Constant turn model for statically fused converted measurement Kalman filters

Zhou, Gongjian; Wu, Ligang; Xie, Junhao; Deng, Weibo; Quan, Taifan

doi:10.1016/j.sigpro.2014.09.022

Cited by 27 publications

(7 citation statements)

References 32 publications

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“…If it is assumed that dynamic targets appear randomly in the environment, and they remain at a fixed water depth. That means that the AUV operates with constant angular velocity in a turning motion without considering the motion of the shaft [ 39 ]. In the rectangular coordinate system, the discrete time in the mathematical model of the moving object can be given in the form of Equations (6) and (7) respectively.…”

Section: Model Establishmentmentioning

confidence: 99%

An Adaptive Prediction Target Search Algorithm for Multi-AUVs in an Unknown 3D Environment

Zhang

et al. 2018

Sensors

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For a target search of autonomous underwater vehicles (AUVs) in a completely unknown three-dimensional (3D) underwater environment, a multi-AUV collaborative target search algorithm based on adaptive prediction is proposed in this paper. The environmental information sensed by the forward-looking sonar is used to judge the current state of view, and the AUV system uses this environmental information to perform the target search task. If there is no target in the field of view, the AUV system will judge whether all sub-regions of the current layer have been searched or not. The next sub-region for searching is determined by the evaluation function and the task assignment strategy. If there are targets in the field of view, the evaluation function and the estimation function of the adaptive predictive optimization algorithm is used to estimate the location of the unknown target. At the same time, the algorithm also can reduce the positioning error caused by the noise of the sonar sensor. In this paper, the simulation results show that the proposed algorithm can not only deal with static targets and random dynamic interference target search tasks, but it can also perform target search tasks under some random AUV failure conditions. In this process, the underwater communication limits are also considered. Finally, simulation experiments indicate the high efficiency and great adaptability of the proposed algorithm.

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Section: Model Establishmentmentioning

confidence: 99%

An Adaptive Prediction Target Search Algorithm for Multi-AUVs in an Unknown 3D Environment

Zhang

et al. 2018

Sensors

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“…In substituting these optimal designed gains into Equations (34)-(36), we obtain a Kalman filter having the same steady state performance. Similarly, solving Equations (34)-(36) with respect to (α, β, θ) and using a set Q gen , we find the steady state gains of the designed PVM Kalman filter and its performance using Equations (20), (26) and (28). Moreover, optimization of the proposed strategy of Equation (32) with respect to (α, β, θ) is equivalent to optimization of the RMS index with respect to (a, b, c) using Equations (34)-(36).…”

Section: Relationship With Steady State Pvm Kalman Filtersmentioning

confidence: 99%

Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

Saho

Masugi

2017

Applied Sciences

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Featured Application: Design and evaluation of monitoring systems in intelligent vehicles, robots, and so on.Abstract: We present a strategy for designing an α-β-η-θ filter, a fixed-gain moving-object tracking filter using position and velocity measurements. First, performance indices and stability conditions for the filter are analytically derived. Then, an optimal gain design strategy using these results is proposed and its relationship to the position-velocity-measured (PVM) Kalman filter is shown. Numerical analyses demonstrate the effectiveness of the proposed strategy, as well as a performance improvement over the traditional position-only-measured α-β filter. Moreover, we apply an α-β-η-θ filter designed using this strategy to ultra-wideband Doppler radar tracking in numerical simulations. We verify that the proposed strategy can easily design the gains for an α-β-η-θ filter based on the performance of the ultra-wideband Doppler radar and a rough approximation of the target's acceleration. Moreover, its effectiveness in predicting the steady state performance in designing the position-velocity-measured Kalman filter is also demonstrated.

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“…In order to rectify these flaws, a new method is proposed in [27][28][29] and summarized in this chapter. In the proposed method, the use of nonlinear filtering approaches is also avoided while dealing with the Doppler measurements.…”

Section: Introductionmentioning

confidence: 99%

Statically Fused Converted Measurement Kalman Filters

Zhou¹,

Guo²

2019

Introduction and Implementations of the Kalman Filter

Self Cite

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This chapter presents a state estimation method without using of nonlinear recursive filters when Doppler measurement is incorporated into the tracking system. The commonly used motions, such as the constant velocity (CV), constant acceleration (CA), and constant turn (CT), are represented in a pseudo-state space, defined from the product of target true range and range rate, by linear pseudo-state equations. Then the linear converted Doppler measurement Kalman filter (CDMKF) is presented to extract pseudo-state from the converted Doppler measurement, constructed by the product of the range and Doppler measurements. The output of the CDMKF is fused statically with that of the converted position measurement (range and one or two angles) Kalman filter (CPMKF) to produce target Cartesian state estimates. The accuracy and consistence of the estimator can be both guaranteed, since linear Kalman filters are both used to extract information from position and Doppler measurements.

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Constant turn model for statically fused converted measurement Kalman filters

Cited by 27 publications

References 32 publications

An Adaptive Prediction Target Search Algorithm for Multi-AUVs in an Unknown 3D Environment

An Adaptive Prediction Target Search Algorithm for Multi-AUVs in an Unknown 3D Environment

Performance Analysis and Design Strategy for a Second-Order, Fixed-Gain, Position-Velocity-Measured (α-β-η-θ) Tracking Filter

Statically Fused Converted Measurement Kalman Filters

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