A GPS inspired Terrain Referenced Navigation algorithm

Abstract

Terrain Referenced Navigation (TRN) refers to a form of localization in which measurements of distances to the terrain surface are matched with a digital elevation map allowing a vehicle to estimate its own position within the map. The main goal of this dissertation is to improve TRN performance through better signal processing. More specifically, the project aims to explore opportunities in the field of TRN by using digital signal processing techniques that were originally developed for the acquisition and tracking of GPS signals. A typical TRN system uses speed, heading and time to establish the relative horizontal position between subsequent elevation measurements. Thus, any error in speed, heading or time will cause an error in the resulting relative position. If the speed or heading error contains a bias, this will cause a gradual reduction in the correlation. To prevent that a reduction in correlation causes the estimated position to drift away, the idea behind the research described in this thesis is the use of arrays of terrain elevation measurements with intentional (positive and negative) offsets in speed and heading in a tracking-loop configuration. It is well known that such a concept works well for optimized signals such as the ones used in GPS. To further explore the viability of this idea for a signal defined by a series of terrain elevation measurements, an analysis of similarities and differences with the GPS signal is performed. In accordance to the GPS receiver approach, a novel correlation algorithm for TRN is proposed and implemented. The basic rationale for the algorithm is to use terrain correlation to “acquire and track” the speed and heading of the host vehicle, while the position advances are calculated using these estimates together with the previously determined position. The novelty of the approach consists in the implementation of a tracking scheme based on the DLL concept. To answer feasibility-related questions, the algorithm is first evaluated in a purely theoretical framework. Based on this analysis it is concluded that the concept seems feasible and promising, but additional considerations in the design are required to compensate for the differences between the GPS and TRN signals. Enhancements are brought to the initial design resulting in the development of an adaptive tracking scheme, in which the tracking loops are configured based on an analysis of the terrain signal. Next, an in-depth sensitivity analysis is carried out to understand how sensor measurement errors (in speed, heading and terrain height) impact the algorithm performance. The analysis is performed using exclusively simulated data. It is shown that sensitivity to speed and heading errors is dependent on terrain features and it is possible to assess the degree of sensitivity by analysing the terrain signal. By combining this information with the expected error characteristic of the navigation sensors, the performance of the algorithm can be predicted. The sensitivity to terrain measurement errors depends on the ratio between the terrain signal strength and the measurement errors. It is shown that this ratio can be predicted up to a certain extent and a method to improve the ratio is proposed and discussed. The developed capabilities are validated with recorded sensor data from flight tests. Two different types of recorded sensor data are used: radar and lidar based datasets.