Cooperative Navigation of UAVs in GNSS-Denied Area With Colored RSSI Measurements

Abstract

In this article, we investigate how the proposed navigation mode “dead reckoning + relative ranging + heading constraint” can be used to cooperatively localize a UAV swarm when GNSS (Global Navigation Satellite System) is denied. By focusing on the relative ranging performance, the wireless RF modules mounted on the cost-efficient UAVs are used to provide communication, ranging and positioning at the same time; however, its positioning accuracy is usually degraded by the noise in the actual RSSI (Received Signal Strength Indicator) measurements. To solve this problem, we analyze the influence of antenna pattern inhomogeneity and channel variation, respectively. The former mainly determines an antenna radiation function related to the yaw angle and relative position between the two measuring UAVs. The latter uses overlapping Allan variance to analyze and identify the measurement noises from outfield tests, that is, quantization noise, flicker noise, random walk noise and Gaussian white noise, which to some extent bridges the difference between the theoretical model and the practical measurement of RSSI. We also evaluate the sub-swarm size that guarantees robustness of RSS-based localization. In this way, an improved extended Kalman filter is proposed to predict and correct the colored noise by adaptively integrating the current peer-to-peer radio ranging performance and its Allan variance. To prove the effectiveness of this approach, simulation results based on real environment datasets and practical noise modeling are demonstrated.