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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 3232-3245页

作者： Yu, Yu-Feng Zhang, Yang Chen, Long Ge, Pengfei Chen, C. L. Philip Guangzhou Univ Dept Stat Guangzhou 511370 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Guangdong Polytech Normal Univ Sch Math & Syst Sci Guangzhou 510665 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Currently, most Discriminative Correlation Filters (DCF) algorithms used for Unmanned Aerial Vehicle (UAV) target tracking primarily focus on improving the tracking model. However, in UAV tracking, the tracked targets are typically small in size and frequently undergo scale variations, making singular improvements to tracking models less effective. As a response to this challenge, we propose a novel feature preprocessing approach. Specifically, for the extracted Histogram of Oriented Gradients (HOG) and Color Names (CN) features, we simulate their transformations at different scales and electively enhance the target region features based on global features to obtain multi-scale enhanced features. By implementing these procedures, the tracker improves its ability to recognize targets and exhibits increased adaptability in challenging tracking scenarios. Furthermore, in contrast to the conventional approach used by most UAV algorithms, which unidirectionally incorporate historical filters into model updates to prevent filter divergence, we introduce dual second-order difference terms that correspond to features and filters. This integration enables a more effective fusion of historical information with current frame data, thereby enhancing the robustness of the filtering process. Extensive experiments are conducted to evaluate the proposed tracker against other state-of-the-art trackers using the DTB70, UAV123@10fps, and UAVDT datasets. The experimental results affirm the effectiveness of our approach.

关键词： Target tracking Feature extraction Autonomous aerial vehicles Tracking Correlation Convolutional neural networks filtering algorithms Multi-scale enhanced features dual second-order difference discriminative correlation filters UAV tracking

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An Extensive Cancellation Algorithm via Overlapping Subband for Time-Varying Clutter Suppression in Passive Bistatic Radar

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第4期60卷 5169-5185页

作者： Sui, Jinxin Wang, Jun Gao, Jie Fan, Xiaoyan Xidian Univ Sch Elect Engn Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China Natl Key Lab Space Micowave Technol Xian 710000 Peoples R China

For time-varying clutter suppression in passive bistatic radar (PBR) systems, some existing clutter suppression algorithms, belonging to the time-domain filter (TDF) or the frequency-domain filter (FDF), will suffer from performance degradation in the presence of fractional-order range clutter or fractional-order Doppler clutter, respectively. Additionally, some FDF-based clutter cancellation algorithms with subband operation will produce undesired periodic peaks in the range domain, which is analyzed for the first time in this article. To overcome the above limitations, an advanced extensive cancellation algorithm based on overlapping subband (ECA-OS) is developed. Initially, the clutter subspace with Doppler frequency is constructed in the time domain with the reference signal, where the clutter cancellation order in the range domain is constrained to one-dimension. Subsequently, the echo signal and constructed clutter subspace are transformed to frequency-domain, followed by subband operation. Finally, in conjunction with the least squares criterion, the weight coefficients of time-varying clutter are estimated with an overlapping subband, and the clutter cancellation is implemented in each subband. Attributed to the above operations, both the fractional-order Doppler clutter and fractional-order range clutter are suppressed by the proposed ECA-OS with low computational complexity. In addition, by overlapping subband operation to introduce an additional degree of freedom (DOF), the proposed algorithm can avoid the generation of periodic peaks in the range domain. The effectiveness of proposed ECA-OS is verified by theoretical derivations, simulations, and measured data.

关键词： Clutter Doppler effect Surveillance Wiener filters Object detection Aerospace and electronic systems filtering algorithms Fractional-order clutter passive bistatic radar (PBR) subband operation time-varying clutter

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Automatic Tuning of Extended Kalman Filter in Sensorless Synchronous Reluctance Motor Drives

IEEE OPEN JOURNAL OF POWER ELECTRONICS

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IEEE OPEN JOURNAL OF POWER ELECTRONICS 2025年 6卷 734-746页

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

A substantial reduction in the human environmental footprint can be achieved through the use of more efficient motors, such as synchronous reluctance motors (SynRM). As low-cost motors, SynRMs are commonly employed in sensorless AC drives. Sensorless algorithms based on the Extended Kalman Filter (EKF) offer several advantages, but they require a time-consuming trial-and-error tuning procedure. This paper proposes the automatic tuning of the EKF through a second Kalman Filter (KF) in a primary-secondary (PS) configuration. The two KFs work concurrently: the first estimates the required quantities for machine control, and the second updates the process noise statistics of the first KF. The second KF is much easier to tune, requiring only one non-critical parameter. Experimental results confirm the validity of this approach.

