Retarding Potential Analyzers (RPA) have a rich flight heritage. These instruments are largely popular since a single current-voltage (I-V) profile can provide in-situ measurements of ion temperature, velocity and composition. The estimation of parameters from an RPA I-V curve is affected by grid geometries and non-ideal biasing which have been studied in the past. In this dissertation, we explore the uncertainties associated with estimated ion parameters from an RPA in the presence of instrument noise. Simulated noisy I-V curves representative of those expected from a mid-inclination low Earth orbit are fitted with standard curve fitting techniques to reveal the degree of uncertainty and inter-dependence between expected errors, with varying levels of additive noise. The main motive is to provide experimenters working with RPA data with a measure of error scalable for different geometries. In subsequent work, we develop a statistics based bootstrap technique designed to mitigate the large inter-dependency between spacecraft potential and ion velocity errors, which were seen to be highly correlated when estimated using a standard algorithm. The new algorithm - BATFORD, acronym for "Bootstrap-based Algorithm with Two-stage Fit for Orbital RPA Data analysis" - was applied to a simulated dataset treated with noise from a laboratory calibration based realistic noise model, and also tested on real in-flight data from the C/NOFS mission. BATFORD outperforms a traditional algorithm in simulation and also provides realistic in-situ estimates from a section of a C/NOFS orbit when the satellite passed through a plasma bubble. The low signal-to-noise ratios (SNR) of measured I-Vs in these bubbles make autonomous parameter estimation notoriously difficult. We thus propose a method for robust autonomous analysis of RPA data that is reliable in low SNR environments, and is applicable for all RPA designs.