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Dimension-Reduction Maximum Likelihood sensor array calibration Using Inaccurate Cooperative Sources

IEEE sensorS JOURNAL 2024年第6期24卷 8526-8538页

作者： Song, Shuoshuo Ma, Xiaofeng Sheng, Weixing Nanjing Univ Sci & Technol Dept Commun Engn Nanjing 210094 Peoples R China

The state-of-the-art auxiliary calibration algorithms can perform comprehensive calibration of various array nonideal characteristics, such as mutual coupling, gain/phase uncertainties, and sensor position errors, employing a set of cooperative calibration sources with known direction of arrival (DOA). However, the task of deploying calibration sources at precisely measured DOAs is complex. Otherwise, the calibration source DOA errors would seriously degrade the performance of these algorithms. In this article, a dimension-reduction maximum likelihood calibration algorithm (MLCA) with inaccurate cooperative sources is proposed to overcome this issue. First, a maximum likelihood (ML) calibration model is established including both the unknown array of nonideal parameters and 2-D DOAs of all calibration sources. Next, the ambiguity of sensor position estimation caused by inaccurate 2-D DOAs of calibration sources is analyzed. Furthermore, a dimension-reduction ML calibration model is proposed to resolve the ambiguity under the zero mean Gaussian distribution assumption of the calibration source elevation errors. Then, since the proposed multiparameter dimension-reduction model is nonconvex and multimodal, a new filled function method (FFM) is proposed to cope with its local extrema attractors. The proposed single-parameter filled function (SPFF) has a single form without an exponential term and is second-order continuously differentiable, which is stable for numerical calculations and easy to optimize by local optimization tools. Finally, the closed-form hybrid Cramer-Rao lower-bound (CRB) expressions of array parameters under unknown source DOAs are derived in detail. Numerical results verify the effectiveness of the proposed algorithm.

关键词： Dimension-reduction global optimization inaccurate calibration sources maximum likelihood (ML) estimation sensor array calibration

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Necessary and Sufficient Conditions for Observability of SLAM-Based TDOA sensor array calibration and Source Localization

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IEEE TRANSACTIONS ON ROBOTICS 2021年第5期37卷 1451-1468页

作者： Su, Daobilige Kong, He Sukkarieh, Salah Huang, Shoudong China Agr Univ Coll Engn Beijing 100083 Peoples R China Univ Sydney USYD Sydney Inst Robot & Intelligent Syst Camperdown NSW 2006 Australia Univ Technol Sydney UTS Ctr Autonomous Syst Sydney NSW 2007 Australia

sensor array-based systems, which adopt time difference of arrival (TDOA) measurements among the sensors, have found many robotic applications. However, for existing frameworks and systems to be useful, the sensor array needs to be calibrated accurately. Of particular interest in this article are microphone array-based robot audition systems. In our recent work, by using a moving sound source, and the graph-based formulation of simultaneous localization and mapping (SLAM), we have proposed a framework for joint sound source localization and calibration of microphone array geometrical information, together with the estimation of microphone time offset and clock difference/drift rates. However, a thorough study on the identifiability question, termed observability analysis here, in the SLAM framework for microphone array calibration and sound source localization, is still lacking in the literature. In this article, we will fill the abovementioned gap via a Fisher information matrix approach. Motivated by the equivalence between the full column rankness of the Fisher information matrix and the Jacobian matrix, we leverage the structure of the latter associated with the SLAM formulation, and present necessary and sufficient conditions guaranteeing its full column rankness, which lead to parameter identifiability. We have thoroughly discussed the 3-D case with asynchronous (with both time offset and clock drifts, or with only one of them) and synchronous microphone array, respectively. These conditions are closely related to the motion varieties of the sound source and the microphone array configuration, and have intuitive and physical interpretations. Based on the established conditions, we have also discovered some particular cases where observability is impossible. Connections with calibration of other sensors will also be discussed, amongst others. To our best knowledge, this is the first systematic work on observability analysis of SLAM-based microphone array calibr

关键词： Microphone arrays calibration Simultaneous localization and mapping Robot sensing systems Location awareness Observability sensor arrays Fisher information matrix (FIM) microphone array robot audition sensor array calibration simultaneous localization and mapping (SLAM) source localization time difference of arrival (TDOA)

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Maximum Likelihood sensor array calibration Using Non-Approximate Hession Matrix

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 688-692页

作者： Song, Shuoshuo Ma, Xiaofeng Sheng, Weixing Zhang, Renli Nanjing Univ Sci & Technol Dept Commun Nanjing 210094 Peoples R China

Unknown array non-ideal characteristics, including mutual coupling between array elements, channel gain/phase errors, and sensor position errors, will degrade the array performance of direction finding and beamforming. In this letter, we propose an improved maximum likelihood (ML) calibration algorithm to estimate all such non-ideal characteristics with lower computational complexity and faster convergence rate. First a modified formulation of the ML function is developed to avoid matrix inversion in each iteration. Then, a damped Newton formula with non-approximate Hession matrix is derived, which greatly improves the convergence rate. The numerical results demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： Maximum likelihood estimation calibration sensor arrays Computational complexity Convergence Signal processing algorithms Transmission line matrix methods Channel gain phase errors maximum likelihood estimation sensor array calibration sensor position errors mutual coupling

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Automatic sensor array calibration and Coregistration for On-Scalp MEG

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Pang, Maotong Huang, Ziyuan Ding, Zhongya Han, Bangcheng Beihang Univ Sch Instrumentat & Optoelect Engn Key Lab Ultraweak Magnet Field Measurement Technol Minist Educ Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Zhejiang Prov Key Lab Ultraweak Magnet Field Space Hangzhou 310051 Peoples R China Beihang Univ Res Inst Frontier Sci Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China

Accurate magnetoencephalography (MEG) localization of neural electrical activity requires registration between the MEG sensors and MRI. To reduce the geometric error of the sensor array and realize automatic registration, an array geometry calibration algorithm based on prior knowledge is proposed, which automatically completes the position and orientation calibration of optical pumped atomic magnetometer (OPM)-MEG array with the reference of the design model of MEG helmet. Full-process automatic coregistration based on optical scanning is realized. A position error of 0.23 +/- 0.12 mm and orientation error of 0.34 +/- 0.14. are obtained in the calibration results of 85 sensors on-scalp MEG system. The proposed array geometric calibration (AGC) algorithm is used to quantify the deviation between the manufactured array and the theoretical array for the first time. The results show a mean position deviation of 0.57 mm and an angle deviation of 0.68. in the manufactured array, with the maximum position deviation of 1.45 mm and the maximum orientation deviation of 2.13.. It is indicated that the replacement of the manufactured array by the theoretical array in previous research may introduce significant errors in the MEG source localization results.

关键词： ~Coregistration model segmentation on-scalp magnetoencephalography (MEG) sensor array calibration sen-sor localization

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A Statistical Sparsity-based Method for sensor array calibration 4

A Statistical Sparsity-based Method for Sensor Array Calibra...

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4th IEEE World Forum on Internet of Things (WF-IoT)

作者： Zhao, Lifan Goh, Shen Tat Ng, Wee Siong Agcy Sci Technol & Res Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781467399449

In Internet-of-Things applications, direction-of-arrival (DOA) techniques play an important role in identifying the direction of the sources. However, the antenna array system is difficult to be precisely calibrated in practical scenarios. In this paper, the DOA estimation problem is considered for the antenna array with unknown errors. In particular, the sparsity in spatial domain is exploited, where the errors are practically assumed to be unknown without any prior information. The proposed algorithm can calibrate the unknown errors in the sensor array and estimate the DOA of the sources simultaneously. Notably, the proposed algorithm is based on a fast sparse Bayesian framework, where the whole process can be carried out with high efficiency. By exploiting sparsity in a statistical manner, the number of sources is not required to be known a priori. The simulated results can validate that the proposed method is capable of obtaining accurate estimation with low computational complexity.

关键词： Internet-of-Thing direction-of-arrival sensor array calibration statistical sparsity

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An Enhanced Multi-sensor Detection Scheme With Drift calibration and array Optimization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第8期71卷 4030-4034页

作者： Qian, Junhui Xu, Peng Lu, Yuanyuan Liu, Ziyu Wang, Shiyuan Chongqing Univ Sch Microelect & Commun Engn Chongqing 400030 Peoples R China Jianghuai Adv Technol Ctr Hefei 230000 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

In this brief, a multi-metal oxide sensor (MOS) detection system aims at identifying herbal medicines is proposed. To improve the long-term stability of the system and reduce the number of sensors, we derive a combined scheme with drift calibration and array optimization design. Specifically, a subspace projection matrix considering multiple constraints is first optimized to compensate the sensor drift. Subsequence, a sensor array optimization via an embedded joint mutual information framework is developed to remove redundant information in the target domain. Finally, the experimental results show that compared with the existing approaches, the proposed calibration framework can obtain the best system performance with the minimum number of sensors.

关键词： sensor arrays Feature extraction Optimization calibration Time series analysis System performance Mutual information Detection system sensor array calibration sensor array optimization

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Efficient Multifeature Extraction and Embedded array Optimization for Drift-Calibrated Multisensor Odor Detection System

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Qian, Junhui Xu, Peng Sun, Zhuoran Liang, Yezong Qian, Guobing Wang, Shiyuan Chongqing Univ Sch Microelect & Commun Engn Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China Jianghuai Adv Technol Ctr Hefei 230000 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China

This article focuses on achieving a reliable identification of herbal medicine while minimizing the impact of sensor drift. Therefore, 21 metal oxide sensors are implemented as sensitive units during the initial construction of the sensor array. Based on this, to fully exploit odor data and reduce computational burdens, we propose a weighted feature combination selection strategy that evaluates a feature set based on both label relevance and feature stability. To further enhance the long-term stability of the system and reduce the number of sensors, we introduce a combined scheme involving drift calibration and array optimization design. Specifically, we optimize a subspace projection matrix that considers minimizing the maximum mean discrepancy (MMD), preserving the Laplace manifold for the source domain, and maximizing the correlation coefficient between the source and target domains to compensate for sensor drift. Subsequently, we develop sensor array optimization using an embedded composition of feature relevancy (CFR) framework to eliminate redundant information in the target domain. Experimental results demonstrate that, compared to existing approaches, the proposed calibration framework achieves the best system performance with the minimum number of sensors.

关键词： Detection system feature combination selection sensor array calibration sensor array optimization

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Co-Registration Error Analysis and array calibration for OPM-MEG System

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： Pang, Maotong Huang, Ziyuan Ding, Zhongya Han, Bangcheng Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Beihang Univ Res Inst Frontier Sci Beijing 100191 Peoples R China

Optically pumped magnetometer (OPM-magneto- encephalography (MEG) source localization necessitates precise co-registration of the MRI and MEG sensor array. The co-registration error decreases the spatial resolution of MEG source estimation. To clarify and reduce the co-registration error, the data acquisition and co-registration processes are theoretically modeled and analyzed in this article. The results show that the error caused by the substituting of the designed sensor array model for the manufactured array in data acquisition is widely ignored. The amount of data used for transformation calculation is a decisive factor for the transformation accuracy. The error sources of co-registration in the OPM-MEG system are analyzed. The position error and orientation error of the sensitive axis of the sensor are two main error sources. In the range of 250 mm, the calibration experiment achieves a position accuracy of 2.5 mm and an orientation accuracy of 2 degrees of the sensor sensitive axis. Real-time acquisition and correction of sensor position and orientation in the OPM-MEG array can be realized by optimizing the configuration of calibration scheme, and the accuracy of the OPM-MEG source localization can be improved.

关键词： sensor arrays Safety Head Magnetic resonance imaging Data models Magnetic heads Three-dimensional displays Co-registration error magnetoencephalography (MEG) optically pumped magnetometers (OPMs) OPM-MEG system sensor array calibration

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ACTIVE array sensor LOCALIZATION

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SIGNAL PROCESSING 1995年第3期44卷 309-327页

作者： NG, BC NEHORAI, A Department of Electrical Engineering Stanford University Stanford CA 94305 USA Department of Electrical Engineering Yale University New Haven CT 06520 USA

sensor array processing algorithms for source localization often require precise knowledge of the sensor locations, which is often not available in practice. Errors in the sensor locations are known to degrade severely the performance of these algorithms. This paper proposes iterative damped Gauss-Newton maximum likelihood array sensor localization algorithms using known calibration source signals, Cramer-Rao bound (CRB) expressions for the sensor location parameters are found for this problem. Two calibration source signal models are considered. One assumes that each signal waveform is known up to a scaling constant and phase, while the other requires complete knowledge of the signal waveform. For uncorrelated, completely known calibration signals, a necessary and sufficient condition for the optimal choice of the source bearings that minimizes the sensor position CRB variances is presented. Numerical results illustrate that the maximum likelihood calibration algorithms attain the CRB asymptotically.

关键词： sensor array calibration sensor LOCALIZATION CRAMER-RAO BOUND

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