# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 1 Introduction
[PERSON] and [PERSON] [19] introduced the weighting factors to adjust the contribution of each local filter in the final data  
Figure 1: DVL malfunction circumstances. fusion for the SINS/GPS/CNS system. The weighting factor which is computed using fuzzy inference system will be close to zero in a failure. [PERSON] and [PERSON] [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) introduced the degree of confidence which indicates the level at which the local filter information fusion is dependent on its filter information after the measurement update. In the event of malfunction, the degree of confidence will be quite low, and accordingly, the influence of the filter information after measurement update will be very little. This kind of approaches is cost saving, but there is the potential risk of cross contamination. There are also other researchers working to find a replacement for the KF [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018); [PERSON] (2018), and various models have been built to predict the errors of the reference system. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) proposed an artificial-intelligence-based segmented forward predictor to overcome situations of GPS satellite signals blockage. By employing radial basis function neural networks, the predictor provides the INS position and velocity errors. [PERSON] et al. [PERSON] et al. (2018) introduced a genetic neuro-fuzzy system to predict the INS errors during the GPS failures. These intelligent algorithms can solve sensor failures without requiring any prior information about the characteristics of the sensors. In addition, this kind of method is attracting more and more attention. In this paper, a hybrid approach based on partial least squares regression (PLSR) and support vector regression (SVR) is proposed to deal with short-term DVL malfunctions for underwater integrated navigation systems. We aim to build a predictor which can estimate the measurements of the DVL when it malfunctions. The predictor is devoted to establish the relationship between the SINS outputs and the DVL outputs, taking into consideration the changing trend of the velocity, current and past calculating velocities of the SINS as the predictor's inputs. In order to improve the predictor robustness in the presence of intense relativity among independent variables, PLSR is employed to simulate the velocity measurements of the DVL. Considering that the PLSR model is linear, SVR is used to estimate the residual components of the PLSR prediction to improve the accuracy. When the DVL works well, the PLSR-SVR predictor is trained using the SINS calculating velocities as the inputs and the actual DVL velocity measurements as the outputs. When the DVL malfunctions, the invalid measurements of the DVL will be temporarily substituted by the prediction outputs of PLSR-SVR predictor. To confirm the validity of the proposed approach, comparative simulations and field tests are carried out. The results demonstrate that the PLSR-SVR hybrid predictor can correctly provide the estimated measurements for DVL and effectively extend the tolerance time on DVL malfunctions, which can greatly improve the accuracy and reliability of the underwater integrated navigation system. The contributions of this paper are briefly listed as follows. (1) In existing results, faulty sensors of integrated navigation systems have been popularly addressed. The most popular method which attempts to replace KF is based on connecting the SINS error to the corresponding SINS output. However, this would affect the accuracy of the navigation system. This paper does not aim to find a replacement for the KF, but to develop a measurement predictor for the faulty sensor to aid the KF. (2) The existing methods concentrate mostly on the dependence of the prediction on the SINS output at certain time instants, but the dependence on the past outputs of the SINS has not been taken into account.