Đang chuẩn bị liên kết để tải về tài liệu:
handbook of multisensor data fusion phần 5

Không đóng trình duyệt đến khi xuất hiện nút TẢI XUỐNG Tải xuống

hiện nay nhấn mạnh đến đặc tính của cảnh thay đổi động theo dõi, cũng như khả năng thời gian phát triển quá trình phân tích có thể liên quan đến sự tương tác giữa một mạng lưới của các quá trình phản ứng tổng hợp phân phối. | FIGURE 10.1 Detection likelihood function for a sensor at 70 0 . measurements are linear and hopefully the measurement errors are Gaussian. In combining all this information into one tracker the approximations and the use of disparate coordinate systems become more problematic and dubious. In contrast the use of likelihood functions to incorporate all this information and any other information that can be put into the form of a likelihood function is quite straightforward no matter how disparate the sensors or their measurement spaces. Section 10.2.4.1 provides a simple example of this process involving a line of bearing measurement and a detection. 10.2.4.1 Line of Bearing Plus Detection Likelihood Functions Suppose that there is a sensor located in the plane at 70 0 and that it has produced a detection. For this sensor the probability of detection is a function Pd r of the range r from the sensor. Take the case of an underwater sensor such as an array of acoustic hydrophones and a situation where the propagation conditions produce convergence zones of high detection performance that alternate with ranges of poor detection performance. The observation measurement in this case is Y 1 for detection and 0 for no detection. The likelihood function for detection is Ld 1 x Pd r x where r x is the range from the state x to the sensor. Figure 10.1 shows the likelihood function for this observation. Suppose that in addition to the detection there is a bearing measurement of 135 degrees measured counter-clockwise from the x1 axis with a Gaussian measurement error having mean 0 and standard deviation 15 degrees. Figure 10.2 shows the likelihood function for this observation. Notice that although the measurement error is Gaussian in bearing it does not produce a Gaussian likelihood function on the target state space. Furthermore this likelihood function would integrate to infinity over the whole state space. The information from these two likelihood functions is combined by .