Blind Estimation of the PN Sequence of A DSSS Signal Using A Modified Online Unsupervised Learning Machine
<p>A neural network model of LEAP, an online unsupervised learning machine.</p> "> Figure 2
<p>The true pseudo-random (PN) sequence of the direct sequence spread spectrum (DSSS) signal.</p> "> Figure 3
<p>The estimated eigenvalues of the autocorrelation matrix of the DSSS signal.</p> "> Figure 4
<p>The estimated eigenvectors: (<b>a</b>) The eigenvector corresponding to the largest eigenvalue; (<b>b</b>) the eigenvector corresponding to the second largest eigenvalue.</p> "> Figure 5
<p>The estimated sequences: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>b</mi> </mstyle> <mn>1</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mstyle mathvariant="bold" mathsize="normal"> <mi>b</mi> </mstyle> <mn>2</mn> </msub> </mrow> </semantics></math>.</p> "> Figure 6
<p>The estimated PN sequences: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> <mi>N</mi> </mstyle> </mrow> <mo stretchy="true">^</mo> </mover> </mrow> <mn>1</mn> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mstyle mathvariant="bold" mathsize="normal"> <mi>P</mi> <mi>N</mi> </mstyle> </mrow> <mo stretchy="true">^</mo> </mover> </mrow> <mn>2</mn> </msub> </mrow> </semantics></math>.</p> "> Figure 7
<p>Number of iterations for network convergence of the original LEAP and the modified LEAP sunder different SNRs.</p> "> Figure 8
<p>Correct PN sequence estimation probability of different methods under different SNRs.</p> ">
Abstract
:1. Introduction
- (a)
- The LEAP algorithm is applied into the field of the PN sequence estimation of DSSS signals and a modified LEAP algorithm is proposed. Compared to the original LEAP algorithm, the modified LEAP algorithm has a better convergence performance due to its use of variable learning steps rather than a fixed one;
- (b)
- Since the phase of the eigenvector can be inverted, the incorrect estimation of the PN sequence of the DSSS signal may be obtained. Based on this, a novel approach which makes full use of the correlation characteristics of the PN sequence is proposed here to solve this problem.
2. Basic Theories
2.1. DSSS Signal Model
2.2. The Principle of PCA for PN Sequence Estimation
2.3. Mathematical Model of The Modified LEAP
2.4. Asymptotic Stability Analysis of The Modified LEAP
3. PN Sequence Estimation and The Elimination of Phase Ambiguity
4. The Main Steps for PN Sequence Estimation
5. Simulations and Analysis
6. Conclusions
7. Patents
Author Contributions
Funding
Conflicts of Interest
References
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Wei, Y.; Fang, S.; Wang, X.; Huang, S. Blind Estimation of the PN Sequence of A DSSS Signal Using A Modified Online Unsupervised Learning Machine. Sensors 2019, 19, 354. https://doi.org/10.3390/s19020354
Wei Y, Fang S, Wang X, Huang S. Blind Estimation of the PN Sequence of A DSSS Signal Using A Modified Online Unsupervised Learning Machine. Sensors. 2019; 19(2):354. https://doi.org/10.3390/s19020354
Chicago/Turabian StyleWei, Yangjie, Shiliang Fang, Xiaoyan Wang, and Shuxia Huang. 2019. "Blind Estimation of the PN Sequence of A DSSS Signal Using A Modified Online Unsupervised Learning Machine" Sensors 19, no. 2: 354. https://doi.org/10.3390/s19020354