一種K-means改進(jìn)算法的軟擴(kuò)頻信號(hào)偽碼序列盲估計(jì)

張?zhí)祢U; 楊強(qiáng); 宋玉龍; 熊梅

doi:10.11999/JEIT170306

一種K-means改進(jìn)算法的軟擴(kuò)頻信號(hào)偽碼序列盲估計(jì)

doi: 10.11999/JEIT170306

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61671095, 61371164)，信號(hào)與信息處理重慶市市級(jí)重點(diǎn)實(shí)驗(yàn)室建設(shè)項(xiàng)目(CSTC2009CA2003)，重慶市教育委員會(huì)科研項(xiàng)目(KJ130524, KJ1600427, KJ1600429)

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- 文章訪問(wèn)數(shù): 1545
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出版歷程
- 收稿日期: 2017-04-07
- 修回日期: 2017-07-11
- 刊出日期: 2018-01-19

Blind Estimation PN Sequence in Soft Spread Spectrum Signal of Improved K-means Algorithm

Funds:

The National Natural Science Foundation of China (61671095, 61371164), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009 CA2003), The Research Project of Chongqing Educational Commission (KJ130524, KJ1600427, KJ1600429)

摘要

摘要: 針對(duì)軟擴(kuò)頻信號(hào)因采用了編碼技術(shù)使得偽碼序列難以估計(jì)的問(wèn)題，該文提出一種基于K-means聚類改進(jìn)的軟擴(kuò)頻信號(hào)偽碼序列盲估計(jì)方法。該方法首先以單倍偽碼周期的窗長(zhǎng)對(duì)接收信號(hào)進(jìn)行數(shù)據(jù)分段以構(gòu)造觀測(cè)數(shù)據(jù)矩陣，其次利用相似測(cè)度的理論從觀測(cè)數(shù)據(jù)中尋找出K-means算法最優(yōu)的初始聚類中心點(diǎn)，然后通過(guò)搜索平均輪廓系數(shù)(Silhouette Coefficient, SC)最大的絕對(duì)值以完成偽碼集合規(guī)模數(shù)的估計(jì)，最后找到估計(jì)的偽碼集合規(guī)模數(shù)所對(duì)應(yīng)的聚類中心點(diǎn)集合，進(jìn)一步完成對(duì)軟擴(kuò)頻信號(hào)偽碼序列的盲估計(jì)。通過(guò)仿真實(shí)驗(yàn)表明，在偽碼序列估計(jì)錯(cuò)誤概率低于0.1的情況下，該文方法比未改進(jìn)方法提高信噪比約4 dB；而且在同一條件下，該文方法對(duì)信號(hào)的盲解擴(kuò)性能優(yōu)于未改進(jìn)的方法。
- 軟擴(kuò)頻信號(hào) /
- 偽碼序列 /
- K-means聚類 /
- 盲估計(jì)
Abstract: For the problem of the soft spread spectrum signal Pseudo-Noise (PN) sequence is difficult to estimate by using the coding technology, a blind estimation PN sequence method of soft spread spectrum signal is proposed based on improved K-means algorithm. Firstly, the received signal is divided into continuous non-overlapping temporal vectors according to one period of PN sequence to construct observation data matrix. Secondly, the similarity measure theory is applied to find out the optimal initial clustering center point of K-means algorithm from the observed matrix. Then the number of scale of PN sequence can be estimated by searching for the maximum absolute value of the average Silhouette Coefficient (SC). Finally, the estimated clustering center point corresponding to the number of scale of PN sequence is found, the blind estimation PN sequence of the soft spread spectrum signal is further completed. The simulation results show that the proposed method improves the Signal-to-Noise Ratio (SNR) about 4 dB compared to the traditional method under the condition of the estimation error probability of PN sequence is less than 0.1. Moreover, the blind dispreading performance is also better than unmodified method under the same condition.
- Soft spread spectrum /
- Pseudo Noise (PN) sequence /
- K-means clustering /
- Blind estimation

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