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Real-Time Power Tracking Control for Supercapacitor-Based Regenerative Braking System Using interacting multiple model filter

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2023年第9期72卷 11437-11445页

作者： Jin, Weibin Gao, Yan Zhang, Hongjuan Jin, Baoquan Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Shanxi Peoples R China Taiyuan Univ Technol Key Lab Adv Transducers & Intelligent Control Sys Minist Educ Taiyuan 030024 Shanxi Peoples R China

The mismatch between the charging power of the supercapacitor and the braking power of the motor will lead to redundant energy cycling. To accurately track the braking power, a real-time tracking control of braking power using interacting multiple model filter is proposed. Three supercapacitor models are established, and a novel interacting multiplemodel filter is developed accordingly. The main capacitor voltages of three supercapacitor models interact to obtain the terminal voltage of the supercapacitor. Then the loss function is established with power tracking as the objective. The reference current of the supercapacitor is obtained under the constraints. The comparative experimental results show that with the proposed control strategy, the redundant energy cycling of the system is reduced by 55% within one operating cycle.

关键词： Supercapacitor tracking control interacting multiple model filter energy utilization efficiency regenerative braking system

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Maneuvering Target Tracking using the Autoencoder-interacting multiple model filter 54

Maneuvering Target Tracking using the Autoencoder-Interactin...

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54th Asilomar Conference on Signals, Systems and Computers

作者： Vedula, Kirty Weiss, Matthew L. Paffenroth, Randy C. Uzarski, Joshua R. Brown, D. Richard, III Worcester Polytech Inst 100 Inst Rd Worcester MA 01609 USA US Army CCDC SC Soldier Protect & Survivabil Directorate Natick MA 01760 USA

ISBN: (纸本)9780738131269

This paper considers the problem of tracking and predicting the state of a dynamic system with stochastic dynamics and multiple modes of operation. A well-known approach to this problem is the "interacting multiple model" (IMM) estimator, which uses knowledge of the different modes of operation to update a bank of Kalman filters (each optimal for a given mode of operation). The IMM combines estimates according to the posterior probability of the different modes. Despite their popularity, IMMs are known to sometimes be slow to detect mode switching, however, which can result in large state estimation errors. This paper addresses this problem by developing an Autoencoder-interacting multiple model (AEIMM) algorithm. The AEIMM effectively embeds an IMM within an autoencoder framework to create a hybrid approach using both deep learning and classical tracking frameworks. The motivation for this approach is that the neural network can perform nonlinear transformations on the measurements to help the IMM more quickly identify mode changes. The effectiveness of the AEIMM is demonstrated in a maneuvering target tracking scenario. Numerical results show that the AEIMM outperforms classical tracking techniques as well as hybrid techniques and a Long Short-Term Memory network in this scenario.

关键词： Kalman filter interacting multiple model filter Autoencoder Autoencoder-Kalman filter Deep Learning Neural Networks Target Tracking

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UGV Status Monitoring System Using interacting multiple model filter 18

UGV Status Monitoring System Using Interacting Multiple Mode...

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18th International Conference on Control, Automation and Systems (ICCAS)

作者： Kang, Chang Mook Shin, Jongho Lee, Taehyung Agcy Def Dev Daejeon 34186 South Korea

ISBN: (纸本)9788993215151

In this paper, we presents unmanned ground vehicle (UGV) status monitoring system using interacting multiple model (IMM) filter. It is known that the characteristics of the tire cornering stiffness varies according to driving conditions. From the IMM filter, we can obtain the stochastically best hybrid state of the vehicle. The probabilities of each model can represent the reliability of each model. Thus, we consider the probabilities of each model as the index representing the status of UGV. By obsering the index which is representing the status of UGV, the operator can decide whether to continue operating or not. The performance of the proposed scheme was validated via experimental results with a UGV on a unpaved road. We observe that the proposed scheme effectively represents the status of UGV under various driving conditions.

关键词： interacting multiple model filter vehicle dynamics unmanned ground vehicle

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A low-complexity interacting multiple model filter for maneuvering target tracking

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2017年 73卷 157-164页

作者： Khalid, Syed Safwan Abrar, Shafayat COMSATS Inst Informat Technol Islamabad 44000 Pakistan King Abdullah Univ Sci & Technol Thuwal 23955 Saudi Arabia

In this work, we address the target tracking problem fora coordinate-decoupled Markovian jump-mean acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results. (C) 2017 Elsevier GmbH. All rights reserved.

关键词： Tracking Kalman filtering interacting multiple model filter Maneuvering targets Low-complexity

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UGV Status Monitoring System Using interacting multiple model filter

UGV Status Monitoring System Using Interacting Multiple Mode...

