Yanling Hao

Bio: Yanling Hao is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Inertial navigation system & Kalman filter. The author has an hindex of 11, co-authored 57 publications receiving 399 citations.

Comparison of Unscented Kalman Filters

Abstract: Unscented Kalman filter (UKF) has been proven to be a superior alternative to the extended Kalman filter (EKF) when solving the nonlinear system in previous literatures. In order to accelerate the application of UKF in the actual system, a new simplified UKF is proposed in this paper. It is called Rao-Blackwellised additive unscented Kalman filter (RBAUKF), and is specially designed for the dynamic system with the additive noise, the nonlinear state equation and the linear measurement equation. Furthermore, three kinds of UKF are introduced at the same time for the purpose of comparing their advantage and disadvantage. The three filters are general UKF, additive unscented Kalman filter (AUKF), and Rao-Blackwellised unscented Kalman filter (RBUKF). In fact, the AUKF and RBUKF are the improved filters of the general UKF, and RBAUKF proposed in this paper is the upgraded version, which synthesizes the feature of AUKF and RBUKF. Finally, the simulation and analysis of the above UKF algorithms are done. The simulation results indicate that the computational complexity of RBAUKF is nearly half of UKF. The computational complexities of AUKF and RBUKF are in between UKF and RBAUKF. Moreover, the estimation accuracies of RBAUKF, AUKF and RBUKF are the same, while that of UKF is lower than theirs. It suggests that the performance of RBAUKF is best following by AUKF and RBUKF, and it is better than the general UKF.

Real-Time Obstacle Avoidance Method based on Polar Coordination Particle Swarm Optimization in Dynamic Environment

Abstract: Based on the polar coordination particle swarm optimization (PPSO), this paper presents a novel method for the robot path planning in dynamic environment. It decomposes the task into a global planning stage and a local planning stage. PPSO algorithm can search for the global optimal path based on static obstacles information. When the robot moves along the optimal global path, an on-line real-time path planning strategy is adopted to avoid dynamic obstacles by means of predicting the future positions of moving obstacles. Simulation experiment shows that the method is more efficient than traditional particle swarm optimization (TPSO) and genetic algorithm (GA) for solving path planning problem. The feasibility and high stability of real-time obstacle avoidance strategy are demonstrated in dynamic environment.

Rapid transfer alignment based on unscented Kalman filter

Abstract: This paper presents a nonlinear error model based on the quaternion for the rapid transfer alignment of the inertial navigation system (INS). It allows the large initial misalignment uncertainty. Then, the unscented Kalman filter (UKF) is designed to achieve the nonlinear filtering based on the proposed model, and utilizes the difference in velocity and attitude between the slave and master INS as the measurement variables. This paper analyzes and compares the misalignment estimation error and convergence rate of the proposed algorithm with the rapid alignment prototype (RAP) and the velocity-only matching algorithm. The results of simulation suggest that the proposed algorithm could achieve the same alignment performance, not limiting the initial attitude error, as the rapid alignment prototype to do when the misalignment is small. The convergence rate of the azimuth misalignment using the proposed algorithm is rapider than using the velocity matching algorithm for large heading uncertainty.

An affine invariant salient region detector

Abstract: In this paper we describe a novel technique for detecting salient regions in an image. The detector is a generalization to affine invariance of the method introduced by Kadir and Brady [10]. The detector deems a region salient if it exhibits unpredictability in both its attributes and its spatial scale.