# Performance Evaluation of GPS Auto-Surveying Techniques
## 1 Introduction
For the position estimation, four positioning methods were chosen: Least Squares (LS), Weighted Least Squares (WLS), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The first two are based on the Least Squares method of data fitting and attempt to obtain the best position solution possible for a set of observations in a given instant, with no regards for previous observations. The other two methods are based on the theory of the Kalman Filter and provide a way to incorporate previous measurements in the current position estimate, as well as including extra information regarding the receiver's dynamics. Besides these methods, a code-smoothing algorithm, the Hatch Filter, was also implemented in order to use carrier phase measurements to improve the pseudorange measurements. Finally, a position threshold filter based on the standard deviation of the estimates was implemented. Figure 1: Overview of the DGNSS positioning method. The novelty of this paper is that it not only attempts to fill the gap of a complete comparison between these methods using modern low-cost receivers, it also intends to demonstrate the viability of these methods for DGNSS/RTK base station auto-survey using only the measurements available with low-cost single-frequency receivers capable of raw data output By characterizing and benchmarking the time and precision obtained using short-term surveys for the base station position in a completely autonomous fashion, it aims to provide information regarding the trade-off between the survey time and precision of the position estimate, as well as analyzing its impact on the positioning accuracy of a rover receiver. The low-cost, fully-autonomous approach has applicability in several areas where the required position estimate precision is greater than what is obtainable with a single, low-cost receiver but cannot justify the added expense of a high-performance receiver, as well as temporary applications that, by their nature, require a quick survey period for the base station. Such applications can, for example, include the following:  
* Non-professional or associative sporting events, such as orienteering or nautical sports;
* Forestry applications that do not necessitate permanent base stations for monitoring and registry activities;
* Use of drones in temporary or remote activities such as power line monitoring or the monitoring of forest or crop areas;
* Agricultural applications for more precise crop dusting and monitoring using airplanes and helicopters, where the the expense of a full precision-agriculture suite of machinery is prohibitive for the owners;
* Other temporary activities that are performed in correction-denied areas that do not require centimetric precision. While PPP provides better precision for the problem of single-receiver positioning, it also requires access to external data that must be obtained either a priori or during the survey; in comparison, our methodology is totally autonomous, and this paper aims to show it is a viable alternative. In addition, the proposed approach allows the usage of very low-cost processors for the positioning algorithm, since the pseudorange-only approach is computationally much simpler due to not having to solve the integer ambiguity problem, as well as including computationally simple smoothing and filtering methods. As a result, the proposed methodology leads the way to lower entry costs for precise position estimation in remote areas with no connectivity or where the required communication systems are expensive. It is worthy of note that, while this paper worked only with GPS measurements, its methodology can be easily applied to other GNSS systems, which should improve the position estimate accuracy [11; 12]. Figure 2: Overview of the RTK positioning method.