Correct knowledge of noise statistics is essential for an effective estimator in maneuvering target *** practice,however,the noise statistics are usually unknown or not perfectly *** deal with the estimation problem i...
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Correct knowledge of noise statistics is essential for an effective estimator in maneuvering target *** practice,however,the noise statistics are usually unknown or not perfectly *** deal with the estimation problem in linear discretetime systems with Markov jump parameters,where the measurement noise covariance is unknown,a novel approach is presented in this *** approach is based on the interactingmultiplemodel(IMM) *** H Alter is employed to construct a noise statistics estimator to obtain the information which is necessary for the IMM *** our approach,even the priori knowledge of noise statistics is not *** noise statistics loss problem is solved while the merits of IMM algorithm is *** effectiveness of the proposed approach is demonstrated in comparison with single-model H Alter through Monte Carlo simulation for maneuvering target tracking.
In the centralized multi-sensor system, there is often a problem that how to remove measurements used early from the current state estimation, then re-calculate the current state estimation. The interactingmultiple m...
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Anti-ship missile is a kind of typical high maneuvering target which has the characteristics of strong motor and multiple mobile. Especially its unique form of "snake maneuver" brought great difficulties to ...
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ISBN:
(纸本)9781479947249
Anti-ship missile is a kind of typical high maneuvering target which has the characteristics of strong motor and multiple mobile. Especially its unique form of "snake maneuver" brought great difficulties to the tracking system. This article used the IMM algorithm and the "current" statistical model constituted model set, together with the method of UKF filter which avoid extended kalman tedious Jacobi matrix calculation, then we get a new method for anti-ship missiles tracking. Results show that the method has good tracking performance to strong maneuvering target just like anti-ship missile.
In this paper, the interaction and combination of Fuzzy Fading Memory (FFM) technique and Augmented Kalman Filtering (AUKF) method are presented for the state estimation of non-linear dynamic systems in presence of ma...
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In this paper, the interaction and combination of Fuzzy Fading Memory (FFM) technique and Augmented Kalman Filtering (AUKF) method are presented for the state estimation of non-linear dynamic systems in presence of maneuver. It is shown that the AUKF method in conjunction with the FFM technique (FFM-AUKF) can estimate the target states appropriately since the FFM tunes the covariance matrix of the AUKF method in presence of unknown target accelerations by using a fuzzy system. In addition, the benefits of both FFM technique and AUKF method are employed in the scheme of well-known interactingmultiplemodel (IMM) algorithm. The proposed Fuzzy IMM (FIMM) algorithm does not need the predefinition and adjustment of sub-filters with respect to the target maneuver and reduces the number of required sub-filters to cover the wide range of unknown target accelerations. The Monte Carlo simulation analysis shows the effectiveness of the above-mentioned methods in maneuvering target tracking. (C) 2013 Elsevier Inc. All rights reserved.
The traditional interactingmultiplemodel (IMM) algorithm has a fixed structure, in the region of high maneuvering target tracking, needs plenty of models to describe the target maneuver, brings the contradiction of ...
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The traditional interactingmultiplemodel (IMM) algorithm has a fixed structure, in the region of high maneuvering target tracking, needs plenty of models to describe the target maneuver, brings the contradiction of tracking performance and calculating amount, meanwhile, unnecessary inter-competition among the models maybe reduce the performance. To resolve this problem caused by IMM, an adaptive variable structure multiplemodel (AVSMM) algorithm is presented. According to the current target maneuvering level, just several models which related to the target motion, at different moments, the algorithm can adjust the model’s parameters and get a new model set, then begin to filter estimate. Simulation results show that this algorithm can match actual target trajectory with less computational complexity and better accuracy.
To deal with the problem of tracking a maneuvering target in range-based sensor networks, we firstly derive the multiplemodel posterior Cramer-Rao lower bound(PCRLB), and on the basis of this bound, choose the sensor...
