Unmanned aerial vehicle flight data abnormity detecting method based on LSTM

A flight data and anomaly detection technology, applied in computer parts, instruments, characters and pattern recognition, etc., can solve the problems of complex functions, approximation ability cannot meet the requirements of fitting accuracy, abnormal detection of UAV flight data, etc. Good detection effect and accurate detection effect

Active Publication Date: 2018-12-07
HARBIN INST OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the functions generated during the working process of the UAV system are relatively complex in the abnormal detec

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  • Unmanned aerial vehicle flight data abnormity detecting method based on LSTM
  • Unmanned aerial vehicle flight data abnormity detecting method based on LSTM
  • Unmanned aerial vehicle flight data abnormity detecting method based on LSTM

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[0023] Reference figure 1 Specifically describe this embodiment, the LSTM-based UAV flight data abnormality detection method described in this embodiment,

[0024] Step 1: Phase space reconstruction of UAV telemetry data.

[0025] In order to adapt to the characteristics of the LSTM network structure, for the single-dimensional UAV telemetry data, the phase space reconstruction is first carried out, and the input vector and output vector are constructed as the training sample and test sample of the LSTM, as shown in Equation 1.

[0026]

[0027] Where X(t) is the input vector constructed from the single-dimensional time series, x(t) is the value of the single-dimensional UAV telemetry data in the form of time series at time t, and the phase space reconstruction window length is D, Y(t) is the true value corresponding to the output vector X(t). What the LSTM model needs to complete is the learning of the X(t)→Y(t) mapping relationship.

[0028] Before The input vectors and output ve...

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Abstract

An unmanned aerial vehicle flight data abnormity detecting method based on LSTM relates to the field of unmanned aerial vehicle detection and system health management. The method aims to settle problems of relatively complicated functions generated in a working process of an unmanned aerial vehicle system and incapability of satisfying a relatively high fitting precision requirement by the approaching capability in performing abnormity detection on flight data of the unmanned aerial vehicle. The method comprises the steps of reconstructing an unmanned aerial vehicle remote measurement data phase space, obtaining an input vector and an output vector, obtaining a training sample set and a testing sample set, building an LSTM basic predication model by means of a TensorFlow deep learning opensource framework and performing parameter optimization, obtaining an optimal LSTM model and furthermore calculating an LSTM predication result; afterwards performing abnormal point detection and abnormal sequence detection, and finally finishing unmanned aerial vehicle flight data abnormity detection.

Description

technical field [0001] The invention belongs to the fields of unmanned aerial vehicle abnormality detection and system health management. Background technique [0002] UAV anomaly detection methods can be mainly divided into three categories, knowledge-based anomaly detection methods, physical model-based anomaly detection methods and data-driven anomaly detection methods. The first two methods need to fully understand the domain knowledge or system structure to achieve better detection results, poor universality, poor anti-interference ability, and generally can only detect known types of anomaly detection. UAV telemetry data has few anomaly labels and complex anomaly types, and it is difficult to establish a unified physical model for its various subsystems. [0003] Data-driven anomaly detection methods belong to the category of multivariate statistical analysis, which can be subdivided into similarity-based methods, statistics-based methods, classification-based methods...

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Application Information

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IPC IPC(8): G06K9/62G06Q10/00
CPCG06Q10/20G06F18/24G06F18/214
Inventor 刘大同彭宇王泽洋王本宽彭喜元
Owner HARBIN INST OF TECH
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