In this paper, we address the problem of determining maximum-likelihood estimates of sinusoid parameters from a signal that consists of sinusoids and additive noise. We present three algorithms that integrate interval methods for global optimization with procedures that decompose the problem into smaller ones. Interval methods represent a global optimization technique that is based upon the branch and bound principle. More specifically, we decompose the problems via the expectation-maximization algorithm and variations of the coordinate descent algorithm. Although, we have not proven that the proposed algorithms converge to the global optimum, their performance in our simulation example was much superior to that of the popular iterative quadratic maximum likelihood (IQML) method.
Reliable Computing – Springer Journals
Published: Oct 16, 2004
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera