## 1 Introduction  
With the advent of semiconductor technology and the associated development of embedded controls and sensing technologies, agricultural equipment manufacturers have focused on reducing the role of the human operator in the control loop. Satellite-based localization approaches have become matured, and numerous applications in agriculture and forestry in developed countries have taken advantage of the centimeter-level accuracy and precision of global navigation satellite systems (GNSS) receivers. GNSS receivers are a key element in precision agriculture technologies (e.g., precision planting systems) and autonomous agricultural vehicles (e.g., system that integrate inertial sensors with GNSS capability for vehicle automation) because position information is a prerequisite for site-specific crop management [1; 2]. State-of-the-art robotic fleet management is advancing modern precision agriculture towards the goal of the complete automation of agricultural and forestry processes [3]. To guarantee the achievement of high mission standards, autonomous vehicle control is combined with complementary human operator interaction. The main requirement of this interaction is to enable a human operator to configure, monitor, detect and diagnose potential and existing problems and faults in the system and to resolve them so that the system can proceed with the mission execution [4]. However, intelligent agricultural machinery also requires sophisticated hardware and software to adapt to changing environments and accomplish demanding tasks in a safe and efficient manner. Two navigation approaches have become essential for intelligent vehicles systems: combining local information with global localization to enhance autonomous navigation and integrating inertial system information with real-time kinematic (RTK)-GNSS data for vehicle automation [2; 5]. Innovative robotic research and applications have enabled robots to act in direct support of and interact with a human partner. As a result, humans and robots are coupled in time and space during the execution of agricultural operations. This type of joint collaboration requires accurate positioning mechanisms to preserve human safety and maintain mission efficiency. Therefore, this paper focuses on providing high-end localization techniques for human operator and intelligent off-road vehicles in a precision agriculture scenario. Providing an accurate system would require the human operator to carry a large system containing a special antenna, an external GNSS receiver and a battery, which could be difficult to handle and would increase the cost of the system. Thus, our goal is to create a low-cost solution. The former approach is common and well known in outdoor applications, e.g., in various types of robot navigation [6]. The latter approach provides localization capability in indoor wireless-supported environments [7]. Because the robots in our agricultural scenario are equipped with high-quality wireless routers, it is feasible to use network information for positioning purposes. Due to the poor distance estimates produced by the use of radio signals for measuring the distance between nodes [8], we focus on the objective of providing a hybrid localization solution that combines distance estimates and RTK localization of an autonomous tractor in our scenario. Therefore, we introduce a hybrid localization approach that has two components: (1) a localization algorithm based on the received signal strength indication (RSSI) from the wireless environment and (2) the acquisition of autonomous tractor RTK coordinates when the human operator is nearby [9]. However, this hybrid approach has not been conceived to be a standalone positioning solution; instead, it has been planned as a positioning mechanism to complement the existing GNSS receiver. The remainder of this paper is structured as follows. Section 2 describes the materials and methods used to localize the human operator and intelligent off-road vehicle in the relevant scenario.