MFR State Prediction Method Based on Aircraft Maneuvering Features Assistance

doi:10.3969/j.issn.0372-2112.2018.06.019

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MFR State Prediction Method Based on Aircraft Maneuvering Features Assistance

更新时间：2025-07-08

- MFR State Prediction Method Based on Aircraft Maneuvering Features Assistance
- Acta Electronica Sinica Vol. 46, Issue 6, Pages: 1404-1409(2018)
- 作者机构：
  
  1. 航天工程大学研究生院,北京,101416
  2. 航天工程大学光电装备系,北京,101416
  3. 航天工程大学研究生院,北京,101416
  4. 航天工程大学光电装备系,北京,101416
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2018.06.019
  CLC： TN958.9
- Published Online：25 June 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
MFR State Prediction Method Based on Aircraft Maneuvering Features Assistance[J]. Acta Electronica Sinica, 2018, 46(6): 1404-1409.
DOI：

MFR State Prediction Method Based on Aircraft Maneuvering Features Assistance[J]. Acta Electronica Sinica, 2018, 46(6): 1404-1409. DOI： 10.3969/j.issn.0372-2112.2018.06.019.

摘要

针对多功能雷达（Multi-Function Radar，MFR）状态预测方法存在的鲁棒性、预测正确率不佳的问题，提出一种基于机动特征辅助的MFR状态预测方法.该方法将载机机动信息与常规侦收参数共同作为预测特征集，一方面利用支持向量回归（Support Vector Regression，SVR）和侦收信号特征集，得到常规预测模型，另一方面通过SVR和机动特征集，得到MFR各个状态间的转变概率模型；然后利用D-S证据理论得到最终预测状态.实验结果表明，与SVR和LSR方法相比，平均预测精度分别提高了6.97%和7.2%，同时具备更优异的鲁棒性.此外，提出的预测方法通过进一步的拓展，可应用于机械设备、道路交通等领域.

Abstract

Aiming at the problem of weak robustness and poor accuracy of the traditional multi-function radar (MFR) state prediction methods

a MFR state prediction method aided by maneuvering features is proposed.First of all

the aircraft maneuver features and the conventional reconnaissance parameters work together as the prediction feature set;then

on the one hand

the conventional prediction model can be achieved by support vector regression (SVR) and detected signal feature set;on the other hand

the MFR state transition probability model of each state is obtained by SVR and maneuvering feature set;at last

the eventual prediction state is obtained by the D-S evidence theory.Experimental results show that in comparison with SVR and LSR

the proposed method improves the average prediction accuracy by 6.97% and 7.2% respectively

and meanwhile

it is more robust.The proposed method can be applied to the mechanical equipment

road transport

etc.by further extension.

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