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检索条件"主题词=Interacting Multiple Model"

共 350 条记录，以下是61-70 订阅

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Research on adaptive Kalman filtering based on interacting multiple model - art. no. 683324

Research on adaptive Kalman filtering based on interacting m...

引用

Conference on Electronic Imaging and Multimedia Technology V

作者： Zhang Yong Wu Qinzhang Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China

ISBN: (纸本)9780819470089

Some limits of standard Kalman filtering are simply analyzed. Such as the indefiniteness of motion resulted from targets maneuvering and the lower predictive precision brought about by non or little adaptive capabilities make standard Kalman filtering lower tracking precision and stabilization. interacting multiple model algorithm is adopted to combine with Kalman filter, and a new adaptive Kalman filtering algorithm for improving tracking capabilities is proposed. multiple models are designed to represent system possible running patterns, and "current" statistical model is designated as one of them. Each model has an independent Kalman filter, and the general state estimation is a kind of mixing data output produced by interacting among these models' state estimations through certain mixing probabilities. Each model state estimation is produced by one Kalman filter corresponding this system model. In simulation tests, three system models are designed to work, CV model, CA model and "current" statistic model. Tests show that the indefiniteness resulted from the target motional model approximately describing the target motional pattern, the lower adaptive tracking capabilities, and the lower tracking precision and stabilization of in targets tracking are improved efficiently. Moreover, the strong nonlinear problem is solved effectively.

关键词： interacting multiple model adaptive filtering maneuvering target "current" statistical model

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A novel initial alignment algorithm based on the interacting multiple model and the Huber methods

A novel initial alignment algorithm based on the interacting...

引用

IEEE/ION Position, Location and Navigation Symposium (PLANS)

作者： Gao, Wei Deng, Liying Yu, Fei Zhang, Ya Sun, Qian Harbin Engn Univ Coll Automat Harbin Heilongjiang Pr Peoples R China

ISBN: (纸本)9781509020423

Initial alignment is one of the key technologies in Strapdown inertial navigation system (SINS). It is divided into coarse alignment and fine alignment. The conventional method of fine alignment is to adopt Kalman filtering and uses single filter to estimate system states. In Kalman filtering, it is known that system model should be matched with actual system and the statistical characteristics of noise are supposed to be Gaussian. However, single model cannot describe the unknown filtering parameters in practical application. Moreover, noise may be contaminated and present a non-Gaussian form. This paper is devoted to solve these problems, presenting a new alignment method based on interacting multiple model (IMM) algorithm, in which sub-filters are designed to be Huber-based Kalman filters. Uncertain parameters can be depicted by a set of switching sub-models and Huber-based filters can deal with the problem of contaminated noise. Finally, simulations show that the result of this proposed method performs a higher accuracy than conventional method's.

关键词： Strapdown inertial navigation system Initial alignment interacting multiple model Huber-based Kalman filter

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Impact point prediction of small ballistic munitions with an interacting multiple model estimator

Impact point prediction of small ballistic munitions with an...

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Conference on Acquisition, Tracking, Pointing and Laser Systems Technologies XXI

作者： Conover, Steve Kerce, J. Clayton Brown, George Ehrman, Lisa Hardiman, David Georgia Tech Res Inst Atlanta GA 30332 USA AMSRD AMR SG CT Aviat & Missile RDEC Huntsville AL USA

ISBN: (纸本)9780819466914

The interacting multiple model (IMM) estimator, which mixes and blends results of multiple filters according to their mode probabilities, is frequently used to track targets whose motion is not well-captured by a single model. This paper extends the use of an IMM estimator to computing impact point predictions (IPPs) of small ballistic munitions whose motion models change when they reach transonic and supersonic speeds. Three approaches for computing IPPs are compared. The first approach propagates only the track from the most likely mode until it impacts the ground. Since this approach neglects inputs from the other modes, it is not desirable if multiple modes have near-equal probabilities. The second approach for computing IPPs propagates tracks from each model contained in the IMM estimator to the ground independent of each other and combines the resulting state estimates and covariances on the ground via a weighted sum in which weights are the model probabilities. The final approach investigated here is designed to take advantage of the computational savings of the first without sacrificing input from any of the IMM's modes. It fuses the tracks from the models together and propagates the fused track to the ground. Note that the second and third approaches reduce to the first if one of the models has a mode probability of one. Results from all three approaches are compared in simulation.

关键词： interacting multiple model impact point prediction

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Dynamic electrical impedance imaging with the interacting multiple model scheme

Dynamic electrical impedance imaging with the interacting mu...

