R.I.P.
π»
Ghosted
Adaptive Signal Detection and Parameter Estimation in Unknown Colored Gaussian Noise
July 27, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Bo Tang, Haibo He, Steven Kay
arXiv ID
1607.08259
Category
physics.data-an
Cross-listed
cs.IT
Citations
3
Venue
arXiv.org
Last Checked
3 months ago
Abstract
This paper considers the general signal detection and parameter estimation problem in the presence of colored Gaussian noise disturbance. By modeling the disturbance with an autoregressive process, we present three signal detectors with different unknown parameters under the general framework of binary hypothesis testing. The closed form of parameter estimates and the asymptotic distributions of these three tests are also given. Given two examples of frequency modulated signal detection problem and time series moving object detection problem, the simulation results demonstrate the effectiveness of three presented detectors.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β physics.data-an
R.I.P.
π»
Ghosted
A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy
R.I.P.
π»
Ghosted
The Pandora Software Development Kit for Pattern Recognition
R.I.P.
π»
Ghosted
Emergence of Compositional Representations in Restricted Boltzmann Machines
R.I.P.
π»
Ghosted
Investigating echo state networks dynamics by means of recurrence analysis
R.I.P.
π»
Ghosted
Discovering state-parameter mappings in subsurface models using generative adversarial networks
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted