Receiver Differential Code Bias Estimation under Disturbed Ionosphere Status Using Linear Planar Model Based Minimum Standard Deviation Searching Method with Bias Detection

Differential code biases (DCBs) are important parameters that must be estimated accurately for precise positioning and Satellite Based Augmentation Systems (SBAS) ionospheric related parameter generation. In this paper, in order to solve the performance degradation problem of the traditional minimum STD searching algorithm in disturbed ionosphere status and in geomagnetic low latitudes, we propose a linear planar based minimum STD searching algorithm. Firstly, we demonstrate the linear planar trend of the local vertical TEC and introduce the linear planar model based minimum standard variance searching method. Secondly, we validate the correctness of our proposed method through theoretical analysis and propose bias detection to avoid large estimation bias. At last, we show the performance of our proposed method under different geomagnetic latitudes, different seasons and different ionosphere status. The experimental results show that for the traditional minimum STD searching algorithm based on constant model, latitude difference is the key factor affecting the performance of DCB estimation. The DCB estimation performance in geomagnetic mid latitudes is the best, followed by the high latitudes and the worst is for the low latitudes. While the algorithm proposed in this paper can effectively solve the performance degradation problem of DCB estimation in geomagnetic low latitudes by using the linear planar model which is with a higher degree of freedom to model the local ionosphere characteristics and design dJ to screen the epochs. Through the analysis of the DCB estimation results of a large number of stations, it can be found that the probability of large estimation deviation of the traditional method will increase obviously under the disturb ionosphere conditions, but the algorithm we proposed can effectively control the amplitude of the maximum deviation and alleviate the probability of large estimation deviation in disturb ionosphere status. key words: differential code bias, linear planar fit, ionospheric disturbed status, estimation bias detection