Passive detection orbit determination method based on deep neural network

A deep neural network and passive detection technology, applied in the field of optical camera relative orbit determination and navigation, to achieve the effects of reducing load, accelerating convergence, and improving observability

Active Publication Date: 2021-12-07
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

This method yields observability by superimposing independent biases on top of linearized motion dynamics, but due to the bias position vector length limitation, this method is only suitable for the proximate rendezvous phase

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  • Passive detection orbit determination method based on deep neural network
  • Passive detection orbit determination method based on deep neural network
  • Passive detection orbit determination method based on deep neural network

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Embodiment Construction

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0035] The invention discloses a method based on a deep neural network for space non-cooperative target only relative orbit determination based on angle measurement, aiming at the fact that there are three groups of angle observations that are not enough to determine the relative orbit determination based on the linearized relative motion model The relative distance between the satellite and the target satellite, so that the relative motion state cannot be dete...

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Abstract

The invention discloses a passive detection orbit determination method based on a deep neural network, and the method comprises the following steps: 1, defining a spacecraft training data generator, and obtaining the training data of the deep neural network through the data generator; 2, preprocessing the training data generated in the step 1 to obtain standardized data; 3, defining a deep neural network, determining proper parameters, and training the deep neural network offline through standardized data to obtain a nonlinear relative motion model of angle measurement relative orbit determination; and 4, deploying the nonlinear relative motion model on a sensing satellite, and inputting the relative measurement angle into the model to realize online determination of the relative orbit of the target satellite. The three groups of relative sight line measurement angles input by the model are set in a one-to-one mapping manner, so that the passive detection relative orbit of the non-cooperative target on the orbit is determined.

Description

technical field [0001] The invention relates to the technical field of optical camera relative orbit determination and navigation, in particular to a passive detection orbit determination method based on a deep neural network. Background technique [0002] With the increasing frequency of space activities, the number of space non-cooperative targets such as invalid satellites and space debris is increasing rapidly. The near-Earth space environment is becoming more and more deteriorating, and the safety problems of spacecraft in orbit are becoming more and more prominent. Therefore, it is of great significance to enhance space situational awareness and conduct on-orbit autonomous service research such as maintenance and de-orbit cleaning for space non-cooperative targets such as invalid satellites. The key prerequisite for autonomous on-orbit service and enhanced space situational awareness is to realize The target is relatively orbited independently. [0003] At present, s...

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Application Information

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Patent Type & AuthorityApplications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08G01C21/20G01C21/24G06F119/14
CPCG06F30/27G06N3/04G06N3/08G01C21/20G01C21/24G06F2119/14
Inventor龚柏春马钰权李爽廖文和
OwnerNANJING UNIV OF AERONAUTICS & ASTRONAUTICS