## 1 Introduction
The optical flow method [1] is a widely used method to determine this relationship, which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in an image. Researchers have done much useful work in this area [2; 3]. The disadvantage of the optical flow method is the large computational overhead. Moreover, it is difficult to find the correct optical flow mode when the robot is moving at a high speed. To overcome such a difficulty inherent in a visual sensor, other sensors have been introduced, for example, inertial measurement units (IMUs) and global positioning systems (GPSs), into the navigation system. GPSs are now widely used in cars [4]. In most cases, GPSs provide accuracy within approximately 10-30 m. For autonomous cars, however, this accuracy is insufficient. IMUs have become smaller, lower cost, and consume less power thanks to miniaturization technologies, such as micro-electro-mechanical systems or nano-electro-mechanical systems. Because the measurements of the IMUs have unknown drift, it is difficult to use the acceleration and orientation measurements, to obtain the current position of a pedestrian in an indoor navigation system [5]. Combined with video sensors, IMUs have been used in indoor and outdoor seamless navigation connection, such as in unmanned aerial vehicles (UAVs) [6]. Other sensors, such as Wi-Fi [7, 8], have been widely used in indoor navigation systems in recent years. Wi-Fi uses triangulation to locate a target, which can keep the indoor positioning error within tens of meters and effectively overcome the IMU position drift. Another sensor used for indoor positioning is ultra-wide-band (UWB) [9], which requires the user to place the receiver in the area of target activity. Considering these factors, it is clear that the navigation systems are complex. In the process of movement, a robot must first perceive the surrounding environment firstly, and then need to know the way and speed of movement of itself and a target. It also needs to judge the movement of a foreground target and predict whether it will collide with it. Fortunately, some progress has been made in research on autonomous vehicles. Unmanned vehicles driving on roads can identify road signs, traffic conditions, and so on. Robot navigation uses a tracking algorithm [10, 11, 12, 13] to achieve maneuvering target estimation. Based on the known characteristics of the system noise, the estimation method extracts the estimated trajectory of a target from an uncertain signal. To obtain good estimation results, two models are crucial, namely, the process and measurement models. The process model, which describes the motion characteristics of maneuvering targets has been a hot topic in the field of target tracking. Based on a variety of different motion characteristics and using [PERSON]'s law, many modeling methods for maneuvering models have been presented [14, 15, 16, 17] that capture the characteristics of most moving targets. However, at present, the research methods based on estimation still cannot deal with all the complicated situations in practical applications. The main reason is that it is difficult to obtain an accurate measurement model of a sensor used in different applications. For example, the drift of IMU data is still a subject that needs further study [18]. In visual tracking applications, the interference of light, sunlight, and flexible objects makes the target information obtained by the image-processing method less accurate and extremely computationally intensive [19, 20]. The fact that we want robots to behave like human beings, and because a robot's motion space and behaviors are increasingly complicated, brings great difficulties to the application of the traditional estimation-based tracking algorithms. We believe the reason that a robot system cannot cope with such a complex environment space and motion behavior, is that the information collected by sensors has a high degree of uncertainty because of the complexity of environment.