Research On New Algorithms And Implementation Of SINS/GPS Integrated Navigation System

The integrated navigation technique is becoming the main solution for navigation system by taking advantages of navigation subsystems, dealing with their information synthetically, and promoting the whole system performance. However, the integrated navigation system has the inherent drawbacks, such as dependence and fault tolerance capacity is not enough, relying on the satellites excessively. What's more, it is usually demanded in practical application that the navigation system should dynamicly achieve initial alignment and navigation synchronously to startup quickly. But there are strong nonlinearity of SINS(Strap-down inertial navigation system) error model when it initially startup, and some unpredictable harmful jamming of complex environment, and the noise model is uncertain, so the conventional filter method such as Kalman Filter usually becomes ineffectively. In view of these problems, this thesis mainly has done the following researchs:First, based on study of SINS principle, SINS, GPS and OD(Odometer) error model, an information fusion method base on Federal Kalman Filter are detailed presented for SINS/GPS/OD integrated navigation system. Computer simulation programs of SINS and integrated navigation system information fusion method are designed, and lots of simulations under different conditions are made. These simulation results show that this method utilizes the advantages of all the navigation devices, and has high fault-tolerant and credibility. During GPS signal loss in a long time, the integrated system can still get high precise position.Then, the traditional PF(Particle Filter), which can be used for nonlinear- nongauss system, is discussed in this thesis, two improved PF are presented afterward according the nonlinear model of SINS/GPS integrated navigation system: 1) Self-adjust Particle Filter. Its key principle is to change the likelihood distribution adaptively according to the statistical characteristic of the observation noises to increase the overlap area between the likelihood and prior distribution. Simulation results show this method can effectively improve the stability of the filter when the observation precision is high. 2) Kalman/Particle Filter. The new method divides the system into two sub-models: One is linear, and the other one is nonlinear. Then Kalman filter and particle filter are implemented separately. Simulation results show it can solve the costly computation of traditional Particle Filter since the error model of SINS has high dimensions. Improved PF can effectively enhance the anti-jamming capacity, stability, and real-time compute capacity of integrated navigation system.Finally, an entire navigation computer framework base on DSP+ DUAL-RAM +MCU for integrated navigation system is designed and implemented. The tasks of integrated navigation system and their priority are analyzed and partitioned. The hardware and soft design are both described in detail. The integrated navigation system was pass test by use YH-7000VG form YUNHAI Inc and JNS Gyro-2T GPS Receiver form JANAD Inc.