关键词： Tuning Kalman filters Main-secondary Estimation Noise Motors Technological innovation Covariance matrices Mathematical models filtering algorithms Kalman filter adaptive kalman filter sensorless synchronous reluctance motor (SynRM) AC machines primary-secondary Kalman Filter

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MulRF: A Multi-Dimensional Range Filter for Sublinear Time Range Query Processing

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 6600-6613页

作者： Han, Shuai Liu, Xianmin Li, Jianzhong Harbin Engn Univ Sch Comp Sci & Technol Harbin 150009 Peoples R China Harbin Inst Technol Sch Comp Harbin 150001 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing 100045 Peoples R China

Range query is an important operation on big multi-dimensional data. This paper studies the problem of multi-dimensional range query filtering for speeding up the range query processing by avoiding reading the useless data. To solve the problem, a novel multi-dimensional range filter is proposed to filter the multi-dimensional range queries, while the existing one-dimensional range filters can not provide efficient filtering. Based on the multi-dimensional range filter, an efficient range query processing algorithm is presented. It can directly return the locations of the I/O units that contain the data in the query result without any access to the input dataset. The time complexity of the algorithm is O(3(m)h) , where h is the number of I/O units partially overlapping with a range query, and m is the dimension number. Since m is usually o(root logn) , it is a sublinear time algorithm if V=O(n) , where n is the size of the input dataset, V = & prod;(m)(i=1)d(i) , and di is the number of distinct values on the i -th dimension of the dataset for 1 <= i <= m . Experimental results show that the multi-dimensional range filter has low false positive rate and good filtering efficiency. The proposed range query processing algorithm achieves at least 3 similar to 7 times improvement compared to the one-dimensional filter based algorithms on different datasets.

关键词： filtering algorithms Query processing Information filters Temperature measurement Extraterrestrial measurements Time complexity Electronic mail Multi-dimensional data range filter range query sublinear time

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Posterior Error Energy Minimization Based Combined FxNLMS and FxLMS Algorithm for Active Noise Control

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IEEE ACCESS 2024年 12卷 30754-30764页

作者： Pang, Yunhe Ou, Shifeng Cai, Zhuoran Gao, Ying Yantai Univ Sch Phys & Elect Informat Yantai 264005 Peoples R China

The filtered-x normalized least mean square (FxNLMS) algorithm is widely used in active noise control (ANC) systems. However, for cyclostationary input signals, the noise reduction performance of the FxNLMS algorithm is affected by the periodic power of cyclostationary inputs, which leads to a larger steady-state mean square deviation (MSD) than the filtered-x least mean squares (FxLMS) algorithm. In this paper, a new adaptive combination algorithm of FxNLMS and FxLMS is proposed to suppress cyclostationary input noise with fast convergence and low steady-state MSD. The mixing parameter for combining the two algorithms is obtained by minimizing the energy of the posterior error and updated using the time-average method. The mathematical analysis including the mean square convergence and computational complexity are performed. The simulation results show that the proposed combined-FxNLMS-FxLMS algorithm can efficiently combine the respective advantages of FxNLMS and FxLMS algorithms and improve the noise reduction performance of the ANC system.

关键词： filtering algorithms Convergence Adaptive filters Steady-state Vectors Noise reduction Approximation algorithms Noise measurement Parameter estimation Least mean squares methods Mathematical models Performance evaluation Cyclostationary process Active noise control adaptive combinations mixing parameter moving average

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Interacting Multiple-Mode Estimation Using Centroid Fixed Structure for High Precision

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第1期60卷 802-818页

作者： Li, Guowei Wang, Qiang Zhang, Shurui Sheng, Weixing Kirubarajan, Thia Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada

Model set design is an important area for multiple-mode estimation, and its main purpose is to design a suitable model set to improve the accuracy and stability of target tacking. In this article, a novel multiple model estimation algorithm, namely, centroid fixed structure of interacting multiple-model estimation (CFIMM), is proposed to obtain the characteristic of the high precision and strong stability. First, the minimum distance method and the minimum model set method are, respectively, provided. Then, based on those two methods, the centroid model set design method is proposed with three different approaches to split the centroid. It proves that the centroid model set has minimal mathematical expectations with the actual models, when the unknown real model space is very large and even uncountable. Finally, the processing of the proposed CFIMM algorithm based on the centroid model set design method is discussed in detail. The proposed CFIMM holds not only the advantages of centroid model set but also the characteristic of the high precision and strong stability. The simulations of CFIMM highlight the correctness and effectiveness of the proposed methods.

关键词： Estimation Mathematical models Adaptation models Target tracking filtering algorithms Design methodology Switches Centroid fix structure of multiple model interacting multiple model (IMM) maneuvering target model set design

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Robust Object Tracking Against Sensor Failures With Centralized IMM Filter

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IEEE ACCESS 2024年 12卷 85203-85218页

作者： Choi, Jaeho Park, Jongwon Huh, Kunsoo Hanyang Univ Dept Automot Engn Automot Comp Convergence Seoul 04763 South Korea Hanyang Univ Dept Automot Engn Seoul 04763 South Korea

Highly automated driving requires the use of multiple sensors for reliable tracking functionality. In response to the requirement, the proposed method modifies the conventional Interacting Multiple Model (IMM) filter to fuse multi-sensor data by utilizing the independence of observations. In addition, the proposed IMM is integrated with a Centralized Kalman Filter (CKF) that ensures track continuity against sensor failures, providing optimal state estimates. When tracking objects in a moving reference frame, such as in autonomous vehicles, onboard sensor measurements represent relative values, making it challenging to estimate the actual motion of objects. While transforming states to a global coordinates is a solution, the solution can arise another problem where the tracking results depends on the status and performance of the localization. Therefore, to tackle the problem, a track compensation algorithm utilizing a hybrid coordinate system is proposed. The actual motions of objects are estimated based on errors between the track state and the associated measurement. The performance of the proposed algorithms is demonstrated using experimental scenario data conducted with an actual vehicle.

