Abstract
A novel integrated navigation algorithm, multi-model EKF (Extended Kalman Filter) integrated navigation algorithm, is presented in this paper for the deep water autonomous underwater vehicle. When a deep water vehicle is performing tasks in the deep sea, the navigation error will accumulate over time, if it relies solely on its own inertial navigation system. In order to get a more accurate position for the deep water vehicle online, an integrated navigation system is constructed by adding the acoustic navigation system. And because it is difficult to establish the kinematic model and the measurement model accurately for the deep water vehicle in the underwater environment, we propose the Multi-model EKF integrated navigation algorithm, and estimate the measurement errors of beacons online. Then we can estimate the position of the deep water vehicle more accurately. The new algorithm has been tested by both analyses and field experiment data (the lake and sea trial data), and results show that the multi-model EKF integrated navigation algorithm proposed in this paper significantly improves the navigation accuracy for the deep water vehicle.
Highlights
In the last decades, advances in marine technology have made it possible for people to explore the ocean
For the problems mentioned above, based on the multiple models algorithm, PDA (Probabilistic Data Association) theory [24, 25] and EKF algorithm, this paper proposes a novel navigation algorithm, the multi-model EKF integrat‐ ed navigation system, which will be described in detail latter
Here we provide a new unique aspect of choosing the models or fusing the results of different filters corresponding to different models
Summary
Advances in marine technology have made it possible for people to explore the ocean. There are other many factors, such as, the measurement transmission delays for the computation of the AUV position, the multi-paths, the nonlinear measurement equations, the non-constant sound speed in the explored zone, and the effect of water currents These factors are the reasons why the navigation accuracy decreases, and we cannot establish accurate models for all the factors, because of the complexity. Further assume that the vehicle is equipped with an inertial measurement unit (IMU), consisting of two triads of orthogonally mounted accelerometers and rate gyros or magnetic compasses, and an attitude and heading refer‐ ence system (AHRS) This integrated navigation system utilizes range of an AUV from a single acoustic beacon at a known location, along with velocity (heading and speed) of the AUV, to estimate the position of the AUV.
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