An Integrated Adaptive Kalman Filter for High-Speed UAVs

Abstract

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H∞ filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H∞ filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms.

Improving Robot Localization Using Doppler-Based Variable Sensor Covariance Calculation

“…Figure 2 shows the basic localization diagram based on a Kalman filter [1,2]. The localization module receives sensors and covariances as inputs, and gives a localization estimation as the output.…”

“…One of the most used fusion techniques is the Kalman filter [1,2] and its variants. A Kalman filter is a statistical method that fuses information from sensors based on their accuracy.…”

Noise‐aware manoeuvring target tracking algorithm in wireless sensor networks by a novel adaptive cubature Kalman filter

“…The inaccurate noise statistics will lead to a large moving target position deviation and even divergence estimated by the KF [14]. The NSE, which can estimate noise statistics in real‐time, has been developed [16, 34]. Together with the KF, it forms the AKF.…”

Special Issue on Unmanned Aerial Vehicles (UAVs)

“…There are eight papers focused on UAV control technology [3][4][5][6][7][8][9][10]. S. Sato, H. Yokoyama, and A. Lida present plasma actuators were introduced to simplify the complex feathering motion and to control the flow around insect's flapping wings having simplified sinusoidal motion [3].…”

“…S. Sato, H. Yokoyama, and A. Lida present plasma actuators were introduced to simplify the complex feathering motion and to control the flow around insect's flapping wings having simplified sinusoidal motion [3]. T. Huang, H. Jiang, Z. Zou, L. Ye, and K. Song propose an integrated robust adaptive Kalman filter, called the strong adaptive Kalman filter (SAKF), to solve the divergence and low accuracy issues of the existing Kalman filter (KF) for the high-speed UAV application [4]. S. Yeom and I. Cho present an interacting multiple model (IMM) filtering based empirical method to detect and track moving pedestrians using a small UAV (SUAV) in which the detection algorithm is consisted of frame subtraction, thresholding, morphological filter, and false alarm reduction processes and the tracking algorithm uses the output from the detection algorithm processes [5].…”