Moving Horizon Estimation for Cooperative Localisation with Communication Delay

Abstract

Cooperative Localisation (CL) technology is required in some situations for Multiple Unmanned Underwater Vehicle (MUUVs) missions. During the CL process, the Relative Localisation Information (RLI) of the master UUV is transmitted to slave UUVs via acoustic communication. In the underwater environment, the RLI is subject to a random time delay. Considering the time delay characteristic of the RLI during the acoustic communication, a Moving Horizon Estimation (MHE) method with a Delayed Extended Kalman Filter (DEKF)-based arrival cost update law is presented in this paper to obtain an accurate and reliable estimation of present location. Additionally, an effective computation method for the MHE method is employed, in which the "Lower Upper" (LU) factorization is used to compute the solution of the Karush-Kuhn-Tucker (KKT) system. At the end of this paper, simulation results are presented to prove the superiority and practicality of the proposed MHE algorithm. K E Y WO R D S 1. Multiple Unmanned Underwater Vehicles (MUUVs).

Adaptive Sliding Mode Control for Depth Trajectory Tracking of Remotely Operated Vehicle with Thruster Nonlinearity

“…It is assumed that the approximation error of the neural network is unknown and bounded, and the upper bound is known (Gao et al, 2015c; Yang and Wang, 2007), thus the stability of the closed-loop system can be ensured by using the sliding mode strategy to compensate. Although this hypothesis is tenable, the suitable upper bound value is difficult to select.…”

“…The precise synchronisation operation for various degrees of freedom is difficult to achieve, so the ROV controller is essentially an open-loop control system. The operations of ROV are complex and difficult (Gao et al, 2015c), but if automatic depth control can be achieved during the operation, then manual operation is only for the motion in the horizontal plane and underwater operations will become relatively easy (Bessa et al, 2008; Gao et al, 2015a). Therefore the study of depth tracking control for ROV has a certain practical significance.…”

Sensor placement and moving horizon state/parameter estimation for flexible hypersonic vehicles

“…20,21 In MHE, the estimation problem is reformulated as a constrained quadratic optimization problem that utilizes a moving horizon of measurements. 22 The moving horizon strategy not only improves the stability and accuracy of MHE, 23 but also reduces the effects of incorrect initialization and propagation of the prior probability density. 24 Another advantage of MHE is that unknown parameters can be estimated together with the states by naturally taking the parameters as the optimization problem's additional degrees of freedom.…”

“…27 Traditionally, the arrival cost is interpreted from the perspective of probability and is computed by various kinds of Kalman-filter-based schemes. 22,26 However, it is difficult for these approaches to make full use of the MHE's estimation results.…”

A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization

“…A filtering technique based on a state-space model is an effective way to complete this task, which can achieve statistically optimal state estimates [7]- [9]. A large number of nonlinear filters for cooperative localization of AUVs have been proposed, such as the extended Kalman filter [4], unscented Kalman filter (UKF) [10], and moving horizon estimation algorithm [6], [11]. In practical cooperative localization, outlier measurements of velocity and range may occur, which can induce heavy-tailed non-Gaussian process and measurement noises respectively.…”