Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Publication date | 8/11/2016 |
---|---|
Host publication | 2016 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings |
Publisher | IEEE Computer Society |
Number of pages | 6 |
ISBN (electronic) | 9781509007462 |
<mark>Original language</mark> | English |
Event | 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings - Vietri sul Mare, Salerno, Italy Duration: 13/09/2016 → 16/09/2016 |
Conference | 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings |
---|---|
Country/Territory | Italy |
City | Vietri sul Mare, Salerno |
Period | 13/09/16 → 16/09/16 |
Conference | 26th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2016 - Proceedings |
---|---|
Country/Territory | Italy |
City | Vietri sul Mare, Salerno |
Period | 13/09/16 → 16/09/16 |
This paper provides an algorithm for simulating improper (or noncircular) complex-valued stationary Gaussian processes. The technique utilizes recently developed methods for multi-variate Gaussian processes from the circulant embedding literature. The method can be performed in O(n log2 n) operations, where n is the length of the desired sequence. The method is exact, except when eigenvalues of prescribed circulant matrices are negative. We evaluate the performance of the algorithm empirically, and provide a practical example where the method is guaranteed to be exact for all n, with an improper fractional Gaussian noise process.