Multiple Model Algorithm for Single-Beacon Navigation of Autonomous Underwater Vehicle without Its A Priori Position. Part 2. Simulation

The algorithm presented earlier is updated for the case when an autonomous unmanned vehicle (AUV) is equipped with a strapdown inertial navigation system (SINS) based on angular rate sensors and a water speed log. The algorithm simulation results for a SINS of navigation grade, a two-component water speed log, two trajectories of the AUV motion when approaching the beacon to a distance of 100 m; two values of the AUV’s speed; solid and fragmented diagrams of received acoustic measurements are presented. The results show that the algorithm provides the AUV positioning accuracy of several meters even in the situations when the received acoustic measurements are discontinuous. It has been found that the accuracy characteristics provided by the algorithm adequately reflect the level of real errors.

Observability Degree-Based AUV Single Beacon Navigation Trajectory Optimization with Range-Only Measurements

Aiming at the problem of autonomous underwater vehicle navigation trajectory optimization using single beacon location under direct route condition, a nonlinear system model for AUV single beacon navigation is established, and the linearized system model with error states is constructed by polar coordinate transformation and simplification. Then, current disturbance is considered. To find out the optimum path to utilize range-only measurements, a novel observability degree-based analysis method is proposed, which gives a quantitative insight into convergence characteristics of the error states by using the eigenvalues of the normalized error covariance matrix. Simulation experiments are done to test convergence characteristics of AUV integrated navigation error states with single beacon range-only measurements under direct route control conditions. The experimental results show that the proposed control method is effective, and it has an important engineering application value and provides us with an optimized path.