基于隨機(jī)矩陣?yán)碚摵妥钚∶枋鲩L度的機(jī)載前視陣?yán)走_(dá)雜波自由度估計

李海; 劉新龍; 蔣婷; 吳仁彪

doi:10.11999/JEIT160132

基于隨機(jī)矩陣?yán)碚摵妥钚∶枋鲩L度的機(jī)載前視陣?yán)走_(dá)雜波自由度估計

doi: 10.11999/JEIT160132

李海^1, ,,
劉新龍¹,
蔣婷¹,
吳仁彪¹

基金項目:

國家自然科學(xué)基金(61471365, 61571442, 61231017)

中央高?；究蒲袠I(yè)務(wù)費項目(3122015B002)，中國民航大學(xué)藍(lán)天青年學(xué)者培養(yǎng)經(jīng)費

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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-01-29
- 修回日期: 2016-06-23
- 刊出日期: 2016-12-19

Estimation of Clutter Degrees of Freedom in Airborne Forward-looking Radar via Random Matrix Theory and Minimum Description Length Criteria

Funds:

The National Natural Science Foundation of China (61471365, 61571442, 61231017), The National Universitys Basic Research Foundation of China (3122015B002), The Foundation for Sky Young Scholars of Civil Aviation University of China

摘要

摘要: 有限訓(xùn)練樣本時，總體協(xié)方差矩陣特征譜的嚴(yán)重擴(kuò)展使得機(jī)載前視陣?yán)走_(dá)雜波自由度估計困難。該文提出一種前視陣雜波自由度估計方法，該方法利用隨機(jī)矩陣?yán)碚?Random Matrix Theory, RMT)中特征值統(tǒng)計分布特性建立參數(shù)化的概率模型，結(jié)合最小描述長度(Minimum Description Length, MDL)準(zhǔn)則關(guān)于信源檢測的思想估計雜波自由度。該方法能夠在有限訓(xùn)練樣下實現(xiàn)雜波自由度的有效估計，仿真結(jié)果驗證了方法的有效性。
- 前視陣?yán)走_(dá) /
- 雜波自由度 /
- 隨機(jī)矩陣?yán)碚?/a> /
- 最小描述長度 /
- 協(xié)方差矩陣
Abstract: Owing to the heavy spread of eigenspectrum of the population covariance matrix under finite training samples condition, it is a challenge to estimate the clutter Degrees of Freedom (DoF) in airborne forward-looking radar. In this work, a method for estimation the clutters DoF is proposed. In order to estimate the clutters DoF, an idea from sources detection by Minimum Description Length (MDL) criterion is borrowed, and the parametric probability model is formed based on the eigenvalues statistical distribution properties from Random Matrix Theory (RMT). The proposed method is effective to estimate the clutters DoF under finite training samples condition, and the simulation results verify the efficiency of the proposed method.
- Forward-looking radar /
- Clutter Degrees of Freedom (CoF) /
- Random Matrix Theory (RMT) /
- Minimum Description Length (MDL) /
- Covariance matrix

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參考文獻(xiàn)(20)

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