Improvement of RSS-Based Measurement Based on Adaptive Kalman Filter Considering the Anisotropy on Antenna in Dynamic Environment

Abstract

The classic propagation model of received signal strength (RSS) measurement is significantly affected by the environment. This paper divides the error sources of RSS ranging into three parts and deals with them in different methods. First, considering the influence of UAV pose on RSS, the compensation model is established through Gaussian Process Regression (GPR) for the first time by mapping the quaternion of the UAV and the attenuation caused by the influence of anisotropy on the antenna in the corresponding the special orthogonal group <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SO</i> (3). On this basis, the distribution of random compensation variables is derived by unscented transformation (UT) and covariance propagation. Besides, a polynomial function is employed for fitting the distance-dependent environment noise. Then, to deal with the RSS measurement instability caused by the environment noise and the uncertainty of compensation, the adaptive Kalman filter (AKF) is introduced. Finally, an UWB-based environmental adaptive algorithm (UEAA) is proposed to improve ranging precision by using the UWB device in a dynamic environment. By setting the threshold parameter, the UEAA enables the classic RSS ranging model to have a certain adaptive ability for the dynamic environment of UAV, which significantly reduces the error caused by model parameter mismatch. Several experiments are carried out on the UAV platform, and the results show that the improvement of the RSS propagation model is adequate.