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State Estimation With Nonlinear Inequality Constraints for Small Celestial Body Flexible Landing

IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2025年第2期61卷 4155-4167页

作者： Cui, Pingyuan Chen, Zelong Ge, Dantong Zhu, Shengying Cui, Shisheng Beijing Inst Technol Sch Aerosp Engn Beijing 100081 Peoples R China Beijing Inst Technol Minist Ind & Informat Technol Key Lab Autonomous Nav Control Deep Space Explorat Beijing 100081 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol State Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

Compared to rigid landers, a flexible lander can reduce the landing risks in small celestial body missions by taking advantage of its soft structure. In this article, we focus on the autonomous navigation problem of the flexible lander. Particularly, we consider the utilization of the additional information provided by the inherent state constraints of the landing system, i.e., the flexible deformation constraints, into the design of the navigation filter. To involve such nonlinear inequality constraints, we develop a constrained filtering algorithm with theoretically guaranteed performance. By exploiting the geometrical relationship between the state estimates and the constraint region, we establish the estimation refinement theorem. This theorem presents the sufficient condition for improving estimation accuracy through the inclusion of constraints. Guided by this theorem, we devise a constrained filter where a maximum margin separating hyperplane is optimized to refine the state estimate. Further, we demonstrate that the constrained estimation error is exponentially bounded. At last, we validate the proposed filter through a 433 Eros-based flexible landing simulation.

关键词： Deformation Navigation Information filters Optical filters Estimation error Autonomous robots Surface topography State estimation filtering theory filtering algorithms Autonomous navigation estimation refinement theorem flexible landing inequality constraints stability of nonlinear systems

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Innovative Interaction Approach in IMM filtering for Vehicle Motion Models With Unequal States Dimension

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2022年第4期71卷 3579-3594页

作者： Zubaca, Jasmina Stolz, Michael Seeber, Richard Schratter, Markus Watzenig, Daniel Graz Univ Technol Inst Automat & Control A-8010 Graz Austria Virtual Vehicle Res GmbH A-8010 Graz Austria

Robust and adaptive vehicle state estimation and tracking algorithms have become a very important part within the autonomous driving field. The family of interacting multiple model (IMM) filters has shown to provide very effective and accurate state estimation in systems whose behavior patterns change significantly over time. The main reason for the improved performance of IMM filters compared to single model approaches is the mode mixing, which constantly aligns the different models. This paper proposes an innovative way for the mode mixing, when the state-vectors of the models are of different size. The proposed mixing approach consists of two parts: firstly mixing the common states and secondly weighting between original and mixed states based on the model probabilities. Results from artificial simulations and real world measurement setups are shown to demonstrate the validity of the approach. Compared to previously suggested solutions, the proposed approach is more general and the overall complexity of the mode mixing step is reduced, which is the main contribution of the presented paper.

关键词： State estimation filtering algorithms Tracking Data models Autonomous vehicles Noise measurement Motion estimation Autonomous driving interacting multiple model filter mode mixing state estimation vehicle motion models vehicle tracking

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ReAdapt-II: Energy-Quality Optimizations for VLSI Adaptive Filters Through Automatic Reconfiguration and Built-In Iterative Dividers

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年第2期33卷 558-562页

作者： Pereira, Pedro T. L. Costa, Patricia Ucker L. da Costa, Eduardo Flores, Paulo Bampi, Sergio Fed Univ Rio Grande UFRGS Informat Inst Grad Program Microelect PGMICRO BR-91501970 Porto Alegre Brazil Univ Catolica Pelotas UCPel Grad Program Elect Engn & Comp BR-96015560 Pelotas Brazil Univ Lisboa ULisboa INESC ID P-1000029 Lisbon Portugal Univ Lisboa ULisboa Inst Super Tecn IST P-1000029 Lisbon Portugal

Adaptive filters using least mean square (LMS) algorithms offer high precision, low complexity, and fast convergence, but choosing the correct algorithm can be difficult and time-consuming. In this brief, we present ReAdapt-II, a VLSI circuit that enhances energy efficiency in adaptive filters through automatic reconfiguration and built-in iterative dividers, optimizing the energy-quality (EQ) tradeoff. This design features a self-selecting, reconfigurable hardware system with four adaptive algorithms, integrating iterative-based dividers and reusing arithmetic operators. Our results show a minimum energy consumption reduction of 39.75%, a 66.61% reduction in the circuit area, and a maximum accuracy increase of 17.07% compared with the previous ReAdapt architecture.

