Navigation Analysis of Multi-Source Opportunity Signals Based on Kalman Filtering Method

Vehicles often encounter situations in which some satellites cannot provide effective positioning in complex environments, and at this time it is particularly necessary to utilize widely available opportunity signals for autonomous positioning and navigation. In this paper, the problem is modeled and analyzed of using multi-source opportunity signals to realize positioning and navigation. First, this paper analyzes different types of opportunity signals and establishes a geometric model for fusion positioning of multi-source opportunity signals. Then, a set of opportunity signals of the aircraft is imported and processed by the Kalman filter state estimation model. The method successfully predicts the position and velocity (motion trajectory) of the vehicle within 0–10 seconds by iterative estimation of the state and observation equations. Then, the Kalman filter state estimation model was further optimized by filtering the deviation data using a sequence smoothing moving average algorithm and establishing a noise assessment model using the sample mean and standard deviation to quantify the stochastic deviation and constant value drift. This optimization method greatly improves the accuracy of navigation and positioning and provides strong support for the safe and efficient operation of aircraft in complex environments.