Aircraft telemetry data feature extraction and hierarchical classification method and device

Abstract

The invention discloses an aircraft telemetry data feature extraction and hierarchical classification method and device, and the method comprises the steps: carrying out the time sequence analysis ofregularized preprocessing data, and constructing a plurality of basic feature classes; constructing a high-order statistic of the sample, and extracting a feature dimension with the maximum weight invarious types of data as a category feature; identifying a first-level category, and marking a category identifier on the sample sequence; carrying out primary classification, carrying out secondary classification on the steady-state slowly-varying data by adopting a linear regression classifier, and carrying out secondary classification on the dynamic rapidly-varying data by adopting an autocorrelation characteristic analysis classifier to obtain a classification result. According to the method, feature extraction and effective classification of aircraft telemetry data can be realized; a classification result covers the main category of telemetry data behavior characteristics in the field of aircraft measurement and control, classifier parameters can be used as input conditions of a compression transmission bandwidth or other signal processing methods, and technical support can be provided for development of future deep space exploration tasks and spatial information networks in China.

Description

technical field [0001] The invention relates to the technical field of spatial information transmission, in particular to a method and device for feature extraction and classification of aircraft telemetry data. Background technique [0002] Ensuring the high reliability of telemetry data transmission links of deep space probes, manned spacecraft, earth orbit satellites and other on-orbit aircraft is the key to the success of spaceflight missions. Under the conditions of long transmission distance, limited transmission power, and dynamic channel state changes, the aircraft should reduce the transmission rate of telemetry data as much as possible to ensure the level margin of the link. [0003] The aircraft is usually composed of subsystems such as energy, thermal control, star affairs, attitude and orbit control, measurement and control, and communication. The number of subsystem equipment units is large, the mission functions are different, and there are certain differences...

AI Technical Summary

Problems solved by technology

Further in-depth analysis of the machine learning process of the above literature shows that the existing research has not classified and identified the behavioral characteristics of telemetry data, the statistical feature acquisition and trend prediction algorithms are not targeted, and the applicability and work efficiency of the research methods have certain limitations.

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