Research On Post-processing Technology Of High Refresh Rate And Ultra-short Baseline Data

Ultra-short baseline positioning system,capable of providing technical support for high-precision underwater operations,such as underwater target positioning and navigation,plays an increasingly important and prominent role in marine science,defense industry and other fields.The research on ultra-short baseline positioning systems focuses mainly on the validity of the delay difference and slope distance information which,due to the complexity of the underwater environment,are often disturbed by outliers,thereby increasing the positioning of the system error.Therefore,it is necessary to take effective measures to detect and correct outliers of delay difference and slope distance data.Besides,in order to ensure that sampling points for underwater targets are sufficient,the transmission period of the signal needs to be reduced to obtain a high data refresh rate,but the problem of distance blur will inevitably arise when the target distance is greater than the maximum non-blurred distance,thus contributing to resolving the distance.Particularly,a fuzzy problem serves as an important part in the research on the high refresh rate ultra-short baseline positioning system to achieve underwater positioning.To address the problems of outliers and skew data in ultra-short baseline systems,an outlier detection and correction algorithm are investigated in this paper based on adaptive Kalman filtering.Particularly,attenuation memory filtering method is adopted in this algorithm which combines innovation-based outlier detection method with adaptive model for adaptive noise estimation to make the outlier detection more reliable,along with the employment of a strong tracking filter algorithm to increase the robustness of the system.In the outlier correction,the forward and reverse filtering method,instead of the forward Kalman filter,is used to correct the speckle outlier.Finally,the RTS fixed interval smoothing algorithm is employed to improve the outlier correction accuracy.The results show that the adaptive anti-outlier algorithm,compared with the Kalman filter of the CV model,can help detect and correct outliers more effectively.To address the problem of distance ambiguity in the high refresh rate ultra-short baseline positioning system,an anti-distance ambiguity method suitable for ultra-short baselines is investigated in this paper,and a depth measuring instrument is conducted according to thefeature that the directional measurement has nothing to do with distance ambiguity.Depth view measurement,the slope distance calculated by the directional view measurement and depth view measurement,is employed as the rough distance value which is used as the reference distance of the "initial value binding method" each time,thus solving the "initial value binding method",making it difficult to obtain a priori initial value.Finally,in view of the fact that the above-mentioned anti-distance fuzzy method is no longer applicable to large base array opening angles,the fact that the algorithm can solve large base array opening and detection while outlier detection and correction is verified by simulation in this paper through combining with adaptive Kalman filtering algorithm.This helps address the problem of blurring the ultra-short baseline distance under the corner.The field algorithm is employed to verify the algorithm studied in this paper.The results of sea trial data processing show that outliers can be detected and the outlier rate in the data can be reduced effectively with the adaptive anti-outlier algorithm based on Kalman filtering.The results of lake test data processing demonstrate that the anti-distance blur method studied in this paper can address the problem of ultra-short baseline distance ambiguity at large base array opening angles,and can reduce the short baseline positioning error more effectively compared with the positioning results calculated by the rough value of the slope distance.