作者：
Wu, HaoYe, HaoTsinghua Univ
Dept Automat Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China
In this article, the problem of state estimation for networked systems (NSs) with three kinds of observation uncertainties (i.e. missing measurements, packet delays and packet dropouts) and without timestamps in the m...
详细信息
In this article, the problem of state estimation for networked systems (NSs) with three kinds of observation uncertainties (i.e. missing measurements, packet delays and packet dropouts) and without timestamps in the measurement data is investigated. Both the measurement state and network transmission state are assumed to follow a Markov process, which can capture the temporal correlation nature of the measurement process and network channels. The NS is modelled as a special Markovian jump linear system (MJLS). Then, by modifying the widely adopted interacting multiple models (imm) algorithm, an extended imm algorithm for the state estimation of the MJLS is proposed. The multiple filters strategy adopted in this article takes advantage of the particular characteristics of each mode as much as possible and updates the probability estimation of each mode;ultimately, it achieves better estimation performance than the single filter strategy used in existing approaches. Another contribution of this article is the extension of the standard imm algorithm to handle some special characteristics of the MJLS established herein. The effectiveness and advantage of the proposed method are verified by simulation.
Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisit...
详细信息
ISBN:
(纸本)9781628419191
Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisition-tracking-pointing system (ATP), the primary traditional maneuvering targets are ballistic target, large aircraft and other big targets. Those targets have the features of fast velocity and a strong regular trajectory and Kalman Filtering and polynomial fitting have good effects when they are used to track those targets. In recent years, the small unmanned aerial vehicles developed rapidly for they are small, nimble and simple operation. The small unmanned aerial vehicles have strong maneuverability in the observation system of ATP although they are close-in, slow and small targets. Moreover, those vehicles are under the manual operation, therefore, the acceleration of them changes greatly and they move erratically. So the prediction and tracking precision is low when traditional algorithms are used to track the maneuvering fly of those targets, such as speeding up, turning, climbing and so on. The interacting multiple model algorithm (imm) use multiple models to match target real movement trajectory, there are interactions between each model. The imm algorithm can switch model based on a Markov chain to adapt to the change of target movement trajectory, so it is suitable to solve the prediction and tracking problems of the small unmanned aerial vehicles because of the better adaptability of irregular movement. This paper has set up model set of constant velocity model (CV), constant acceleration model (CA), constant turning model (CT) and current statistical model. And the results of simulating and analyzing the real movement trajectory data of the small unmanned aerial vehicles show that the prediction and tracking technology based on the interacting multiple model algorithm can get relatively lower tracking error and improve tracking precision com
Interacting multiple model (imm) algorithm is one of the effective methods for maneuvering target tracking. The conventional imm algorithm based on a fixed model set may lead to filter mismatch for a high maneuvering ...
详细信息
ISBN:
(纸本)9781509048281
Interacting multiple model (imm) algorithm is one of the effective methods for maneuvering target tracking. The conventional imm algorithm based on a fixed model set may lead to filter mismatch for a high maneuvering target. In this paper, an improved imm algorithm is proposed by using maneuver-adaptive model set. Based on the maneuver detection and adaptively adjusting the process noise levels, it can effectively make a quick response when the maneuver occurs while it has a low root-mean-square error when the motion is relatively stable. Finally, some numerical results are provided to demonstrate the effectiveness of the proposed method.
This paper presents sensor and data rate control algorithms for tracking maneuvering targets. The manuevering target is modeled as a jump Markov linear system. We present novel extensions of the Interacting Multiple M...
详细信息
This paper presents sensor and data rate control algorithms for tracking maneuvering targets. The manuevering target is modeled as a jump Markov linear system. We present novel extensions of the Interacting Multiple Model (imm), Particle filter tacker, and Probabilistic Data Association (PDA) algorithms to handle sensor and data rate control. Numerical studies illustrate the performance of these sensor and data rate control algorithms.
The commonly used root-mean-square error for estimation performance evaluation is easily dominated by large error terms. So many new alternative absolute metrics have been provided in X. R. Li's work. However, eac...
详细信息
The commonly used root-mean-square error for estimation performance evaluation is easily dominated by large error terms. So many new alternative absolute metrics have been provided in X. R. Li's work. However, each of these metrics only reflects one narrow aspect of estimation performance, respectively. A comprehensive measure, error spectrum, was presented aggregating all these incomprehensive measures. However, when being applied to dynamic systems, this measure will have three dimensions over the total time span, which is not intuitive and difficult to be analysed. To overcome its drawbacks, a new metric, dynamic error spectrum (DES), is proposed in this study to extend the error spectrum measure to dynamic systems. Three forms under different application backgrounds are given, one of which is balanced taking into account both good and bad behaviour of an estimator and so can provide more impartial evaluation results. It can be applied to a variety of dynamic systems directly. Then the challenge in performance evaluation of the interacting multiple model (imm) algorithm is considered, and the imm algorithm is chosen as the testing case to illustrate the superiority of the DES metric. The simulation results validate its utility and effectiveness.
