Research And Realization Of Acquisition And Tracking For Weak GPS Signals

GPS (Global Positioning System) is the only system of the world, which is under full operation. Its Standard Positioning Service (SPS) is widely applied in mapping, vehicle navigation and other location-based service. For its high precision and good stabilization, no other system can take its place. However, as a satellite-based navigation system, GPS also has its inherent limitation. Because GPS is a kind of passive positioning system, and receivers only do receiving and send nothing, the signal is very weak. When there are no shelters, GPS receivers can acquire signals from 4 or more satellites using the nice functioned C/A code. But if the environment is not very good, for example, indoor, under crossroads, in forests, normal receivers cannot acquire enough satellites. And they're not able to supply positioning service. So how to process when signal is very weak is a big and important problem. Research on weak GPS signal processing, which is very difficult, plays an important role in GPS theory researching.The contents of this thesis are mainly focus on GPS signal processing including acquisition and tracking. First, a circuit of acquisition is designed. A piece of development board called DE2 with FPGA chip produced by Altera is used. This circuit can realize the conventional acquisition algorithm and it also has nice real-time performance. This realized module lays solid foundation of subsequent work and provides an important module.The emphasis of this thesis is weak GPS signal tracking. All algorithms in this thesis are based on Kalman filtering theory. And different Kalman filter models are used to replace the traditional tracking loops including PLL and DLL to obtain higher sensitivity and precision. This thesis proposes two kind algorithms. First, a square-root filter with Potter algorithm is implemented to alleviate round-off errors. And square process is called after accumulation to remove the unknown navigation bit. At last this algorithm introduces incoherent integration to strengthen the signal and restrain the noise effectively. The second algorithm uses another form of square-root Kalman filter, and improves the filter model with a dual Kalman filter, which reduces computational cost with high efficiency. This algorithm introduces Beyesian estimation theory to deal with the navigation bit of every accumulation period. This thesis uses a kind of software to generate GPS IF signals to make simulations. And the simulation results indicates that the first algorithm can track signals as weak as 17dB-Hz but the precision is not ideal and it costs a little more time. The second algorithm can track signal as weak as 19dB-Hz. And its precision is high than the former and the converging time is less. Based on these two algorithms, the problem of weak GPS signals processing can be well solved.