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A Frequency-Domain I/Q Imbalance Calibration Algorithm for Wideband Direct Conversion Receivers Using Low-Cost Compensator

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IEEE ACCESS 2023年 11卷 48739-48748页

作者： Peng, Xiaoye Yu, Faxin Wang, Zhiyu Liu, Jiarui Wang, Chenge Wang, Jing Zhejiang Univ Inst Astronaut Elect Engn Sch Aeronaut & Astronaut Hangzhou 310007 Peoples R China Hangzhou Core Device Technol Co Ltd Hangzhou 310007 Peoples R China

The amplitude and phase imbalances of the in-phase (I) and quadrature (Q) branches inherent in the direct conversion receiver structure cause the generation of image frequency interference signals. In this paper, a frequency-domain I/Q imbalance calibration algorithm is proposed for wideband direct conversion receivers. The I/Q imbalance model is rebuilt by applying the infinitesimal method and FFT algorithm. The mathematical expressions for the exact computation of the I/Q imbalance parameters are derived based on the frequency-domain statistical properties of the baseband signal. The two phase parameters, frequency-dependent I/Q imbalance (FD-IQI) and frequency-independent I/Q imbalance (FI-IQI), are separated according to the parity properties of the imbalance parameter. To avoid the interference from the transmitter, the receiver impairment estimation is performed using a frequency offset (FO) DC training signal, and a low-cost real-valued compensation (RVC) filter is introduced to correct the impairments of the received signal. The performances of the proposed calibration model are evaluated through simulations and experiments. The simulation results show that the image rejection ratio (IRR) is improved to 80-120 dBc and can also exceed 40 dBc at high noise levels. The experimental results based on the CX9261A evaluation board show that the average IRR of the multi-tone signal is increased by 24.99 dB, and the IRR of the wideband signal is increased by 19.08 dB.

关键词： Receivers Baseband Calibration Wideband Training filtering algorithms Mathematical models Frequency domain analysis Direct conversion receiver frequency-dependent I Q imbalance frequency-independent I frequency-domain statistical properties infinitesimal method real-valued compensation filter

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Improving Visual Object Tracking Using General UFIR and Kalman Filters Under Disturbances in Bounding Boxes

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IEEE ACCESS 2023年 11卷 57905-57915页

作者： Pale-Ramon, Eli G. Morales-Mendoza, Luis J. Gonzalez-Lee, Mario Ibarra-Manzano, Oscar G. Ortega-Contreras, Jorge A. Shmaliy, Yuriy S. Univ Guanajuato Dept Elect Engn Salamanca 36885 Guanajuato Mexico Univ Veracruzana Elect & Telecommun Engn Fac Poza Rica 93380 Veracruz Mexico

A well-known problem of visual object tracking is the difficulty of accurately estimating the object trajectory under conditions of environmental disturbances in the bounding box (BB) of a video camera. In this paper, we consider BB variations as Gaussian-Markov colored measurement noise (CMN). In order to perform accurate tracking in the presence of CMN, we use measurement differencing and develop a robust general unbiased finite impulse response (GUFIR) filter and use the general Kalman filter (GKF) as a benchmark. The GUFIR and GKF algorithms are tested by the "Car4"benchmark. It is shown that, in terms of the tracking precision and under the heavy disturbance with the 0.65 = ? = 0.95 coloredness factor, the best tracking performance is achieved using the robust GUFIR filter. When ? < 0.6, the GUFIR and GKF algorithms perform near equally. In the extreme point of ? = 1.0, where the Gauss-Markov CMN loses the stationarity, both algorithms provide zero precision and become inefficient. In general, it is concluded that the GUFIR filter, which ignores any zero mean disturbance and initial values, is much more suitable for applications in visual object tracking than Kalman-like algorithms relying on complete object information.

