Research Of Kalman Filtering In GNSS Receiver

With the development of satellite navigation technology, satellite can basically reliaze all day, global location and continuously provided to the Earth’s surface for positioning information. Many researchers applied this technology to the aviation, transport, navigation, exploration and mapping and many other areas, satellite navigation is deep into the production and living among people. Because the widely use, study its content is very practical significance and value.Application of satellite navigation technology is mainly based on the background of the receiver. The technology is moving in highly dynamic, high-precision direction. But the practical application of the technology need to deal with often encounter various noises, some noise can be eliminate by fixed model, but some noise such as dynamic noise and satellite geometry errors, using conventional methods in this area is difficult to eliminate the error, these errors will cause deterioration of positioning accuracy. Kalman filter can create noise model for noise, this filtering method can eliminate all kinds of random noise encountered in the measurement process, In addition, Kalman filter just consider the relationship between last moment and the current time, greatly reducing the space required for data storage, the method can also be applied iteratively to deal with Kalman Filtering, can be better simplify the calculation process, so this method would be a good choice. Although the Kalman filter has many advantages, however, there are some obvious shortcomings, so in practical applications require some improvement. Such as, When the actual operation, There is a larger point error, But Kalman filter process itself can not determine whether or not to deal with the point, If handled this point will result in reducing the positioning accuracy. This paper adding the active monitoring method based on the original algorithm, By mixing the carrier phase and code phase data, Combined with receiver autonomous integrity monitoring algorithm extended security features and error detection elimination algorithm, To achieve the purpose of monitoring and troubleshooting erroneous measurements. Kalman filter measurements using pseudo range and Doppler, Pseudo range contains a large noise. And the value of the carrier phase measurement process has high accuracy. But solving the ambiguity on the phase of the carrier is a more complicated issue. So with carrier phase smoothing pseudo distance can enhance the accuracy of the measurement and avoid the calculation ambiguity, ultimately improve positioning accuracy. Kalman filter algorithm and improvements in the receiver is the main content of this article. First introduce the basic and related principles of receiver. Then describes some of the errors encountered during the calculation leads to the use of the Kalman filter algorithm causes. Finally, this paper shows how to realize the Kalman filter in the receiver.