Multi-sensor fusion is the key technology in many modern applications, such as intelligent unmanned systems, Internet of Things (IoT), robots, intelligent transportation, and intelligent manufacturing, etc. These systems have a common feature: they have multiple sensors of the same or different types. The state estimation method is one of the basic methods in the multisensor state, which gives the true value of the measured variable based on the sensor measurement data. Various techniques based on estimation methods have a wide range of applications, such as detection, positioning and tracking techniques, nonlinear filtering, navigation, etc. These methods have always been the key technologies in multisensor information fusion.

Multi-sensor information fusion technology can effectively process a limited amount of sensor data and even obtain the best results in real-time. Due to the tremendous development of sensors and computer storage technology, more sensors have been used in existing systems in recent years. A large amount of measurement data has been recorded and restored. The development of sensor systems has brought many new challenges to multisensor information fusion theory and its applications. For example, in the unmanned vehicle system, the positioning and tracking technology can effectively improve the vehicle body's understanding of the position and improve the vehicle body's self-control decision-making, which is of great significance to the performance of the unmanned vehicle. We must consider how to process large amounts of sensor data in a real-time model and control multisensor systems based on large amounts of data. Therefore, it is still necessary to further research the possible innovations of information fusion.

The aim of this Special Issue is to report the innovative ideas and solutions of multisensor information fusion methods. We welcome submissions discussing tracking technologies based on state estimation theory. Moreover, submissions focusing on development, adoption, and application are also encouraged.

Potential topics include but are not limited to the following:

• Estimation in complex multi-sensor systems
• Location in complex multi-sensor systems
• Tracking in complex multi-sensor systems
• Control of multi-sensor systems
• Modelling of big data from multi-sensor systems
• Artificial intelligence technology for multi-sensor fusion
• The structure and/or levels of multi-sensor systems
• Application of multi-sensor systems (e.g., Internet of Things, robots, transportation systems, intelligent manufacturing, etc.)