关键词： Adaptive filters filtering algorithms Very large scale integration Computer architecture Accuracy Power demand Logic gates energy-quality (EQ)-scalable filtering feedback-control circuit iterative-based divider reconfigurable architecture

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Deep Learning-Based Performance Testing for Analog Integrated Circuits

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2025年第4期33卷 1187-1191页

作者： Cao, Jiawei Guo, Chongtao Wang, Houjun Wang, Zhigang Li, Hao Li, Geoffrey Ye Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Guangdong Peoples R China Shenzhen Univ Coll Elect & Informat Engn Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China Imperial Coll London Dept Elect & Elect Engn London SW7 2AZ England

In this brief, we propose a deep learning-based performance testing framework to minimize the number of required test modules while guaranteeing the accuracy requirement, where a test module corresponds to a combination of one circuit and one stimulus. First, we apply a deep neural network (DNN) to establish the mapping from the response of the circuit under test (CUT) in each module to all specifications to be tested. Then, the required test modules are selected by solving a 0-1 integer programming problem. Finally, the predictions from the selected test modules are combined by a DNN to form the specification estimations. The simulation results validate the proposed approach in terms of testing accuracy and cost.

关键词： Testing Accuracy Costs Training Very large scale integration Integer programming Hardware filtering algorithms Circuit faults Artificial neural networks Analog integrated circuits (ICs) deep learning intelligent method low cost performance testing

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Secure and Efficient Bloom-Filter-Based Image Search in Cloud-Based Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2024年第3期11卷 5024-5035页

作者： Li, Yingying Ma, Jianfeng Miao, Yinbin Wang, Xiangyu Lu, Rongxing Zhang, Wei Hangzhou Normal Univ Sch Math Hangzhou 311121 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Xidian Univ Shaanxi Key Lab Network & Syst Secur Xian 710071 Peoples R China Univ New Brunswick Fac Comp Sci Fredericton NB E3B 5A3 Canada

Image search is a hot topic, which has played a significant role in various Internet of Things (IoT) applications, such as disease diagnosis, face recognition, and fingerprint recognition. Meanwhile, the proliferation of images has led image owners to outsource images to the cloud for reducing local storage and computation burdens. Therefore, image search without compromising privacy over cloud has received considerable attention and extensively explored in the literature. Many Bloom filter (BF)-based schemes have been put forth in past years, however, most of them suffer from high storage overhead, low false positive rate, and even expose the values in BF. To solve these challenges, in this article, we first design a merged and repeated indistinguishable BF (MRIBF) index structure, which can reduce the storage overhead and achieve adaptive security with a low false positive rate. Then, with the MRIBF, we propose a secure and efficient BF-based image search (BFIS) scheme to achieve a faster-than-linear and more accurate search. Detailed theoretical analysis shows that our scheme is really accurate and secure. Extensive experiments demonstrate that our scheme is indeed efficient and feasible.

