【摘要】：針對觀測噪聲對周跳探測與修復性能的影響,提出了一種新的利用貝葉斯壓縮感知技術進行周跳探測與修復的方法.在歷元間-站間載波相位雙差觀測模型的基礎上,通過挖掘周跳信號的稀疏特性,獲取感知矩陣,推導并建立稀疏周跳探測模型,利用稀疏貝葉斯學習中的相關向量機原理,結合周跳相關數據的先驗信息,基于主動相關決策理論,進行回歸估計獲得周跳預測值的分布,進而實現(xiàn)周跳的探測與修復.實驗表明,新方法在僅利用單頻或雙頻載波相位觀測量的情況下能有效探測并修復周跳,性能優(yōu)于正交匹配追蹤法及l(fā)_1范數法.
[Abstract]:Aiming at the effect of observation noise on cycle slip detection and repair performance, a new method of cycle slip detection and repair using Bayesian compression sensing technique is proposed. Based on the double difference observation model of carrier phase between epoch and station, the sparse characteristic of cycle hopping signal is excavated, the perception matrix is obtained, the detection model of sparse cycle slip is deduced and established, and the principle of correlation vector machine in sparse Bayesian learning is used. Based on the prior information of cycle slip correlation data and active correlation decision theory, the distribution of cycle slip prediction value is obtained by regression estimation, and the cycle slip detection and repair is realized. Experiments show that the new method can effectively detect and repair cycle slips with only single or double frequency carrier phase observations, and the performance of the new method is better than that of orthogonal matching tracking method and L _ S _ 1-norm method.
【作者單位】： 哈爾濱工程大學自動化學院;
【基金】：國家自然科學基金(批準號:61273081);國家自然科學基金青年基金(批準號:61304235,61401114) 中央高�；究蒲袠I(yè)務費專項資金(批準號:HEUCFD1431) 國家留學基金資助的課題~~
【分類號】：P228.4

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本文編號：2276879