State Observability through Prior Knowledge: Analysis of the Height Map Prior for Track Cycling

Inertial navigation systems suffer from unbounded errors in the position and orientation estimates. This drift can be corrected by applying prior knowledge, instead of using exteroceptive sensors. We want to show that the use of prior knowledge can yield full observability of the position and orientation. A previous study showed that track cyclers can be tracked drift-free with an IMU as the only sensor and the knowledge that the bike drives on the track. In this paper, we analyze the observability of the pose in the experiment we conducted. Furthermore, we improve the pose estimation of the previous study. The observability is analyzed by testing the weak observability criterion with a Jacobian rank test. The improved estimator is presented and evaluated on a dataset with three 60-round trials (10 km each). The average RMS is 1.08 m and the estimate is drift-free. The observability analysis reveals that the system can gain complete observability in the curves and observability of the orientation on the straight parts of the race track.

Point-actuated feedback control of multidimensional interfaces

We consider the application of feedback control strategies with point actuators to multidimensional evolving interfaces in order to stabilize desired states. We take a Kuramoto–Sivashinsky equation as a test case; this equation arises in the study of thin liquid films, exhibiting a wide range of dynamics in different parameter regimes, including unbounded growth and full spatiotemporal chaos. The controls correspond physically to mass-flux actuators located in the substrate on which the liquid film lies. In the case of partial state observability, we utilize a proportional control strategy where forcing at a point depends only on the local observation. We find that point-actuated controls may inhibit unbounded growth of a solution, if the actuators are sufficient in number and in strength, and can exponentially stabilize the desired state. We investigate actuator arrangements, and find that the equidistant case is the most favourable for control performance, with a large drop in effectiveness for poorly arranged actuators. Proportional controls are also used to synchronize two chaotic solutions. When the interface is fully observable, we construct model-based controls using the linearization of the governing equation. These improve on proportional controls and are applied to stabilize non-trivial steady and travelling wave solutions.

Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter

Underwater target tracking system can be kept covert using the bearing-only or the bearing-Doppler measurements (passive measurements), which will reduce the risk of been detected. According to the characteristics of underwater target tracking, the square root unscented Kalman filter (SRUKF) algorithm, which is based on the Bayesian theory, was applied to the underwater bearing-only and bearing-Doppler non-maneuverable target tracking problem. Aiming at the shortcomings of the unscented Kalman filter (UKF), the SRUKF uses the QR decomposition and the Cholesky factor updating, in order to avoid that the process noise covariance matrix loses its positive definiteness during the target tracking period. The SRUKF uses sigma sampling to avoid the linearization of the nonlinear bearing-only and the bearing-Doppler measurements. To ensure the target state observability in underwater target tracking, the paper uses single maneuvering observer to track the single non-maneuverable target. The simulation results show that the SRUKF has better tracking performance than the extended Kalman filter (EKF) and the UKF in tracking accuracy and stability, and the computational complexity of the SRUKF algorithm is low.