关键词： Cryptography Internet of Things Cloud computing Indexes filtering algorithms Homomorphic encryption Feature extraction Bloom filter (BF) clustering image search

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A Unified Framework for Mining Batch and Periodic Batch in Data Streams

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第11期36卷 5544-5561页

作者： Liu, Zirui Wang, Xiangyuan Wu, Yuhan Yang, Tong Yang, Kaicheng Zhang, Hailin Tu, Yaofeng Cui, Bin Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing 100871 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China ZTE Corp Beijing 100728 Peoples R China

Batch is an important pattern in data streams, which refers to a group of identical items that arrive closely. We find that some special batches that arrive periodically are of great value. In this paper, we formally define a new pattern, namely periodic batches. A group of periodic batches refers to several batches of the same item, where these batches arrive periodically. Studying periodic batches is important in many applications, such as caches, financial markets, online advertisements, networks, etc. This paper proposes a unified framework, namely the HyperCalm sketch, to detect batch and periodic batch in data streams. HyperCalm sketch takes two phases to detect periodic batches. In phase 1, we propose a time-aware Bloom filter, called HyperBloomFilter (HyperBF), to detect batches. In phase 2, we propose an enhanced top-k algorithm, called Calm Space-Saving (CalmSS), to report top-k periodic batches. Extensive experiments show HyperCalm outperforms the strawman solutions 4x in term of average relative error and 98.1x in term of speed. All related codes are open-sourced.

关键词： Streams filtering theory filtering algorithms Data mining Information filters Real-time systems Time-frequency analysis data stream sketch periodic batch

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A Multihypotheses Importance Density for SLAM in Cluttered Scenarios

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IEEE TRANSACTIONS ON ROBOTICS 2024年 40卷 1019-1035页

作者： Kaltiokallio, Ossi Hostettler, Roland Ge, Yu Kim, Hyowon Talvitie, Jukka Wymeersch, Henk Valkama, Mikko Tampere Univ Unit Elect Engn Tampere 33100 Finland Uppsala Univ Dept Elect Engn S-75236 Uppsala Sweden Chalmers Univ Technol Dept Elect Engn S- 41258 Gothenburg Sweden Chungnam Natl Univ Dept Elect Engn Daejeon 34134 South Korea

One of the most fundamental problems in simultaneous localization and mapping (SLAM) is the ability to take into account data association (DA) uncertainties. In this article, this problem is addressed by proposing a multihypotheses sampling distribution for particle filtering-based SLAM algorithms. By modeling the measurements and landmarks as random finite sets, an importance density approximation that incorporates DA uncertainties is derived. Then, a tractable Gaussian mixture model approximation of the multihypotheses importance density is proposed, in which each mixture component represents a different DA. Finally, an iterative method for approximating the mixture components of the sampling distribution is utilized and a partitioned update strategy is developed. Using synthetic and experimental data, it is demonstrated that the proposed importance density improves the accuracy and robustness of landmark-based SLAM in cluttered scenarios over state-of-the-art methods. At the same time, the partitioned update strategy makes it possible to include multiple DA hypotheses in the importance density approximation, leading to a favorable linear complexity scaling, in terms of the number of landmarks in the field-of-view.

关键词： Simultaneous localization and mapping Robots Uncertainty Density measurement Radio frequency Probabilistic logic filtering algorithms Importance density particle filter (PF) probability hypotheses density random finite set (RFS) simultaneous localization and mapping (SLAM)

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A Level Set Method With Heterogeneity Filter for Side-Scan Sonar Image Segmentation

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IEEE SENSORS JOURNAL 2024年第1期24卷 584-595页

作者： Zhang, Meiyan Cai, Wenyu Wang, Yuhai Zhu, Jifeng Zhejiang Univ Water Resources & Elect Power Coll Elect Engn Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Coll Elect & Informat Hangzhou 310018 Peoples R China

Small underwater target detection from sonar images remains a challenging task. In this article, a novel level-set-based image segmentation algorithm combined with heterogeneity filter is proposed to segment target from original sonar images. The proposed method first uses nonlocal means filter to remove speckle noise of sonar image, and then applies super-pixel method to aggregate areas with similar texture, thus reducing computational complexity. In addition, two heterogeneity filters are used to eliminate heterogeneity in sonar images and enhance target contours. Moreover, the adaptive threshold is provided to obtain the rough contours of highlight and shadow areas. The level set method is further evolved on the basis of rough contours to obtain fine contours of underwater targets. Extensive experimental results verify that the proposed method has a better performance than that of the traditional sonar image segmentation algorithms in terms of false alarms, missing alarms, etc.

