Monte-Carlo Simulation of Cooperative Localization Techniques for Inter-Vehicle Distance Estimation

This paper presents a simulation study of non-ranging-based cooperative positioning algorithms, including absolute position differencing (APD), single-differencing (SD), single-differencing with a single satellite (SD-SS), and double-differencing (DD), for estimating the Inter-Vehicle Distance (IVD) of two static autonomous vehicles. To this end, a simplified scenario with two autonomous vehicles separated by 5.2 meters and receiving 99 epochs of Global Navigation Satellite System (GNSS) observables (also called the pseudorange) from four GPS satellites are considered. Then, a Monte-Carlo simulation is performed to investigate the performance of the APD, SD, SD-SS, and DD algorithms with different levels of pseudorange uncertainties, which are assumed to be uncorrelated and have zero means (i.e., no bias). The simulation results demonstrated that there is no significant difference between SD-based and DD-based approaches when four satellites are employed. Indeed, the systematic effects affecting the pseudorange measurements appear to be cancelled out. This is somehow expected since every satellite system suffers from different systematic measurement uncertainties. The results also indicate that the DD-based technique has a lower average IVD estimation error than the SD-SS algorithm since it can eliminate pseudorange uncertainties and any other common biases, implying that using the DD-based algorithm with multiple satellite systems may result in higher accuracy in the IVD estimation problem.