Multisensor data fusion using Kalman filters based on neurofuzzy linearisation

Abstract

The previous chapter discussed nonlinear state estimation based on neurofuzzy linearisation models, where available measurements are from a single set of measurements. One of the most important state estimation applications is in target tracking, where nonlinearity and uncertainty are inherently severe. The main purpose of a target tracking system is to obtain a target trajectory, which is more accurate than the direct trajectory observation, and to predict the target behaviour in the near future. In many target tracking problems, in order to obtain more accurate and robust tracking performance, a dynamic target is often detected by multiple disparate sensors, each with different measurement dynamics and noise characteristics. For example, in ship collision avoidance guidance and control, as shown in Figure 9.1, each ship has on-board sensors to measure the positions of itself and other ships or obstacles, and there is a vessel traffic control centre that can also measure ship positions and communicate with each ship. For a particular target (ship or obstacle), there are plenty of associated sensor measurements at different levels and with different accuracy and reliability. Various data fusion problems naturally arise such as how can the multisensor measurements be combined to obtain a joint estimate of the target state vector, which is superior to the individual sensor based estimates and how can the target information be shared robustly from the various data sources (or locations)?