Computationally Efficient Calculations of Target Performance of the Normalized Matched Filter Detector for Hydrocoustic Signals
February 24, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Roee Diamant
arXiv ID
1604.06416
Category
physics.data-an
Cross-listed
cs.CE,
cs.IT
Citations
5
Venue
arXiv.org
Last Checked
3 months ago
Abstract
Detection of hydroacoustic transmissions is a key enabling technology in applications such as depth measurements, detection of objects, and undersea mapping. To cope with the long channel delay spread and the low signal-to-noise ratio, hydroacoustic signals are constructed with a large time-bandwidth product, $N$. A promising detector for hydroacoustic signals is the normalized matched filter (NMF). For the NMF, the detection threshold depends only on $N$, thereby obviating the need to estimate the characteristics of the sea ambient noise which are time-varying and hard to estimate. While previous works analyzed the characteristics of the normalized matched filter (NMF), for hydroacoustic signals with large $N$ values the expressions available are computationally complicated to evaluate. Specifically for hydroacoustic signals of large $N$ values, this paper presents approximations for the probability distribution of the NMF. These approximations are found extremely accurate in numerical simulations. We also outline a computationally efficient method to calculate the receiver operating characteristic (ROC) which is required to determine the detection threshold. Results from an experiment conducted in the Mediterranean sea at depth of 900~m agree with the analysis.
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
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