关键词： Visualization Object tracking filtering algorithms Cameras Trajectory Finite impulse response filters Target tracking Visual object tracking bounding box environmental disturbance colored measurement noise general UFIR filter general Kalman filter precision

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Comparison Between UKF and EKF in Sensorless Synchronous Reluctance Motor Drives

IEEE OPEN JOURNAL OF POWER ELECTRONICS

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IEEE OPEN JOURNAL OF POWER ELECTRONICS 2024年 5卷 1562-1572页

作者： Rigon, Saverio Haus, Benedikt Mercorelli, Paolo Zigliotto, Mauro Univ Padua Dept Management & Engn I-36100 Vicenza Italy Leuphana Univ Luneburg Inst Prod Technol & Syst IPTS D-21335 Luneburg Germany

The reduction of the human environmental footprint can be achieved through the use of more efficient motors, such as synchronous reluctance motors (SynRMs). Known for their affordability, SynRMs are increasingly employed in sensorless AC drives. This paper presents a critical comparison of sensorless algorithms based on two nonlinear Kalman Filters (Unscented and Extended). The objective is to highlight the theoretical and practical advantages and drawbacks of each method when applied to the speed control of a SynRM, culminating in a definitive decision on the best choice. The magnetic model of the SynRM is much more nonlinear than that of induction motors, which are predominantly addressed in existing literature. This study aims to fill the gap by answering the question: "Is the extended Kalman filter still the best choice even in the case of nonlinear electric motors?" The answer comes through a large batch of experiments, including speed and load torque tests, zero-speed standstill starts, parameter sensitivity, and evaluation of computational burdens for both Kalman filter algorithms.

关键词： Mathematical models Magnetic separation Kalman filters Motors Magnetic flux Couplings Reluctance motors filtering algorithms Computational modeling Vectors Kalman filter extended kalman filter sensorless unscented kalman filter synchronous reluctance motor (SynRM) AC machines

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TWOR: Improving Modeling and Self-Localization in RFID-Tag Networks Under Colored Noise

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IEEE ACCESS 2023年 11卷 25583-25592页

作者： Ortega-Contreras, Jorge A. A. Andrade-Lucio, Jose A. A. Ibarra-Manzano, Oscar G. G. Shmaliy, Yuriy S. S. Univ Guanajuato Dept Elect Engn Salamanca 36855 Guanajuato Mexico

This paper discusses the three-wheeled omnidirectional robot (TWOR) self-localization in radio frequency identification (RFID) tag environments. The nonlinear TWOR model is significantly improved by using geometric interpretation and incremental time representation in discrete time. The TWOR position and heading are self-estimated using distance measurements to RFID tags and a digital gyroscope in the presence of typical colored measurement noise (CMN). The extended unbiased finite impulse response (EFIR) is developed along with the extended Kalman filter (EKF) and their versions, cEKF and cEFIR, modified for Gauss-Markov CMN. A particle filter is used as a benchmark. It is shown that the cEFIR filter is more robust than the cEKF and almost as robust as the particle filter, although the latter is less accurate in real time.

关键词： Kalman filters Robot kinematics filtering algorithms Mathematical models RFID tags Kinematics Autoregressive processes Finite impulse response filters TWOR modeling self-localization colored noise extended unbiased FIR filter extended Kalman filter

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A Distributed Implementation of Steady-State Kalman Filter

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第4期68卷 2490-2497页

作者： Yan, Jiaqi Yang, Xu Mo, Yilin You, Keyou Tsinghua Univ Dept Automat BNRist Beijing 100084 Peoples R China

This article studies the distributed state estimation in sensor network, where m sensors are deployed to infer the n-dimensional state of a linear time-invariant Gaussian system. By a lossless decomposition of the optimal steady-state Kalman filter, we show that the problem of distributed estimation can be reformulated as that of the synchronization of homogeneous linear systems. Based on such decomposition, a distributed estimator is proposed, where each sensor node runs a local filter using only its own measurement, alongside with a consensus algorithm to fuse the local estimate of every node. We prove that the average of local estimates from all sensors coincides with the optimal Kalman estimate, and under certain condition on the graph Laplacian matrix and the system matrix, the covariance of local estimation error is bounded and the asymptotic error covariance is derived. As a result, the distributed estimator is stable for each single node. We further show that the proposed algorithm has a low message complexity of min(m, n). Numerical examples are provided in the end to illustrate the efficiency of the proposed algorithm.

关键词： Kalman filters Linear systems Estimation Synchronization Steady-state Robot sensing systems filtering algorithms Consensus algorithm distributed estimation Kalman filter linear system synchronization

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Stability Guaranteed Active Noise Control: algorithms and Applications

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2023年第4期31卷 1720-1732页

作者： Tang, Yu Zhang, Hongwei Zhang, Yuting Southwest Jiaotong Univ Sch Elect Engn Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 611756 Peoples R China Harbin Inst Technol Sch Mech Engn & Automation Shenzhen 518055 Peoples R China

Stability of the controller, robust stability of the active noise control (ANC) system, and limited output power of the hardware control board or actuators are crucial issues for ANC applications. Violation of these requirements results in output saturation, and hence nonlinear distortion by overloading the actuators. As a consequence, noise reduction performance of an ANC system will be dramatically degraded. A novel frequency-weighted leaky filtered-x least-mean-square (FxLMS) algorithm is developed in this article to fulfill these requirements. The proposed algorithm is an improvement of the traditional leaky FxLMS algorithm, where a frequency-weighted matrix is designed to replace the corresponding scalar weight. The frequency-weighted matrix, which is achieved by solving a constrained optimization problem, consists of $\it N$ frequency-weighted factors. By the frequency-weighted factors, the frequency response of the control filter is only suppressed at specific frequencies where the requirements on stability and/or limited output power are violated. Thus, the proposed control algorithm achieves better noise reduction performance than the traditional leaky FxLMS algorithm, which unnecessarily suppresses the control filter at all the frequencies. Simulations and experiments are carried out in a duct ANC system to verify the effectiveness of the proposed algorithm for both the feedforward and feedback ANC systems.

关键词： Feedforward systems Frequency control Noise reduction Acoustics Actuators filtering algorithms Control systems Active noise control (ANC) constrained optimization leaky FxLMS algorithm output saturation stability

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Super-Resolution With Binary Priors: Theory and algorithms

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 3213-3228页

作者： Sarangi, Pulak Hattori, Ryoma Komiyama, Takaki Pal, Piya Univ Calif San Diego Elect & Comp Engn La Jolla CA 92092 USA Univ Calif San Diego Ctr Neural Circuits & Behav Neurobiol Sect Dept Neurosci La Jolla CA 92092 USA Univ Calif Halicioglu Data Sci Inst San Diego CA 92092 USA

The problem of super-resolution is concerned with the reconstruction of temporally/spatially localized events (or spikes) from samples of their convolution with a low-pass filter. Distinct from prior works which exploit sparsity in appropriate domains in order to solve the resulting ill-posed problem, this paper explores the role of binary priors in super-resolution, where the spike (or source) amplitudes are assumed to be binary-valued. Our study is inspired by the problem of neural spike deconvolution, but also applies to other applications such as symbol detection in hybrid millimeter wave communication systems. This paper makes several theoretical and algorithmic contributions to enable binary super-resolution with very few measurements. Our results show that binary constraints offer much stronger identifiability guarantees than sparsity, allowing us to operate in "extreme compression" regimes, where the number of measurements can be significantly smaller than the sparsity level of the spikes. To ensure exact recovery in this "extreme compression" regime, it becomes necessary to design algorithms that exactly enforce binary constraints without relaxation. In order to overcome the ensuing computational challenges, we consider a first order auto-regressive filter (which appears in neural spike deconvolution), and exploit its special structure. This results in a novel formulation of the super-resolution binary spike recovery in terms of binary search in one dimension. We perform numerical experiments that validate our theory and also show the benefits of binary constraints in neural spike deconvolution from real calcium imaging datasets.

关键词： Superresolution Deconvolution filtering algorithms Calcium Low-pass filters IIR filters Signal processing algorithms Beta-expansions binary compressed sensing sparsity binary search spike deconvolution super-resolution

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A New Virtual Tracking Sub-Algorithm Based Hybrid Active Control System for Narrowband Noise With Impulsive Interference

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2023年 31卷 915-926页

作者： Zhu, Wenzhao Luo, Lei Sun, Jinwei Christensen, Mads Graesboll Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Chongqing Univ Key Lab Optoelect Technol & Syst Educ Minist China Chongqing 400044 Peoples R China Harbin Inst Technol Sch Instrumentat Sci & Engn Harbin 150001 Peoples R China Aalborg Univ Dept Architecture Design & Media Technol Audio Anal Lab DK-9000 Aalborg Denmark

Mechanical noise is usually a mixture of narrowband and impulsive noise which needs complex active noise control (ANC) algorithms to improve the de-noising performance. But the ANC algorithm with a high computation load will reduce the real-time performance of an ANC system, thus decreasing the attenuation performance and even leading to divergence. To alleviate this contradiction in narrowband ANC systems, a new virtual filtered-x L0 norm discrete Fourier cancellation (FxL0DFC) based hybrid FxNLMS(filtered-x normalized least mean square)-FxDFC framework is proposed to decrease the total computing load and keep good attenuation performance. For a fast-changing noise, the FxNLMS algorithm is employed. The new virtual FxL0DFC algorithm serves to prepare parameters for steady-state, and when this happens, the FxDFC algorithm with the parameters provided by FxL0DFC is applied. Compared to using the FxNLMS algorithm to attenuate narrowband periodical noise, the FxDFC algorithm has nearly the same tracking performance while having a low computational load. As a result, the FxL0DFC-based FxNLMS-FxDFC algorithm leads to a reduction of the total computational load. Moreover, the proposed method performs excellently in terms of tracking in simulations and experiments on actual data, particularly in environments with rapid power changes and impulsive noise.

