Spacecraft Telemetry System From The Tracking Mode And Tracking Algorithm

Single Channel Monopulse (SCM) radar is a classical module in radar field. It is used to track the moving target. Nowadays, the tracking technology is applied not only in the military field commercial but also in the civil field widely. The dissertation is accomplished in this background.In this paper, I introduce the theories of SCM, then, show the structure of the whole ADAS (Automatic Data Acquisition System) receiver system. Focused on the tracking signal processing flat, I discuss the data acquisition subsystem, the servocontrol system and the struction of the whole system application program. Base on these theories, I introduce how to improve the application programes including the following aspects. Strengthen the universal serial port communication program. Design and accomplis the simulator of handspike control pattern to improve the operating performance of the application program. Achieve the servocontrol's serial port scanner program to improve the debugging efficiency.I also introduce how to test and debug the whole system, then, discussing the scheme for strengthening the system's function to realize analog image transmission depending on the primary channel condition.In this paper, I focus on the Kalman Filter arithmetic in order to apply it to the tacking system. I chose the UKF(Unscented Kalman Filter) arithmetic as a main research direction after compared the strongpoints and the demerits about the classic Kalman Filter, EKF(Extended Kalman Filter) and UKF. Developing a new algorithm named MAUKF(Memory Attenuated Unscented Kalman Filter) by adding a fading factor to the UKF algorithm after accept the main optimizing ideas of some STF(strong tracking filter) arithmetics .First, I prove the superiority of MAUKF from the theories briefly, then, in the simulation, the same contrail of a linear moving object at regular speed in EKF and UKF and MAUKF is emulated respectively. By comparison, proving the MAUKF is faster in convergence rate and more accurate and robust in the nonlinear system with the white Gaussian noise.