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12th International Conference on Electrical Bio-Impedance/5th Electrical Impedance Tomography Conference

作者： Kim, KY Kim, BS Kim, MC Kim, S Isaacson, D Newell, JC Cheju Natl Univ Dept Elect & Elect Engn Cheju 690756 South Korea Cheju Natl Univ Dept Chem Engn Cheju 690756 South Korea Cheju Natl Univ Dept Nucl & Energy Engn Cheju 690756 South Korea Rensselaer Polytech Inst Dept Math Sci Troy NY 12180 USA Rensselaer Polytech Inst Dept Biomed Engn Troy NY 12180 USA

In this paper, an effective dynamical EIT imaging scheme is presented for on-line monitoring of the abruptly changing resistivity distribution inside the object, based on the interacting multiple model (IMM) algorithm. The inverse problem is treated as a stochastic nonlinear state estimation problem with the time-varying resistivity (state) being estimated on-line with the aid of the IMM algorithm. In the design of the IMM algorithm multiple models with different process noise covariance are incorporated to reduce the modeling uncertainty. Simulations and phantom experiments are provided to illustrate the proposed algorithm.

关键词： dynamical electrical impedance tomography inverse problems state estimation extended Kalman filter interacting multiple model

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Ultrasound image segmentation using an interacting multiple model probabilistic data association filter

Ultrasound image segmentation using an interacting multiple ...

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Medical Imaging 2004 Conference

作者： Abolmaesumi, P Sirouspour, MR Queens Univ Sch Comp Kingston ON K7L 3N6 Canada

ISBN: (纸本)0819452831

This paper presents a novel segmentation technique to extract cavity contours from ultrasound images. The problem is first discretized by projecting equispaced radii from an arbitrary seed point inside the cavity towards its boundary. The distance of the cavity boundary from the seed point;is modeled by the trajectory of a moving object. The motion of this moving object is assumed to be governed by a finite set of dynamical models subject to uncertainty. Candidate edge points obtained along each radius may include the actual object position and some false measurements. This modeling approach enables us to employ the interacting multiple model (IMM) estimator along with a probabilistic data association filter (PDAF) for contour extraction. Since the method does not employ any numerical optimization, convergence is very fast. The stability and accuracy of the method is demonstrated by segmenting contours from a series of ultrasound images with different levels of speckles and echo dropouts in the cavity contour. An application of the method in segmenting bone contours from CT images is also presented. We have demonstrated that in certain situations, such as wrist CT images, when the distance between two bones is very small and conventional thresholding techniques fail, our algorithm is able to segment bones successfully.

关键词： image processing segmentation ultrasound images CT images medical imaging interacting multiple model probabilistic data association filter contour extraction

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interacting multiple model Algorithm with Quasi-Monte Carlo Kalman Filter

Interacting Multiple Model Algorithm with Quasi-Monte Carlo ...

引用

32nd Chinese Control Conference (CCC)

作者： Yang Yanbo Zou Jie Yang Feng Qin Yuemei Pan Quan Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Key Lab Natl Def Sci & Technol Fire Control Techn Luoyang 471000 Peoples R China

ISBN: (纸本)9781479900305

The interacting multiple model (IMM) Algorithm is widely used in multi-model systems over the recent years. It often needs to handle nonlinearity of each mode in the framework of IMM. Compared with particle filter based on sequential Monte Carlo method, the Quasi-Monte Carlo (QMC) method has a superior performance in dealing with nonlinearity. Based on the technique that the QMC method is introduced into the IMM framework to dealing with the nonlinearity in each mode, the IMM algorithm with Quasi-Monte Carlo Kalman Filter (QMC-KF) is proposed in this paper. Meanwhile, the sample number in each mode is decided by the value of the mode probability in order to pay more attention to the dominant mode. Simulation results show that the performance of the proposed IMMQMC-KF is prior to that of the IMMUKF, IMMPF, IMMEPF and IMMUPF. Furthermore, the computing load of the IMMQMC-KF is lower than that of the IMMPF, IMMEPF and IMMUPF.

关键词： Quasi-Monte Carlo QMC-KF nonlinear system interacting multiple model mode probability

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Unscented information consensus filter for maneuvering target tracking based on interacting multiple model 21

Unscented information consensus filter for maneuvering targe...

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21st International Conference on Information Fusion (FUSION)

作者： Ding, Ziran Liu, Yu Liu, Jun Sun, Shun Qi, Lin Naval Aviat Univ Yantai Peoples R China

ISBN: (纸本)9780996452762

This paper deals with the problem of maneuvering target tracking with networked multiple sensors. To avoid linearization of nonlinear function, and obtain more accurate estimate for maneuvering target, a novel distributed maneuvering target tracking method based on interacting multiple model with unscented information consensus protocol is proposed. The pseudo measurement matrix is computed according to unscented transform, based on which the information form of measurements is calculated and local estimate is updated. To unify estimation in different sensors and improve the maneuvering target tracking accuracy throughout the whole network, the weighted information consensus protocol is applied for each model in all sensors. With multiple models interacting, the posterior estimate in each sensor is acquired with weighted combination of the model-conditioned estimates. Experimental results demonstrate that the proposed algorithm outperforms the existing methods in the aspect of tracking accuracy and agreement of estimates in all sensors.

关键词： maneuvering target tracking multiple sensor fusion consensus interacting multiple model

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COMPENSATING POWER AMPLIFIER DISTORTION IN COGNITIVE RADIO SYSTEMS WITH ADAPTIVE interacting multiple model 23

COMPENSATING POWER AMPLIFIER DISTORTION IN COGNITIVE RADIO S...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Ben Mabrouk, Mouna Grivel, Eric Magnant, Clement Ferre, Guillaume Deltimple, Nathalie Univ Bordeaux Bordeaux INP CNRS UMR 5218IMS Lab 351 Cours Liberat F-33405 Talence France

ISBN: (纸本)9780992862633

This work aims at improving the power amplifier (PA) efficiency in uplink OFDM-based cognitive radio (CR) communications. Unlike the traditional approaches, we suggest transmitting a non-linearily amplified signal without any filtering and addressing the OEDM sample estimation from the distorted signal at the receiver. The proposed post-distortion and detection technique is based on a Volterra model for the PA and the channel. As the transmission can switch from one sub-band to another, the CR-PA behavior varies over time and the Volterra kernels can be constant or suddenly change. Therefore, an interactive multiple model (IMM) combining extended Kalman filters is considered. The transition probability matrix, which plays a key role in the IMM, is also sequentially estimated. The resulting uplink system has various advantages: it learns from the observations and a part of the computational load is exported to the receiver, which is not battery driven unlike the mobile terminal.

关键词： Power amplifier digital post/pre-distortion cognitive radio Volterra modeling interacting multiple model transition probability matrix estimation

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A detection adaptive filtering algorithm with interacting multiple model for-multisensor tracking 6

A detection adaptive filtering algorithm with interacting mu...

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6th International Conference on Signal Processing

作者： Zhou, GJ Wu, ZL Quan, TF Harbin Inst Technol Dept Elect & Commun Engn Harbin 150001 Peoples R China

ISBN: (纸本)0780374886

A maneuvering target tracking algorithm for the multisensor system using an image sensor and other different sensors is presented which combines variable dimension filter (VDF) with interacting multiple model (IMM). This technique uses an interacting multiple model estimator instead of a single model filter used in VDF when the maneuver detector declares an onset of maneuver. The performances of this approach, VDF and IMM algorithms are investigated and compared. During nonmaneuvering period, this approach is better than the IMM algorithm and equal to the VDF algorithm. During maneuvering period, especially right after the end of a maneuver, this approach provides better estimations than the VDF algorithm.

关键词： optical tracking target tracking tracking filters adaptive filters image processing radar tracking sensor fusion detection adaptive filtering interacting multiple model interactive multiple model IMM maneuver detector multisensor tracking maneuvering target tracking image sensor variable dimension filter tracking filter 3D radar data fusion

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Comparison of Kalman Filter and Converted Measurements Kalman Filter for interacting multiple model 26

Comparison of Kalman Filter and Converted Measurements Kalma...

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26th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Turgut, Kubra Hocaoglu, Ali Koksal Gebze Tekn Univ Elekt Muhendisligi Kocaeli Turkey

ISBN: (纸本)9781538615010

It is well known that Converted Measurements Kalman Filter performs better Kalman Filter when the measurements are obtained in polar coordinates. In this study, we show that when used with interacting multiple model, Kalman Filter has a better performance than Converted Measurements Kalman Filter, as opposed to the single mode case, for the case of high target distance, heavy target maneuvers and high measurement noise. We also show that this becomes even more pronounced as the number of modes increases.

关键词： Target tracking interacting multiple model Converted Measurements Kalman Filter

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