The wind as a natural phenomenon would cause the derivation of the pesticide drops during the operation of agricultural unmanned aerial vehicles(UAV).In particular,the changeable wind makes it difficult for the precis...
详细信息
The wind as a natural phenomenon would cause the derivation of the pesticide drops during the operation of agricultural unmanned aerial vehicles(UAV).In particular,the changeable wind makes it difficult for the precision *** accurate spraying of pesticide,it is necessary to estimate the real-time wind parameters to provide the correction reference for the UAV *** estimation algorithms are model based,and as such,serious errors can arise when the models fail to properly fit the physical wind *** address this problem,a robust estimation model is proposed in this *** the diversity of the wind,three elemental time-related Markov models with carefully designed parameterαare adopted in the interacting multiple model(imm)algorithm,to accomplish the estimation of the wind ***,the estimation accuracy is dependent as well on the filtering *** that regard,the sparse grid quadrature Kalman filter(SGQKF)is employed to comprise the computation load and high filtering ***,the proposed algorithm is ran using simulation tests which results demonstrate its effectiveness and superiority in tracking the wind change.
In target tracking, most tracking algorithms are model based, and serious errors can arise when the models fail to fit the physical target motions, especially during coordinated turns. To address this problem, a two-s...
详细信息
In target tracking, most tracking algorithms are model based, and serious errors can arise when the models fail to fit the physical target motions, especially during coordinated turns. To address this problem, a two-step tracking algorithm is proposed. First, considering the diversity of the target manoeuvres, five elemental Singer models with carefully designed parameter alpha interact with each other in an interacting multiple model (imm) algorithm to accomplish the tracking and estimate the target's kinematic parameters. Second, if a turn motion occurs, then it discriminates the turn motion into a horizontal turn or a three-dimensional (3D) turn;and the real-time turn rate is calculated to refine the corresponding model for tracking. Since the filtering stage is important in estimating the kinematic parameters, sparse-grid quadrature Kalman filter is employed to improve the filtering capability. The proposed algorithm is exemplified by simulation tests and data tests in 3D, and is compared with that of an imm algorithm utilising constant velocity, constant acceleration and 3D coordinated turn models. All of the test results demonstrate that the proposed algorithm is effective and has higher accuracy.
An Interacting Multiple Model (imm) algorithm for manoeuvring target tracking in the presence of standoff jammer is proposed. In the imm, the conventional Gaussian likelihood is replaced with a Gaussian sum (GS) likel...
详细信息
An Interacting Multiple Model (imm) algorithm for manoeuvring target tracking in the presence of standoff jammer is proposed. In the imm, the conventional Gaussian likelihood is replaced with a Gaussian sum (GS) likelihood, derived from a sensor model accounting for both the measurements and jamming information. Thus, the model-conditioned posterior probability density function of the state is also a weighted sum of Gaussians but with recalculated weights. As a result of the combination of multiple models and jamming information, the proposed approach has a significant performance improvement when both manoeuvre and jamming occur. Simulation results show that the proposed approach outperforms the original imm algorithm as well as the GS filters with only one model in terms of track loss and track accuracy.
Recent years have witnessed the shift of wireless sensor networks (WSNs) from theoretical research to practical applications. Due to their simplicity, range-based sensor networks have been widely used. To track a mane...
详细信息
Recent years have witnessed the shift of wireless sensor networks (WSNs) from theoretical research to practical applications. Due to their simplicity, range-based sensor networks have been widely used. To track a maneuvering target in range-based sensor networks, first, we derive the relationship between the multiple model posterior Cramer-Rao lower bound (PCRLB) and the distance from the sensor to the target, which forms the basis of choosing the subset of candidate sensors that may attend the incoming tracking event. Second, we design two optimization strategies under the communication constraint, namely the optimal sensor selection and cluster head selection. Third, we can estimate the state of the maneuvering target by making use of the interacting multiple model (imm) algorithm and predict the model index one time step ahead. Last, simulation results show the effectiveness of the proposed schemes. (C) 2012 Elsevier B.V. All rights reserved.
This study proposes a new multiple model estimation algorithm. Although the derivation of the proposed algorithm is different from the existing interacting multiple model (imm), both algorithms are exactly the same if...
详细信息
This study proposes a new multiple model estimation algorithm. Although the derivation of the proposed algorithm is different from the existing interacting multiple model (imm), both algorithms are exactly the same if the dynamic system is linear. However, for non-linear systems, it appears that the proposed algorithm is different from the imm algorithm. The proposed algorithm is applied to the tracking problem of re-entry vehicles that is known as highly non-linear system. The estimation performance of the proposed algorithm is compared with imm by a series of simulation runs. The result shows that the proposed algorithm improves the estimation performance.
暂无评论