1 Introduction
2 Blind Separation of Weak Signals Against the Strong Signal Interference
2.1 BSS Model
2.2 Framework of Blind Separation of Weak Signals Against the Strong Signal Interference
2.3 FastICA Algorithm
2.4 Improved FastICA Algorithm with K-Means Algorithm
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Reduce iteration timesIf the separation results are out of the acceptable range or the FastICA algorithm is non-convergent, we must replace the initial value. The improved FastICA algorithm reduces iteration times and improve the stability of the convergence.
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Improve the stability of the algorithmThe original vector \(W_0\) is in \(\mu _1,\mu _2,\ldots ,\mu _K\), which have universality. Then, the process can improve stability of the algorithm.
2.5 Interference Cancelation Algorithm (IC-Algorithm)
3 Simulation and Blind Source Signal Separation Results
3.1 Effectiveness of Interference Cancellation algorithm (IC-Algorithm)
3.2 Extracting the Strong Interference Signal with the Improved FastICA Algorithm
3.3 Strong Interference Signal Cancelation Using the Interference Cancelation Algorithm (IC-Algorithm)
3.4 Blind Signals Separation with the Improved FastICA Algorithm
4 Discussions on the Properties
4.1 The First Comparative Experiment of Effect
4.2 The Second Comparative Experiment of Effect
4.3 Termination Criteria
4.4 Robustness Analysis
4.5 Complexity Analysis
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Estimate the channel parameters with the reference strong jamming.In (4), the coefficient matrix is \(L\times K\), the source signal matrix is \(K\times N\), then, the multiplicative complexity is \(O(K \times N \times L)\), addition complexity is \(O(K \times N \times L)\).
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Calculating the strong interference signal can be separated from the mixed signal based on the channel parameters.