With the expansion of the exploration scope of underwater vehicles and the improvement of their safety requirements, integrated navigation under passive conditions has become a hot topic in the field of navigation. Due to the stability and detectability of the Earth’s gravity field, the use of a matching result for external correction of inertial navigation systems (INSs) can be effective. For practical navigation applications, an inertial gravity matching integrated navigation algorithm must be designed for real-time applicability, diversified output information capability, and anti-interference capability. In this paper, an INS/gravity matching (INS-GM) integrated navigation algorithm is improved to extract the information of gravity fields in real time and correct the navigation results, and a robust integrated algorithm is proposed. Through two-hierarchy filtering, correction of the gravity field to the carrier position and of the integrated navigation system is obtained. According to the first-hierarchy filtering noise characteristics of gravity matching, the robustness of the algorithm to a complex environment is improved by second-hierarchy filtering. The ocean experiment data processing results show that the algorithm can effectively handle a certain degree of initial error, and an integrated navigation accuracy of 205 m is achieved in a good experimental environment. In the case of severe test conditions, the accuracy of the robust integrated navigation algorithm is 72% higher than that of the ordinary navigation algorithm, and it has a strong anti-jamming ability, which can meet the requirements of marine inertial gravity matching integrated navigation tasks.

In March 2015, a ground vehicle gravimetry test was implemented in eastern Changsha to assess the repeatability and accuracy of ground vehicle SINS/GNSS gravimeter—SGA-WZ02. The gravity system developed by NUDT consisted of a Strapdown Inertial Navigation System (SINS), a Global Navigation Satellite System (GNSS) remote station on test vehicle, a GNSS static master station on the ground, and a data logging subsystem. A south-north profile of 35 km along the highway in eastern Changsha was chosen and four repeated available measure lines were obtained. The average speed of a vehicle is 40 km/h. To assess the external ground gravity disturbances, precise ground gravity data was built by CG-5 precise gravimeter as the reference. Under relative smooth conditions, internal accuracy among repeated lines shows an average agreement at the level of 1.86 mGal for half wavelengths about 1.1 km, and 1.22 mGal for 1.7 km. The root-mean-square (RMS) of difference between calculated gravity data and reference data is about 2.27 mGal/1.1 km, and 1.74 mGal/1.7 km. Not all of the noises caused by vehicle itself and experiments environments were eliminated in the primary results. By means of selecting reasonable filters and improving the GNSS observation conditions, further developments in ground vehicle gravimetry are promising.

The use of Global Navigation Satellite System (GNSS) data for land vehicle gravimetry tests is challenged by complicated environments. A new approach for land vehicle gravimetry using a Strapdown Inertial Navigation System and velometer-integrated navigation computation (SINS/VEL) without using GNSS information has been put forward. Aided by the velometer with continuous longitudinal velocity output instead of GNSS signals, a SGA-WZ02 strapdown gravimeter that used the SINS/VEL method was tested in 2015. Four repeated lines were measured along a south-north direction highway in Eastern Changsha to verify the new method’s feasibility and performance. The gravity disturbance results showed an internal accuracy in scalar gravimetry about 1.17 mGal and 1.91 mGal for external accuracy assessment, with a spatial resolution of 1.7 km. Comparing this new method with the traditional SINS/GNSS gravimetry approach, it appeared that the results using SINS/VEL showed comparable internal and external accuracy. Theoretical analysis and practical test results showed that the new method was feasible for gravity determination by land dynamic vehicle.

Due to the errors of the accelerometer and gyroscope, the position and the velocity calculated by the inertial navigation system (INS) are divergent with time. We can normally estimate and compensate the errors of the INS using the underwater sensors. However, the underwater sensors such as ultrashort baseline (USBL) acoustic positioning system and Doppler velocity log (DVL) are challenged by the complex environment, which affects the reliability and the accuracy of data processing. From the gravimetry error equation, the horizontal position errors have little influence on the underwater gravimetry. Observability analysis indicates that the velocity error and the position error can be well estimated on the condition of only positional observations in the Kalman filter. Based on these, we proposed a novel method using the INS and depth gauge (DG) based on trajectory constraint without USBL and DVL. In this method, we considered to fit the track of the gravimeter to a straight line because the gravimeter maintains uniform motion in the measuring line and take the fit line and the depth measured by the DG as the observations to correct the errors of the INS. We constructed an underwater experiment in 2019 to test the novel method. The test results showed a repeatability from 1.24 to 2.03 mGal for the space resolution of 200 m. The new method can acquire a good accuracy and can be applied to the gravimetry data processing.

Underwater gravity information plays a major role in deepwater oil and gas exploration. To realize underwater dynamic gravimetry, we have developed a strapdown gravimeter mounted in a pressure capsule for adaption to the underwater environment and we adopted a two-stage towed underwater gravimetry scheme. An improved strapdown gravimeter and other underwater sensors were installed in a towed vessel to form an underwater dynamic gravimetry system. Because the global navigation satellite system cannot be used for underwater dynamic gravimetry, we developed a new method based on underwater multisensor integrated navigation, in which a federal Kalman filter was applied for error estimation. This new method allowed us to obtain the accurate attitude, velocity, and position necessary for gravity estimation. In addition, the gravity data can then be extracted from the noisy data through finite impulse response low-pass filtering. We acquired the underwater gravity data at a depth of 300 m to test the validity of the new method and evaluate the accuracy of the underwater gravity system. The results indicated a repeatability from 0.85 to 0.96 mGal at a half wavelength of approximately 0.2 km and also indicated good consistency with the marine gravity data.