Abstract
Based on analyses and research the correlation characteristic of MSK signal, this paper present a novelalgorithm to detect the endpoint of MSK signal pulse. This is accomplished by computing the short time auto correlation of received signal with a sliding window. Since the computation quantity of this algorithm is small it can be easily applied to engineering. The simulation results show that this algorithm could limit the estimate error under a quarter of the code width when SNR above 5dB.
This work was supported in part by the National Science Foundation of China under Grant (Program No. 60971100) and in part by Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2010JQ8003, 2010JM8037).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Huang, C.-L., Jiang, W.-L., Zhou, Y.-Y.: Cyclic spectrum estimation of initial phase and spreading code timing epoch of DS signals. Journal of China Institute of Communications 23(7) (2002)
Mengaliu, D’Andrea, A.N.: Synchronization techniques for digital receivers. Plenum, New York (1997)
Feng, Q., Zhang, X.: Improved Timing Offset Estimation Method For MSK Burst-Mode Signal. Journal of Data Acquisition & Processing 21(12), 132–134 (2006)
Li, L., Yu, H., Yuan, J.: Algorithm for MSK Pulse Start-End Point Detection Based on Higher-order Cumulants. Journal of Data Acquisition & Proceessing 21(12) (2006)
Sklar, B.: Digital Communications: Fundamentals and Applications, 2nd edn.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, D., Guo, Y. (2012). A Novel Algorithm for CPM-Signal Endpoint Detection Based on Auto Correlation. In: Zhang, Y., Zhou, ZH., Zhang, C., Li, Y. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2011. Lecture Notes in Computer Science, vol 7202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31919-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-31919-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31918-1
Online ISBN: 978-3-642-31919-8
eBook Packages: Computer ScienceComputer Science (R0)