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ISBN:
(纸本)9781424462520
To deal with the problem of tracking a maneuvering target in range-based sensor networks, we firstly derive the multiplemodel posterior Cramer-Rao lower bound(PCRLB), and on the basis of this bound, choose the sensor subset that may attend the incoming tracking event. Secondly, we design the sensor selection strategy under communication constraint, and further pick up the optimal sensor. Thirdly, we can estimate the state of the maneuvering target by making use of the interactingmultiplemodel(IMM) algorithm. Finally, the simulation results show the effectiveness of the proposed scheme.
The interactingmultiplemodel (IMM) algorithm has proved to be useful in tracking maneuvering targets. Tracking accuracy can be further improved using data fusion. Tracking of multiple targets using multiple sensors ...
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The interactingmultiplemodel (IMM) algorithm has proved to be useful in tracking maneuvering targets. Tracking accuracy can be further improved using data fusion. Tracking of multiple targets using multiple sensors and fusing them at a central site using centralized architecture involves communication of large volumes of measurements to a common site. This results in heavy processing requirement at the central site. Moreover, track updates have to be obtained in the fusion centre before the next measurement arrives. For solving this computational complexity, a cluster-based parallel processing solution is presented in this paper. In this scheme, measurements are sent to the data fusion centre where the measurements are partitioned and given to the slave processors in the cluster. The slave processors use the IMM algorithm to get accurate updates of the tracks. The master processor collects the updated tracks and performs data fusion using 'weight decision approach'. The improvement in the computation time using clusters in the data fusion centre is presented in this paper.
A new interactingmultiplemodel (IMM) algorithm using intelligent input estimation (IIE) is proposed for maneuvering target tracking. In the proposed method, the acceleration level for each sub-model is determined by...
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A new interactingmultiplemodel (IMM) algorithm using intelligent input estimation (IIE) is proposed for maneuvering target tracking. In the proposed method, the acceleration level for each sub-model is determined by IIE-the estimation of the unknown target acceleration by a fuzzy system using the relation between the residuals of the maneuvering filter and the non-maneuvering filter. The genetic algorithm (GA) is utilized to optimize a fuzzy system for a sub-model within a fixed range of target acceleration. Then, multiplemodels are represented as the acceleration levels estimated by these fuzzy systems, which are optimized for different ranges of target acceleration. In computer simulation for an incoming anti-ship missile, it is shown that the proposed method has better tracking performance compared with the adaptive interactingmultiplemodel (AIMM) algorithm.
A distributed multirate interactingmultiplemodel (DMRIMM) algorithm for multi-platform tracking is proposed. Under the assumption that each model operates at an update rate proportional to the model's assumed dy...
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A distributed multirate interactingmultiplemodel (DMRIMM) algorithm for multi-platform tracking is proposed. Under the assumption that each model operates at an update rate proportional to the model's assumed dynamics, a wavelet transform is used to generate equivalent lent multirate measurements and a multirate constant-velocity (MRCV) model is derived. Thus, a multirate interactingmultiplemodel (MRIMM) algorithm using an MRCV model and multirate measurements is obtained. Based on the multirate IMM, its distributed version DMRIMM algorithm for multiplatform tracking is proposed. The MRIMM algorithm is first employed to perform each local/platform estimation. A global filter is then constructed to perform a fusion of MRIMM estimations. The advantages of low computation loads and performance improvement are demonstrated through Monte Carlo simulations. (C) 2000 Elsevier Science Ltd. All rights reserved.
A Markov chain plays an important role in an interactingmultiplemodel (IMM) algorithm which has been shown to be effective for target tracking systems. Such systems are described by a mixing of continuous states and...
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A Markov chain plays an important role in an interactingmultiplemodel (IMM) algorithm which has been shown to be effective for target tracking systems. Such systems are described by a mixing of continuous states and discrete modes. The switching between system modes is governed by a Markov chain. In real world applications, this Markov chain may change or needs to be changed. Therefore, one may be concerned about a target tracking algorithm with the switching of a Markov chain. This paper concentrates on fault-tolerant algorithm design and algorithm analysis of IMM estimation with the switching of a Markov chain. Monte Carlo simulations are carried out and several conclusions are given.
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