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Efficient maximum likelihood angle estimation for signals with known waveforms in white noise
H. Li
, H. Pu
, J. Li
Department of Electrical and Computer Engineering
School of Engineering and Science
Stevens Institute of Technology
Research output
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Contribution to conference
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Paper
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peer-review
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Dive into the research topics of 'Efficient maximum likelihood angle estimation for signals with known waveforms in white noise'. Together they form a unique fingerprint.
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Keyphrases
Maximum Likelihood
100%
Known Waveform
100%
Angle Estimation
100%
Maximum Likelihood Method
33%
Signal-to-noise Ratio
33%
Additive Noise
33%
Estimation Accuracy
33%
Performance Estimation
33%
Computational Demand
33%
Snapshot number
33%
Computer Science
maximum-likelihood
100%
Maximum Likelihood Method
50%
Noise-to-Signal Ratio
50%
Estimation Accuracy
50%
Estimation Performance
50%
Priori Knowledge
50%
Engineering
Maximum Likelihood
100%
Priori Knowledge
33%
Signal-to-Noise Ratio
33%
Additive Noise
33%
Neuroscience
Signal-to-Noise Ratio
100%