Low Frequency Analyzer and Recorder and Low Frequency Analysis and Recording (LOFAR) are the equipment and process respectively for presenting a visual spectrum representation of low frequency sounds in a time–frequency analysis.

Here, in the x-axis, the frequency information is shown while in the y-axis, as time progresses (each signal block pass through LOFAR Analysis), the frequency information is updated, creating...

Feb 25, 2023 · Lofargram, a type of sonar image whose frequency spectrum contains abundant motion information of the target, is important to passive underwater target detection. When the target is moving and passes by the CPA, the lofargram will display interference striations with parabolic patterns.

To tackle this issue, we introduce a pioneering Dual-Domain Multi-Frequency Network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection.

Frequency line tracking in a lofragram is usually challenged by the strong background noise in passive sonar systems. This paper proposes a principle component analysis based lofargram analysis method for multiple frequency line tracking.

Oct 19, 2020 · This paper investigates the problem of dim frequency line detection and recovery in the so-called lofargram. Theoretically, long enough time integration can always enhance the detection characteristic.

The authors deals with the issue of spectral line tracking in a time/frequency sonar image, the so-called lofargram (LOw Frequency Analysis Representation).

The tutorial style coverage of the detection of sinusoids in noise using lofargrams provides an introduction to those involved in signal analysis, acoustic processor testing and the ttaining of sonar operators.

Jun 1, 2022 · In this paper, a novel algorithm for extracting frequency lines on lofargram based on feature function is proposed. Firstly, the lofargram is preprocessed before extracting the frequency line, which makes the frequency line in the lofargram more obvious.

In this paper, we use convolutional neural network models to identify tonal frequencies in a lofargram. We divide a lofargram into several small patches, and a CNN model predicts the probability that the patch is from a tonal frequency.