Research On INS/GPS Deep Integrated Navigation Algorithm Based On A Loop Kalman Filter

An INS/GPS deep integrated system uses INS to couple with GPS signal process loop, which can improve the precision of both the systems and improve the adaptability of GPS as well. Performance of GPS on a dynamic carrier and under a weak signal environment can be raised using deep integration. This dissertation studies on a series of algorithms of deep integration based on GPS. The validity and performance of the algorithms are tested by simulation. Some novel methods are also brought forward to improve the capability of algorithms or increase of efficiency of simulation.Firstly, the design of GPS track loop based on a kalman filter and a states feedback control is analyzed. The mathematical expression of coherent integration is deduced to build the measurement model. A kalman filter is adopted to estimate the frequency and frequency shift and a states feedback is used to control the VCO to generate local signal, which may reduce the thermal noise in PLL.After the study on classical tracking loop, a semi-deep integration method which uses loop kalman filters to get measurement and uses INS to aid on GPS loop is discussed. The frequency of loop kalman filter and the INS aid patterns are analyzed and the validity is tested by simulation.This dissertation also emphasizes the deep integration method which cuts off the classical tracking loop. In the new method, PLL/FLL/DLL is cut off and a vector tracking loop based on INS and kalman filter is presented. Measurement model and noise are discussed and the performance is proved.Finally, a deep integrated simulation system for highly dynamic signal is designed. The dynamic signal generator based on transmit time and the fast tracking loop based on characteristic value of signal are studied to improve the efficiency of simulation. The simulation shows validity of the algorithms and the dynamic performance of the navigation system is evidently improved.