# Reducing GPS Error for Smart Collars Based on Animal's Behavior
## 1 Introduction  
Animal localization plays a vital role in monitoring and controlling animals in real-time using virtual fence applications [1]. Global Positioning System (GPS) devices obtain coordinates from internal GPS modules which monitor signals from satellites in near-earth orbit. GPS technology is designed to determine geoposition and has in recent times been extensively used in many Internet of Things (IoT) devices such as smart collars [2; 3]. However, the accuracy of the positioning of the single-frequency GPS module is not sufficient for safe IoT applications in agriculture, such as intelligent farming systems and virtual fencing systems, to avoid incorrect monitoring and convenient animal management. GPS receivers embedded in smart collars are commonly low-cost single-frequency modules, and theirnormal location error is around 10-15 m [4, 5]. These characteristics are sufficient for coarse navigation, but they are not accurate enough from a security point of view. In addition, in overcast weather and inside cites with urban canyons, the positioning characteristics deteriorate significantly. Many methods have been proposed to increase the GPS positioning accuracy. One commonly used method is relative positioning [6]. Methods such as relative positioning, including static, fast static, kinematic, pseudo-kinematic, and kinematic in real-time, have proven the ability to improve GPS accuracy [7, 8, 9]. [PERSON] in 1989 [10] and 1993 [11] carefully studied the principles of operation, the degree of internal error as well as the methods used to reduce errors associated with GPS. Since animals of medium and large size wear GPS collars, it seems to have little effect on their behavior [12]. However, the GPS technology offered a magnificent opportunity for recording animal movements in arbitrary zones [13, 14, 15], use of resources and their activities [16, 17]. One of the problems of studying the behavior of animals using GPS based smart collars, inherent inflationary impact of system errors in the movement of animals [18]. As a rule, GPS accuracy can be related to several aspects such as satellite geometry, atmospheric conditions, receiver or satellite errors, numerous road effects, satellite orbit errors [11], and topography, depending on the air tires or adjacent structures [19, 20]. The error of the GPS signals does not make exact distance [21]. The technology of virtual fencing has allowed fast modern approaches and demonstrated technical capabilities for goats [22]. Normally, virtual fencing systems for animals use a method which combines animal behavior with GPS to develop complete virtual fencing [22, 23, 24, 25]. Similar to physical fences, a virtual fence system provides a border for animals, but, unlike normal ones, they do not create physical barriers [26]. The perception and development of reducing GPS error in virtual fencing technology for goats are less advanced. Nevertheless, the potential of virtual fencing technology for goat grazing was demonstrated very well. A wide study is needed to correct GPS errors for improving performance of the virtual fence system for goats, determining the management of goats in their correct coordinates. This study utilizes research results from our virtual fence project for controlling and monitoring the behavior of goats [22]. While conducting our project study, we faced several problems related to goat GPS position error. For example, occasionally, we received wrong coordinate data from the GPS module and, as a result, test goats were given electronic or sound stimuli unnecessarily. In this study, we have purposed two objectives. First, to achieve goats' current behavior using Support Vector Machines (SVM) which contribute to the effectiveness of individual accelerometer units [22]; and second, to demonstrate GPS error correction based on achieved real-time goat behavior. The rest of this article is organized as follows. Section 2 summarizes information about the materials and methods of the proposed GPS error filter method.