UAV Position Estimation and Collision Avoidance Using the Extended Kalman Filter

Abstract

Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and/or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper tackles the problem of constant positioning and collision avoidance on UAVs in outdoor (wildness) search scenarios by using received signal strength (RSS) from the on-board communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in Least Square (LS) algorithms which are widely used in RSS based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter for distance estimation. Furthermore, the constantly changing path loss factor during UAV flight can also affect the accuracy of estimation. In order to overcome this challenge, we present an adaptive algorithm to estimate the path loss factor. Given the position and velocity information, if a collision is detected we further employ an orthogonal rule to adapt the UAV predefined trajectory. Theoretical results prove that such an algorithm can provide effective modification to satisfy the required performance. Experiments have confirmed the advantages of the proposed algorithms.

Characterization of Received Signal Strength Perturbations Using Allan Variance

“…In order to improve the accuracy of RSS based localization, Chandrasekaran et al exploited the correlations in positioning errors over time. Practical application of RSS measurements was demonstrated in [1], [26], which implemented positioning and collision avoidance algorithms in unmanned aerial vehicle networks.…”

A Communication Model to Decouple the Path Planning and Connectivity Optimization and Support Cooperative Sensing

“…A wide scope of practical applications require cooperation among a swarm of mobile robots, e.g., WiSaR [1], [4], [21] where a reliable and efficient communication network is essential to the success of the whole task [14], [22].…”

Optimal Maneuver Strategy of Observer for Bearing-Only Tracking in Threat Environment

“…In [15], the lower bound of the determinant of the FIM matrix was used as reward function, while the parameters evaluating distance from the observer to target were ignored due to its inaccuracy. The observer's trajectories in all those researches do not consider the constraints of the threat environment, while obstacle/threat avoidance is the pure control or decision methodology applied for an autonomous flight of UAV [16,17], which has little connection with BOT problem. Thus, it remains difficult to ensure that the observer's maneuvering trajectories satisfy the requirements of BOT and the threat avoidance at the same time.…”