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International Conference on Control, Automation and Systems

作者： Chang Mook Kang Jongho Shin Taehyung Lee Agency for Defense Development Daejeon Korea

ISBN: (纸本)9781538670804;9788993215168

关键词： interacting multiple model filter Vehicle dynamics Unmanned ground vehicle

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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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Adaptive Decentralized Sensor Fusion for Autonomous Vehicle: Estimating the Position of Surrounding Vehicles

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IEEE ACCESS 2023年 11卷 90999-91008页

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

The tracking accuracy of nearby vehicles determines the safety and feasibility of driver assistance systems or autonomous vehicles. Recent research has been active to employ additional sensors or to combine heterogeneous sensors for more accurate tracking performance. Especially, autonomous driving technologies require a sensor fusion technique that considers various driving environments. In this research, a novel method for high-level data fusion is proposed to improve the accuracy of tracking surrounding vehicles. In response to the changing driving environment, the locations of the vehicles are estimated in real-time using an adaptive track-to-track fusion technique and an interacting multiple model filter. Asynchronous measurements from multiple sensors such as radar, camera, and LiDAR, are utilized for the estimation. For each sensor, two motion models representing the vehicle's movement are applied to increase the estimation accuracy. Utilizing a multimodal network-based track-to-track fusion approach, it combines the estimates of the target vehicle position from each sensor into a single estimate. The inputs of the network are intended to determine the reliability of each sensor, considering the driving conditions that may affect sensor accuracy. Also, multiple embeddings in the network are created so that the corresponding data maintains its relevance and enables the real-time computing. The proposed method is verified using real driving data collected from various environments.

关键词： Perception sensor fusion autonomous vehicle advanced driver assistance system track-to-track fusion interacting multiple model filter multimodal learning

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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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An interacting multiple model filter-based autofocus strategy for confocal time-lapse microscopy

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JOURNAL OF MICROSCOPY 2012年第3期245卷 265-275页

作者： Chowdhury, S. Kandhavelu, M. Yli-Harja, O. Ribeiro, A. S. Tampere Univ Technol Computat Syst Biol Res Grp Dept Signal Proc FIN-33101 Tampere Finland Inst Syst Biol Seattle WA USA

Gene expression and other cellular processes are stochastic, thus their study requires observing multiple events in multiple cells. Therefore, confocal microscopy cell imaging has recently gained much interest. In time-lapse imaging, adjustments are needed at short intervals to compensate for focus drift. There are several automated methods for this purpose. In general, before acquiring higher resolution images, software-based autofocus algorithms require a set of low-resolution images along the z-axis to determine the plane for which a predefined focusing function is maximized. These algorithms require 10100 z-slices each time, and there is no fixed number or upper limit of required z-slices that ensures optimal focusing. The higher is this number, the stronger is photo bleaching, hampering the feasibility of long-time series measurements. We propose a new focusing strategy in time-lapse imaging. The algorithm relies on the nature and predictability of the focus drift. We first show that the focus drift curve is predictable within a small error bound in standard experimental setups. We, then, exploit the interactingmultiple model filter algorithm to predict the drift at time, t, based on the measurement at time t - 1. This allows a drastic reduction of thenumberof required z-slices for focusdrift correction, largely overcoming the problem of photo bleaching. In addition, we propose a new set of functions for focusing in time-lapse imaging, derived from preexisting ones. We demonstrate the method's efficiency in time-lapse imaging of Escherichia coli cells expressing MS2d-GFP tagged RNA molecules.

关键词： Fluto focus confocal microscopy focus drift interacting multiple model filter thermal drift time-lapse imaging

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Low Temperature, Current Dependent Battery State Estimation Using interacting multiple model Strategy

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IEEE ACCESS 2021年 9卷 99876-99889页

作者： Messing, Marvin Rahimifard, Sara Shoa, Tina Habibi, Saeid McMaster Univ Dept Mech Engn Hamilton ON L8S 4L7 Canada Cadex Elect Inc Richmond BC V6W 1J6 Canada

Lithium-ion battery State of Charge (SoC) estimation for Electric Vehicle (EV) applications must be robust and as accurate as possible to maximize battery utilization and ensure safe operation over a wide range of operating conditions. SoC estimation commonly utilizes filters such as the Extended Kalman filter (EKF) which rely on battery models, usually in the form of Equivalent Circuit models (ECM). At low temperatures the battery response to current draw becomes increasingly non-linear, resulting in amplified SoC estimation errors. In this study, current dependent SoC estimation at low temperature is proposed using an interacting multiple model (IMM) filter with three ECMs covering a range of C-rates. The IMM is combined with the Smooth Variable Structure filter (SVSF) to obtain robust SoC estimates within a SoC estimation error of 2%.

关键词： Batteries Integrated circuit modeling Electronic countermeasures State estimation Mathematical model Estimation error Temperature dependence Lithium-ion battery state estimation interacting multiple model filter smooth variable structure filter low temperature

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