## 1 Introduction
The w-test method is often used to identify faults by checking whether the corresponding statistic follows the standard normal distribution or not [14]. RAIM (Receiver Autonomous Integrity Monitoring) which also makes the same binary hypothesis judgment as a w-test is extended to detect simultaneous multiple faults in GNSS/INS integrated systems [15]. The multiple solution separation (MSS) algorithm, which consists of a main estimator using all the available GPS (Global Positioning System) measurements and sub-estimators using different subsets of the GPS measurements, identifies faults by comparing the main solution with the different sub-solutions [16]. For deep integration, few researchers focus on the identification of GNSS fault channels. In [17], the pseudo-range and pseudo-range rate residuals outputted by each receiving channel are adopted as the monitoring objects to detect whether the channel is abnormal. In [18], a sub-filter based on Kalman filtering algorithm is proposed to be added to each receiving channel to identify faults. The above methods may be effective when the receiving channels are independent of each other. However, the situation is different in vector tracking loop. Due to the fault propagation problem, all receiving channels will be abnormal, resulting in the inaccurate identification of fault source. Thus, from the above analysis, it can be concluded that the inaccurate identification caused by fault propagation is lack of research, and there is no effective solution so far. After analyzing the fault propagation path in deep integration, we find that maintaining correct navigation solution is the key to prevent fault propagation. The robust algorithm that can reduce the adverse effect of fault information on navigation solution is utilized in this study. It is derived by applying the maximum likelihood estimation to resist the outlier effect of measurement vector, and its function is realized by adjusting the covariance matrix of measurement noise with expansion factors [19]. The Huber weight function that is commonly used outputs the reciprocal of normalized residual as an expansion factor when the residual value is larger than the preset threshold [20; 21]. However, its performance is restricted by fixed parameter value and limited parameter range. The IGGIII (Institute of Geodesy and Geophysics III) scheme is another frequently used method to generate expansion factors [22; 23], but it has more parameters to configure, and has the same problem as Huber. In [24], the fuzzy inference algorithm is proposed to determine the value of expansion factor. By comparing the difference between the theoretical covariance and the actual covariance of residuals, the expansion factor is adjusted adaptively. Although fuzzy inference rules can be acquired from prior knowledge or experience, the exact membership function is difficult to be defined. Thus, the output of the fuzzy inference algorithm is not guaranteed to be the optimal value. The neural network is famous for its strong self-learning ability and complex system mapping ability, and it has the potential to generate expansion factors. CNN (convolutional neural network) is a kind of deep neural network, and has achieved great success in the field of image processing [25; 26]. Moreover, because of the attractive qualities, it has been gradually applied in the field of speech recognition [27], behavior recognition [28], and fault mode recognition [29; 30]. Compared with the traditional multilayer perceptual neural network (MLP), CNN can deal with more complex classification problems for its deeper network structure. In the neural network, the number of neurons is greatly reduced by the pooling operation, and the number of parameters which are needed to be trained is also reduced by sharing weights. Thus, CNN has the better generalization ability and the lower network complexity [31].