Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm

Xie, Guo; Sun, Lanlan; Wen, Tao; Hei, Xinhong; Qian, Fucai

doi:10.1109/tsmc.2019.2922305

Cited by 91 publications

(47 citation statements)

References 18 publications

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“…The effectiveness of the proposed results has been illustrated by the simulation example. There remain several issues to be investigated, for example, how to apply the methods to MASs with interactive multi-model [46] and complex input nonlinearities.…”

Section: Resultsmentioning

confidence: 99%

Observer-based adaptive consensus control for nonlinear multi-agent systems with time-delay

Xiao

Cao

et al. 2020

Sci. China Inf. Sci.

114

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In this paper, we consider the observer-based adaptive consensus tracking problem for a class of nonlinear time-delay multi-agent systems in the presence of input saturation. Under the assumption that the communication topology is directed and connected, a distributed adaptive consensus controller is developed based on the dynamic surface control technique. By constructing the nonlinear observer, the unmeasurable agents dynamics can be estimated. Input saturation problem is solved by a smooth function combined with an auxiliary variable. With the help of prescribed performance functions, the synchronization errors converge to the prescribed sets, which are characterized as a neighborhood of zero. According to Lyapunov stability theory, it is shown that with the proposed distributed consensus tracking approach, the consensus errors are cooperatively semi-globally uniformly ultimately bounded. Finally, a simulation example is provided to show the effectiveness of the proposed algorithm.

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Section: Resultsmentioning

confidence: 99%

Observer-based adaptive consensus control for nonlinear multi-agent systems with time-delay

Xiao

Cao

et al. 2020

Sci. China Inf. Sci.

114

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show abstract

“…In addition it can avoid spending time finding proper PID values of gains. The researches on adaptive control also draw much attention [45]- [47]. However, uncertainties in model dynamics are ubiquitous [48], [49] and have attracted attention of researchers [50]- [54].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Human Impedance and Motion Intention for Human–Robot Collaboration

et al. 2021

IEEE Trans. Cybern.

104

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“…This approach overcomes the problem of peak position error that appears in the maneuver-detector algorithms [5], [6]. Several algorithms have been proposed in this approach such as the first order generalized pseudo-Bayesian (GPB1), the second order of GPB (GPB2), and interacting multiple model (IMM) [7]- [11]. The IMM algorithm has a slightly higher complexity, but a better performance compared to that of GPB1 algorithm and significantly lower complexity than GPB2 with a comparable tracking performance [12].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Turn Rate Estimation for Tracking a Maneuvering Target

2020

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Tracking maneuvering targets accurately is one of the most challenging tasks in the design of aircraft tracking systems. For efficient tracking performance, the target motion predicted by the target motion model needs to match the target's actual motion during the maneuver. For tracking a maneuvering target, a combination of the constant velocity (CV) model and the coordinated turn (CT) model with a known turn rate are incorporated in the interacting multiple model (IMM) algorithm. However, in such a scheme when a target performs an unexpected maneuver, the tracking performance deteriorates, or the scheme may even fail to track the target. To overcome this problem, there exists a scheme in the literature, in which instead of using an a priori knowledge of the target turn rate, it is estimated adaptively using the target acceleration and speed. However, this algorithm uses a three-dimensional model to estimate the turn rate in two-dimensional space, which may result in an inaccurate estimation of the target acceleration, and thus may lead to in inaccurate turn rate value. In this paper, an adaptive algorithm to track a maneuvering target in an IMM framework is proposed. Estimating the turn rate is based on the speed of the target and the radius of the turn, where the latter is computed by a simple method using the previous three successive measurements. Further, a detailed study to select an appropriate transition probability matrix for the proposed algorithm is carried out. Simulation results demonstrate that the proposed tracking algorithm outperforms the other algorithms in terms of its tracking accuracy and consistency, particularly in the realistic situation when neither an a priori knowledge about the target turn rate nor about the range rate information is available to the tracking algorithm. INDEX TERMS Interacting multiple model for radar tracking, target tracking, target turn rate estimation, maneuvering targets.

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Adaptive Transition Probability Matrix-Based Parallel IMM Algorithm

Cited by 91 publications

References 18 publications

Observer-based adaptive consensus control for nonlinear multi-agent systems with time-delay

Observer-based adaptive consensus control for nonlinear multi-agent systems with time-delay

Bayesian Estimation of Human Impedance and Motion Intention for Human–Robot Collaboration

An Adaptive Turn Rate Estimation for Tracking a Maneuvering Target

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