Optimization of Satellite Combination in Kinematic Positioning Mode with the Aid of Genetic Algorithm

Abstract

Optimization of Satellite Combination in Kinematic Positioning Mode with the Aid of Genetic AlgorithmThe basis of high precision relative positioning is the use of carrier phase measurements. Data differencing techniques are one of the keys to achieving high precision positioning results as they can significantly reduce a variety of errors or biases in the observations and models. Since GPS observations are usually contaminated by many errors such as the atmospheric biases, the receiver clock bias, the satellite clock bias, and so on, it is impossible to model all systematic errors in the functional model. Although the data differencing techniques are widely used for constructing the functional model, some un-modeled systematic biases still remain in the GPS observations following such differencing. Another key to achieving high precision positioning results is to fix the initial carrier phase ambiguities to their theoretical integer values. To obtain a high percentage of successful ambiguity-fixed rates, noisy GPS satellites have to be identified and removed from the data processing step. This paper introduces a new method using genetic algorithm (GA) to optimize the best combination of GPS satellites which yields the highest number of successful ambiguity-fixed solutions in kinematic positioning mode. The results indicate that the use of GA can produce higher number of ambiguity-fixed solutions than the standard data processing technique.

Use of genetic algorithm and sliding windows for optimising ambiguity fixing rate in GPS kinematic positioning mode

“…As shown in Table 4, the window width is 1 h and start epoch is at 06:00:00 GPST and the result of the sliding window was extracted from the last epoch. In this study the GA related configuration and objective function will remain unchanged from previous paper (Srinuandee et al, 2012).…”

“…Combination of sliding window and GA in GPS kinematic positioning mode Srinuandee et al (2012) introduced the methodology of using GA in GPS post-processing kinematic mode as shown in Fig. 3 where the combination of satellites will be optimised by GA and used for the whole data processing.…”

“…In a previous study, instead of the process of trial and error to identify and remove noisy data including unhealthy satellites from the computation applied by the user, the use of a Genetic Algorithm (GA) to optimise the selection of best combination of GPS satellites which can improve the number of ambiguity-fixed solution comparing to the standard computation in GPS post processing kinematic (PPK) mode has been successfully presented (Srinuandee et al, 2012). However, since the data used in the demonstration were from static observations, where the base station and the roving station were set up stationary, the flag that indicated the positioning mode in raw observation data was then changed to kinematic positioning (for roving station) in the pre-computation process.…”

“…Operating procedure of evolution step of standard genetic algorithm 3 Operating procedure of optimised genetic algorithm with PPK from previous work(Srinuandee et al, 2012) 4 Operating procedure of combination between sliding window and GA in GPS kinematic positioning mode Srinuandee and Satirapod Use of genetic algorithm and sliding windows Survey Review…”