Missile-borne deep integrated ARCKF (Adaptive Robust Capacity Kalman Filtering) method under strong maneuvering condition

A deep combination and maneuvering technology, applied in the field of integrated navigation, can solve problems such as errors and carrier observation anomalies, and achieve the effect of resisting errors and high estimation accuracy

Inactive Publication Date: 2017-06-23
NANJING UNIV OF SCI & TECH
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Problems solved by technology

However, in complex environments such as strong maneuvering and high dynamics, observation anomalies and errors caused by inaccurate dynamic models may be brought to the carrier, and the performance of integrated navigation filtering needs to be further improved

Method used

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  • Missile-borne deep integrated ARCKF (Adaptive Robust Capacity Kalman Filtering) method under strong maneuvering condition
  • Missile-borne deep integrated ARCKF (Adaptive Robust Capacity Kalman Filtering) method under strong maneuvering condition
  • Missile-borne deep integrated ARCKF (Adaptive Robust Capacity Kalman Filtering) method under strong maneuvering condition

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Embodiment 1

[0115] In order to illustrate the method of the present invention, fully demonstrate the reliability and robustness of the method, and reflect the filtering performance and effect of the filtering method under strong maneuvering and high dynamic conditions, the following simulation test is carried out. The proposed Adaptive Robust Volumetric Kalman Filter (ARCKF) method is applied to the GNSS / SINS deep integrated navigation of ballistic missiles under strong maneuvering and high dynamic conditions for simulation verification, and compared with the ordinary Kalman Filter (KF) algorithm , the simulation conditions are as follows:

[0116] In the GNSS / SINS integrated navigation system under the launch inertial coordinate system of this method, the state equation is taken as the linear system equation of the combination of pseudorange and pseudorange rate, and the observation equation is taken as the nonlinear observation equation of the combination of pseudorange and pseudorange r...

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Abstract

The invention discloses a missile-borne deep integrated ARCKF (Adaptive Robust Capacity Kalman Filtering) method under a strong maneuvering condition. The missile-borne deep integrated ARCKF method comprises the following steps: generating a ballistic trajectory and corresponding IMU (Inertial Measurement Unit) data of a simulated strong maneuvering missile through a trajectory generator; inputting the generated ballistic trajectory into a satellite signal simulator, and generating a GNSS (Global Navigation Satellite System) intermediate-frequency signal; then inputting the generated GNSS intermediate-frequency signal into a software receiver and carrying out inertial navigation calculation on the IMU data; establishing a state equation and an observation equation of a GNNS / SINS (Strapdown Inertial Navigation System) deep integrated navigation system under a launch inertial coordinate system; simultaneously integrating a robust M-estimation algorithm in a robust estimation theory and adaptive factors to a CFK (Capacity Kalman Filtering) algorithm, forming an ARCKF algorithm, and carrying out filtering correction on a system state. The missile-borne deep integrated ARCKF method disclosed by the invention can be used for GNSS / SINS deep integrated navigation of a carrier under strong maneuvering and high dynamic conditions, and the anti-jamming capability of navigation and the navigation accuracy are effectively increased.

Description

[0001] 1. Technical field [0002] The invention relates to the technical field of integrated navigation, in particular to a deep integrated ARCKF filter method on missiles under strong maneuvering conditions. [0003] 2. Background technology [0004] Carrier navigation and positioning under strong maneuvering and high dynamic conditions is a hot and difficult issue, especially in military applications, which has attracted widespread attention from many scholars at home and abroad. The difficulties of navigation and positioning under strong maneuvering and high dynamic conditions mainly include the following points: the calculation error of the inertial navigation system becomes larger, which also puts forward high requirements for positioning technology; the system observation transfer function is strongly nonlinear, and the linearization error It leads to system divergence, so higher requirements are put forward for the robustness and reliability of data fusion technology. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/16G01C21/20
CPCG01C21/165G01C21/20
Inventor 陈帅汪益平蒋长辉任智博卢启伟屈新芬韩乃龙孙昭行赵琛韩筱韩林张博雅樊龙江
Owner NANJING UNIV OF SCI & TECH
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