关键词： Adaptive filters Narrowband Attenuation Switches filtering algorithms Band-pass filters Interference Active noise control narrowband noise with impulsive interference virtual FxL0DFC hybrid FxNLMS-FxDFC

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Overload-Checking and Edge-Finding for Robust Cumulative Scheduling

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INFORMS JOURNAL ON COMPUTING 2023年第6期35卷 1419-1438页

作者： Fahimi, Hamed Quimper, Claude-Guy Shahid Chamran Univ Ahvaz Fac Math Sci & Comp Dept Comp Sci Ahvaz *** Iran Univ Laval Fac Sci & Genie Dept Informat & Genie Logiciel Quebec City PQ G1V 0A6 Canada

Scheduling frameworks are not necessarily stable. The aim is to introduce schedules resistant to disruptions such as when resources become unavailable, the supply chain for them breaks down, etc. A schedule is robust if it absorbs some level of unforeseen events when at most a certain number of activities are delayed. Taking advantage of constraint programming, we present two new filtering algorithms for a constraint that models cumulative scheduling problems in robust contexts where up to r out of n tasks can be concurrently delayed while keeping the schedule valid. We adapt the overload-checking and edge-finding filtering rules for this framework. We show that our robust versions of these algorithms run in & UTheta;(r2nlog(n)) and O(r2znlog(n)), respectively, where z denotes the number of distinct capacities of all tasks. This achievement implies that the complexities of the state-of-the-art algorithms for these techniques are invariable when r is constant. Experiments illustrate that our algorithms scale, with respect to n and r. As a practical application, the experimental results on a special case of crane assignment problem also verify a stronger filtering for these methods in terms of backtrack numbers as well as computation times when used in conjunction with time tabling. Finally, in order to show that our CP-based algorithms improve to solve a robust scheduling problem, we make a comparison against temporal protection as an external robust scheduling approach.

关键词： constraint programming constraint propagation filtering algorithms robust cumulative scheduling uncertainty data structure

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Robust Adaptive Heading Tracking Fusion for Polarization Compass With Uncertain Dynamics and External Disturbances

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

作者： Zhao, Huijun Niu, Xiaoming Cao, Huiliang Shen, Chong Wang, Chenguang Li, Jie Tang, Jun Liu, Jun North Univ China Sch Instrument & Elect State Key Lab Dynam Measurement Technol Taiyuan 030051 Peoples R China Chongqing Univ Coll Math & Stat Chongqing 400044 Peoples R China China South Ind Grp Automat Res Inst Co Ltd Mianyang 621000 Peoples R China

For the polarization compass (PC), maintaining high-precision and high-robustness autonomous measurement of the heading information under conditions where the atmospheric polarized light is being blocked or disturbed represents a challenging problem. In this article, the $E$ -vector orientation algorithm, which provides high orientation accuracy and text real-time performance in sunny weather, and the symmetric axis orientation algorithm, which offers a strong anti-interference ability in occluded environments, are integrated to solve this problem. Furthermore, an improved state error model is established to increase the accuracy of the heading estimation. To track the system state rapidly and circumvent the uncertainties caused by measurement noise variance, an improved variational Bayesian strong tracking cubature Kalman filter (VBSTCKF) is proposed to estimate both the state and the time-varying measurement noise variance accurately. In addition, a multifrequency data fusion algorithm based on residual compensation is proposed to overcome the inconsistent sampling operation frequency problems of the two orientation algorithms. The outstanding feature is that the proposed multifrequency VBSTCKF (MF-VBSTCKF) can improve the orientation precision and robustness while also ensuring the high-frequency output performance. Finally, experimental results acquired in occluded environments verify the superiority of the proposed method.

关键词： filtering algorithms Real-time systems Kalman filters Compass Robustness Noise measurement Estimation Autonomous orientation cubature Kalman filter (CKF) heading fusion polarization compass (PC)

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