Objective The underwater dynamic navigation based on the sectional observation system generates multi-source and heterogeneous data, creating crossed or forked tracks caused by asynchronous time delay and unknown system errors, which brings on difficulties to describe the continuous navigating process and identify the local characteristic points. Aiming at the problem, the algorithm of underwater data fusion processing by functional reconstruction is proposed.
Method the polynomial constraint fusion method (PCF) and spline function fusion method(SFF) are selected to deal with the track data base on the sectional observation, which integrate the entire underwater track and solved the problem of discontinuous dynamic parameter sequence and fuzzy data in overlapped section.
Results The numerical simulations show that both PCF and SFF could capture the main characteristics of underwater dynamic motion and obtain a reasonable and complete track. By comparing with the general data fusion method (GDF), the PCF and SFF provided a better data series in smoothness and continuity, which effectively described the motion in the coincident zone. Compared with the moving average filter algorithm, the fusion processing results based on the functional reconstruction algorithm and the filter algorithm both show an optimizing performance in accuracy and smoothness. When it comes to the consistency of the velocity and acceleration, the functional reconstruction algorithm is better than the filter algorithm. Verified by the sea test, the SFF and the PCF were used to obtain the re-analysis track in the observation section with the velocity accuracy superior to 5% at the characteristic points, and also obtain the predicted track in the subsequent section with the velocity accuracy superior to 15%.
Conclusion The proposed method shows application values for the multi-source and heterogeneous data processing in complex underwater motion, and is also useful for the short duration estimation of underwater navigation.
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