper only addressed the case of one missing sensor, but the approach can be readily generalised. Simulations show that the proposed method yields AoA estimates which are very close to those obtained if there was no missing sensor, in contrast to the case where the measurements from the missing sensor were zero. In order to appropriately combine the estimates for the missing signal subspace terms, the report assessed the accuracy of signal subspace estimation as a function of the number N of ULA elements. Simulations indicate that, in the examples considered, the variance of these estimates decreases only as N-1/3 which is surprising given that the variance of AoA estimates (at least for one source) decrease as N-3. The paper suggests that further study of this empirical result is warranted.

关键词： sensor array signal processing subspace methods

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Efficient DOA Estimation Algorithm for Noncircular Sources Under Unknown Mutual Coupling

IEEE SENSORS LETTERS

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IEEE sensorS LETTERS 2018年第3期2卷 1页

作者： Xie, Jian Wang, Ling Su, Jia Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 518057 Peoples R China Shaanxi Lingyun Elect Grp Co Ltd Baoji 721006 Peoples R China

Mutual coupling effects may seriously degrade the direction-of-arrival (DOA) estimation performance in practical sensor arrays. In this article, a computationally efficient DOA estimation algorithm is proposed for noncircular sources in the presence of mutual coupling. Utilizing the noncircularity of the sources, we first construct an extended covariance matrix. Then, based on the banded symmetric Toeplitz structure of the mutual coupling matrix, the extended covariance matrix is divided into two submatrices. By simultaneous singular-value decomposition (SVD) of the two submatrices, the DOA parameter is obtained in a closed form. The proposed algorithm can improve the DOA estimation performance. Moreover, it is computationally efficient since it does not require any spectral search and avoids high-order statistics. Simulation results demonstrate both the efficiency and the effectiveness of our proposed algorithm under unknown mutual coupling.

关键词： sensors signal processing direction-of-arrival (DOA) estimation mutual coupling noncircular sources sensor array signal processing

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A Noise Removal Method for Uniform Circular arrays in Complex Underwater Noise Environments with Low SNR

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sensorS 2017年第6期17卷 1345-1345页

作者： Xia, Huijun Yang, Kunde Ma, Yuanliang Wang, Yong Liu, Yaxiong Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Minist Ind & Informat Technol Key Lab Ocean Acoust & Sensing Xian 710072 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710072 Peoples R China

Generally, many beamforming methods are derived under the assumption of white noise. In practice, the actual underwater ambient noise is complex. As a result, the noise removal capacity of the beamforming method may be deteriorated considerably. Furthermore, in underwater environment with extremely low signal-to-noise ratio (SNR), the performances of the beamforming method may be deteriorated. To tackle these problems, a noise removal method for uniform circular array (UCA) is proposed to remove the received noise and improve the SNR in complex noise environments with low SNR. First, the symmetrical noise sources are defined and the spatial correlation of the symmetrical noise sources is calculated. Then, based on the preceding results, the noise covariance matrix is decomposed into symmetrical and asymmetrical components. Analysis indicates that the symmetrical component only affect the real part of the noise covariance matrix. Consequently, the delay-and-sum (DAS) beamforming is performed by using the imaginary part of the covariance matrix to remove the symmetrical component. However, the noise removal method causes two problems. First, the proposed method produces a false target. Second, the proposed method would seriously suppress the signal when it is located in some directions. To solve the first problem, two methods to reconstruct the signal covariance matrix are presented: based on the estimation of signal variance and based on the constrained optimization algorithm. To solve the second problem, we can design the array configuration and select the suitable working frequency. Theoretical analysis and experimental results are included to demonstrate that the proposed methods are particularly effective in complex noise environments with low SNR. The proposed method can be extended to any array.

关键词： underwater ambient noise sensor array signal processing signal-to-noise ratio beamforming noise removal

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Dual-Microphone Noise Reduction in Car Environments With Determinant Analysis of Input Correlation Matrix

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IEEE sensorS JOURNAL 2016年第9期16卷 3131-3140页

作者： Hong, Jungpyo Park, Sangjun Jeong, Sangbae Hahn, Minsoo Korea Adv Inst Sci & Technol Dept Elect Engn Daejeon 34141 South Korea Gyeongsang Natl Univ Engn Res Inst Dept Elect Engn Jinju 52828 South Korea

In this paper, a novel noise reduction technique is proposed to improve the speech interface performance in car environments. The proposed noise reduction method with dual microphones is primarily based on the determinant analysis of the input correlation matrix. Through the analysis, a robust feature for speech activity detection and signal-to-noise ratio (SNR) estimation is derived. Using the feature, the SNR of each time-frequency component is estimated, and an enhanced speech signal is obtained through Wiener filtering. To evaluate the proposed noise reduction technique, we constructed a database in a real car environment and comparatively analyzed the performances of noise reduction methods. The results show that meaningful SNR and perceptual speech quality improvements with less signal distortion are achieved compared with the other competing methods.

关键词： sensor array signal processing determinant analysis dual-microphone noise reduction speech interface car environment

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Multi-Microphone Target signal Enhancement Using Generalized Sidelobe Canceller Controlled by Phase Error Filter

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IEEE sensorS JOURNAL 2016年第21期16卷 7566-7567页

作者： Kim, Seon Man Kim, Hong Kook Gwangju Inst Sci & Technol Sch Elect Engn & Comp Sci Gwangju 61005 South Korea

We propose an improved generalized sidelobe canceller (GSC) utilizing a phase-error filter (PEF) for multi-channel signal enhancement. The PEF is first incorporated for improving the fixed beamformer (BF) of the GSC. Furthermore, the weight update of the GSC is formulated using the PEF spectral gain. The experimental results show that the proposed method provides better perceptual evaluation and intelligibility scores under multiple noise conditions than conventional BFs, including the GSC and PEF.

关键词： sensor array signal processing signal enhancement generalized sidelobe canceller phase-error filter

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Compressed Symmetric Nested arrays and Their Application for Direction-of-Arrival Estimation of Near-Field Sources

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sensorS 2016年第11期16卷 1939-1939页

作者： Li, Shuang Xie, Dongfeng Chongqing Univ Technol Sch Elect & Elect Engn Chongqing 400054 Peoples R China Shanghai Huawei Technol Co Ltd Shanghai 200120 Peoples R China

In this paper, a new sensor array geometry, called a compressed symmetric nested array (CSNA), is designed to increase the degrees of freedom in the near field. As its name suggests, a CSNA is constructed by getting rid of some elements from two identical nested arrays. The closed form expressions are also presented for the sensor locations and the largest degrees of freedom obtainable as a function of the total number of sensors. Furthermore, a novel DOA estimation method is proposed by utilizing the CSNA in the near field. By employing this new array geometry, our method can identify more sources than sensors. Compared with other existing methods, the proposed method achieves higher resolution because of increased array aperture. Simulation results are demonstrated to verify the effectiveness of the proposed method.

关键词： sensor array signal processing direction-of-arrival estimation near-field compressed symmetric nested array fourth-order cumulants

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