关键词： Object tracking Information filters Radar tracking Laser radar filtering algorithms Sensor fusion Fuses Multisensor systems State estimation Interacting multiple model filter multi-sensor system sensor failure sensor fusion state estimation

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SCIMITAR: Stochastic Computing In-Memory In-Situ Tracking ARchitecture for Event-Based Cameras

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2024年第11期43卷 4214-4225页

作者： Romaszkan, Wojciech Yang, Jiyue Graening, Alexander Jacob, Vinod K. Sen, Jishnu Pamarti, Sudhakar Gupta, Puneet Univ Calif Los Angeles Dept Elect & Comp Engn Los Angeles CA 90095 USA

Event-based cameras offer low latency and high-dynamic range imaging data in a sparse format that is well-suited for high-speed object tracking. Processing this sparse data in the same way as traditional camera data requires a great deal of unnecessary computation, making it difficult to take advantage of the high-effective frame rate for real-time processing. In this work, we propose an accelerator for high-speed object tracking on event-based camera data. SCIMITAR combines digital in-memory stochastic computing, in-situ stochastic stream generation, and multiple optimizations for utilizing input sparsity. SCIMITAR provides unparalleled performance with latency and energy that scale with sparsity. We demonstrate SCIMITAR performance on an object tracking application using circuit-level simulations of custom-designed compute-in-memory (CIM) macros and digital circuits. We achieve a frame processing rate of 26k frames/s with 100 regions-of-interest per frame and equivalent or better than state-of-the-art tracking accuracy. The accelerator achieves a peak throughput of 71 TOP/S and energy efficiency of 733 to 1702 TOP/S/W demonstrated on a range of event-based vision datasets, which is 5x higher than other CIM solutions.

关键词： Cameras Pipelines filtering algorithms Object tracking Computer architecture Stochastic processes Integrated circuit modeling Approximate computing computer vision gabor filters in-memory computing neuromorphic cameras object tracking stochastic computing systems architecture

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Lightweight Marginalized Particle filtering With Enhanced Consistency for Terrain Referenced Navigation

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2022年第3期58卷 2493-2504页

作者： Choe, Yeongkwon Song, Jin Woo Park, Chan Gook Seoul Natl Univ Dept Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Automat & Syst Res Inst Seoul 08826 South Korea Sejong Univ Sch Intelligent Mechatron Engn & Convergence Engn Seoul 05006 South Korea Seoul Natl Univ Inst Adv Aerosp Technol Seoul 08826 South Korea

This article proposes a computationally lightweight marginalized particle filtering (MPF) algorithm with improved filter consistency. For mixed linear/nonlinear state-space models such as terrain referenced navigation (TRN) models, an MPF can improve the convergence and efficiency of the filters by separately applying a Kalman filter (KF) and a particle filter (PF) to each state space rather than using only one type of filter. However, an MPF still has a high degree of complexity because it requires the same number of KFs as the number of particles in the PF. To address this issue, our method simplifies the MPF through Gaussian approximation to use only one KF and improves the filter consistency compared to other MPF simplification methods. The proposed method further reduces the complexity by considering the dynamic model of a TRN system. This article presents a complexity analysis between the original MPF and other MPF simplification methods and numerical experiments for a range/bearing example and a TRN system. The results show that the proposed algorithm has better filter consistency than the existing simplification algorithms and can achieve performance comparable to an MPF with less computation.

关键词： filtering Complexity theory Nonlinear filters filtering algorithms Aerodynamics Particle filters Navigation Importance sampling (IS) marginalized particle filter (MPF) nonlinear filtering Rao-Blackwellization terrain referenced navigation (TRN)

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Robust Constrained Normalized M-Estimate Subband Adaptive Filter: Algorithm Derivation and Performance Analysis

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第8期71卷 4010-4014页

作者： Xu, Wenjing Zhao, Haiquan Lv, Shaohui Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China

This brief presents a constrained normalized M-estimate subband adaptive filter (CNMSAF) algorithm that is robust to impulsive noise, which utilizes subband decomposition technique to whiten the colored input signals thereby achieving fast convergence speed and low computational complexity. In addition, the stability and theoretical mean square deviation performance of the algorithm are analyzed. In order to solve the constrained filtering problem with sparsity, the L-1 norm of the filter weight vector is used as an additional constraint, and we also propose the L-1 -CNMSAF algorithm. Computer simulations verify the accuracy of the theoretical analysis and the effectiveness of the proposed algorithms.

关键词： Vectors Adaptive filters filtering algorithms Convergence Computational complexity Performance analysis Optimization Constrained adaptive filter colored input subband adaptive filter M-estimate sparse system

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