关键词： Image segmentation Sonar Level set filtering algorithms Sonar detection Sensors Object detection Adaptive threshold heterogeneity filter level set method sonar image segmentation

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Application of Adaptive Lasso-Based Minimum Entropy Deconvolution for Bearing Fault Detection Based on Vibration Signal

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IEEE SENSORS JOURNAL 2024年第14期24卷 22711-22719页

作者： Sun, Yuanhang Zhao, Yuhao Shi, Qing Cao, Jianbin Wei, Jianan Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China Natl Univ Singapore Singapore 119077 Singapore Shanghai Univ Shanghai 200444 Peoples R China

Minimum entropy deconvolution (MED) and its related methods have been applied to bearing fault detection extensively due to their good performance in fault feature enhancement. However, their performance is also constrained largely by their parameter setup. In order to improve the performance, an adaptive least absolute shrinkage and selection operator (Lasso)-based MED (AdaLMED) is proposed in this article. Different from previous MED and its related methods, AdaLMED utilizes their respective advantage of Lasso and MED to strengthen its performance on fault detection. Moreover, a k-sparsity strategy is introduced to Lasso for setting its regularization parameter adaptively in AdaLMED, which improves the performance of Lasso effectively without losing fault-related information. Based on the fault feature extraction ability and fault feature enhancement ability of Lasso and MED, the proposed AdaLMED has a better performance than the previous MED and MED-related methods. For verifying the performance of the proposed AdaLMED, AdaLMED and MED-related methods are performed on the simulation and practical fault signal, respectively. The results indicate that AdaLMED has the excellent robust stability to parameter setup and better applicability in practical industrial application compared with MED and other MED-related methods.

关键词： Vibrations Fault detection Noise filtering algorithms Kurtosis Feature extraction Deconvolution k-sparsity least absolute shrinkage and selection operator (Lasso) minimum entropy deconvolution (MED) vibration signal

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Leveraging Spatiotemporal Estimation for Online Adaptive Steady-State Visual Evoked Potential Recognition

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2024年第6期16卷 1943-1954页

作者： Jin, Jing He, Xinjie Allison, Brendan Z. Qin, Ke Wang, Xingyu Cichocki, Andrzej East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Sch Math Shanghai 200237 Peoples R China Univ Calif San Diego Dept Cognit Sci San Diego CA 92093 USA Nicolaus Copernicus Univ Dept Informat PL-87100 Torun Poland RIKEN Brain Sci Inst Lab Adv Brain Signal Proc Tokyo 1030027 Japan

Online adaptive canonical correction analysis (OACCA) has been applied successfully in the recently popular steady-state visual evoked potential (SSVEP) target recognition methods. However, due to the significant amount of spatiotemporal relevant background noise in the online historical sample label data of OACCA, there is redundant noise component in the learned common spatial filter that can reduce online classification accuracy. Aiming at solving this defect in OACCA, we designed an online spatial-temporal equalization filter (STE) to suppress the background noise component in the electroencephalography (EEG). Meanwhile, an adaptive decoding method for SSVEP based on online spatial-temporal estimation (STE-OACCA) is proposed by combining the online STE filter and the OACCA algorithm. A pseudoonline test on the Tsinghua University FBCCA-DW dataset shows that the proposed STE-OACCA method significantly outperforms the CCA, MSI, OACCA approaches as well as STE-CCA. More importantly, proposed method can be directly used in online SSVEP recognition without calibration. The proposed algorithm is robust, which is promising for the development of practical brain computer interface (BCI).

关键词： Spatial filters Electroencephalography Correlation coefficient Visualization filtering algorithms Estimation Correlation Brain computer interface (BCI) common spatial filter electroencephalography (EEG) online adaptive canonical correction analysis (OACCA) online spatial-temporal estimation (STE) steady-state visual evoked potential (SSVEP)

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