scholarly journals Optimal Position and Velocity Estimation for Multi-USV Positioning Systems with Range Measurements

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Chen ◽  
Ruisheng Sun ◽  
Weisheng Yan
Keyword(s):  
State Estimation ◽  
Local Level ◽  
Problem Formulation ◽  
Minimum Variance ◽  
Velocity Estimation ◽  
Discrete Time System ◽  
Optimal Position ◽  
Positioning Systems ◽  
3 Dimensional ◽  
Level Plane

This paper investigates the problem on simultaneously estimating the velocity and position of the target for range-based multi-USV positioning systems. According to the range measurement and kinematics model of the target, we formulate this problem in a mixed linear/nonlinear discrete-time system. In this system, the input and state represent the velocity and position of the target, respectively. We divide the system into two components and propose a three-step minimum variance unbiased simultaneous input and state estimation (SISE) algorithm. First, we estimate the velocity in the local level plane and predict the corresponding position. Then, we estimate the velocity in the heave direction. Finally, we estimate the 3-dimensional (3D) velocity and position. We establish the unbiased conditions of the input and state estimation for the MLBL system. Simulation results illustrate the effectiveness of the problem formulation and demonstrate the performance of the proposed algorithm.

State Estimation Using Randomly Delayed Measurements

1993 ◽  
Vol 115 (1) ◽  
pp. 19-26 ◽  
Author(s):  
A. Ray ◽  
L. W. Liou ◽  
J. H. Shen
Keyword(s):  
State Estimation ◽  
Minimum Variance ◽  
The State ◽  
Sensor Data ◽  
State Estimator ◽  
Uniform Asymptotic Stability ◽  
Simulation Experiments ◽  
State Estimate ◽  
Sampling Instant ◽  
Recursive Relations

This paper presents a modification of the conventional minimum variance state estimator to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal. In this approach, the currently available sensor data is used at each sampling instant to obtain the state estimate which, in turn, can be used to generate the control signal. Recursive relations for the filter dynamics have been derived, and the conditions for uniform asymptotic stability of the filter have been conjectured. Results of simulation experiments using a flight dynamic model of advanced aircraft are presented for performance evaluation of the state estimation filter.

Computational Techniques in Optimal State-Estimation—A Tutorial Review

1979 ◽  
Vol 101 (2) ◽  
pp. 99-107 ◽  
Author(s):  
Willi Kortu¨m
Keyword(s):  
State Estimation ◽  
Large Scale ◽  
Estimation Error ◽  
Minimum Variance ◽  
Estimation Problem ◽  
Computational Techniques ◽  
Quadratic Loss ◽  
Real Process ◽  
Optimal State Estimation ◽  
Discrete Estimation

The objective of this tutorial presentation is to review the main computational techniques of the state-estimation problem for linearizable dynamic systems where the design is oriented toward a minimum variance (quadratic loss, gaussian) estimation error. It almost goes without saying that the viewpoints taken and the guidelines given in this paper should not be understood as firm recipes but rather as problem-dependent, sometimes subjective and experience-dependent, recommendations. We treat both the continuous and the discrete estimation problem: the first because it is usually closer to the real process and thus allows more direct physical insight; and the latter because it is often preferred for computation and for realization on large scale or special-purpose digital computers.

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