scholarly journals Wave Measurements Using Multi-Frame Processing of Marine Radar Data

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5639
Author(s):  
David Lyzenga ◽  
Mirko Previsic
Keyword(s):  
Additive Noise ◽  
Ocean Waves ◽  
Radar Data ◽  
Angle Dependence ◽  
Wave Measurements ◽  
Combining Data ◽  
Marine Radar ◽  
The Look ◽  
Frame Processing ◽  
Buoy Measurements

Marine radars have proven to be useful for measuring ocean waves, but the accuracy of the measurements is limited by several factors including the look-angle dependence of the radar signals as well as noise in the radar data. The look-angle dependence introduces a systematic error or bias in the measurements, and noise causes a random error. This paper describes a method of combining data from multiple radar frames that is optimal in the sense of minimizing the error for a set of biased measurements with random additive noise. The results are shown experimentally to increase the correlation of the radar estimates with buoy measurements.

scholarly journals Polar Fourier Transform Processing of Marine Radar Signals

2017 ◽  
Vol 34 (2) ◽  
pp. 347-354 ◽  
Author(s):  
David R. Lyzenga
Keyword(s):  
Doppler Radar ◽  
Ocean Waves ◽  
Radar Data ◽  
Propagation Direction ◽  
Wave Fields ◽  
Statistical Measures ◽  
Marine Radar ◽  
Radar Signals ◽  
Ocean Surface Wave ◽  
Power Measurements

AbstractThis paper describes a method of processing marine radar signals for the purpose of generating phase-resolved surface elevation maps as well as statistical measures of ocean surface wave fields. The method is well suited to the processing of data collected by marine radars because it allows for the incorporation of effects dependent on the radar look direction relative to the propagation direction of ocean waves. Applications to Doppler radar and backscattered power measurements are described, and example results are presented using simulated radar data.

scholarly journals Using Data Augmentation and Time-Scale Modification to Improve ASR of Children’s Speech in Noisy Environments

2021 ◽  
Vol 11 (18) ◽  
pp. 8420
Author(s):  
Hemant Kumar Kathania ◽  
Sudarsana Reddy Kadiri ◽  
Paavo Alku ◽  
Mikko Kurimo
Keyword(s):  
Time Scale ◽  
Additive Noise ◽  
Data Augmentation ◽  
Training Data ◽  
Speaking Rate ◽  
Noisy Environments ◽  
Testing Phase ◽  
Combining Data ◽  
Time Scale Modification ◽  
Children's Speech

Current ASR systems show poor performance in recognition of children’s speech in noisy environments because recognizers are typically trained with clean adults’ speech and therefore there are two mismatches between training and testing phases (i.e., clean speech in training vs. noisy speech in testing and adult speech in training vs. child speech in testing). This article studies methods to tackle the effects of these two mismatches in recognition of noisy children’s speech by investigating two techniques: data augmentation and time-scale modification. In the former, clean training data of adult speakers are corrupted with additive noise in order to obtain training data that better correspond to the noisy testing conditions. In the latter, the fundamental frequency (F0) and speaking rate of children’s speech are modified in the testing phase in order to reduce differences in the prosodic characteristics between the testing data of child speakers and the training data of adult speakers. A standard ASR system based on DNN–HMM was built and the effects of data augmentation, F0 modification, and speaking rate modification on word error rate (WER) were evaluated first separately and then by combining all three techniques. The experiments were conducted using children’s speech corrupted with additive noise of four different noise types in four different signal-to-noise (SNR) categories. The results show that the combination of all three techniques yielded the best ASR performance. As an example, the WER value averaged over all four noise types in the SNR category of 5 dB dropped from 32.30% to 12.09% when the baseline system, in which no data augmentation or time-scale modification were used, was replaced with a recognizer that was built using a combination of all three techniques. In summary, in recognizing noisy children’s speech with ASR systems trained with clean adult speech, considerable improvements in the recognition performance can be achieved by combining data augmentation based on noise addition in the system training phase and time-scale modification based on modifying F0 and speaking rate of children’s speech in the testing phase.

Data fusion of IR and marine radar data

2013 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Mohsin M. Jamali ◽  
Peter V. Gorsevski ◽  
Joseph Frizado ◽  
Verner P. Bingman
Keyword(s):  
Data Fusion ◽  
Radar Data ◽  
Marine Radar

Navigation by Computer Processing of Marine Radar Images

1985 ◽  
Vol 38 (3) ◽  
pp. 375-383 ◽  
Author(s):  
G. L. Austin ◽  
A. Bellon ◽  
M. Riley ◽  
E. Ballantyne
Keyword(s):  
Radar Data ◽  
Computer Processing ◽  
Pattern Recognition Technique ◽  
Radar Images ◽  
Well Yield ◽  
Radar Imagery ◽  
On Line ◽  
Marine Radar ◽  
Recognition Technique ◽  
Better Than

The advantages of being able to process marine radar imagery in an on-line computer system have been illustrated by study of some navigational problems. The experiments suggest that accuracies of the order of 100 metres may be obtained in navigation in coastal regions using map overlays with marine radar data. A similar technique using different radar imagery of the same location suggests that the pattern-recognition technique may well yield a position-keeping ability of better than 10 metres.

scholarly journals Spatial Averaging of HF Radar Data for Wave Measurement Applications

2013 ◽  
Vol 30 (9) ◽  
pp. 2216-2224 ◽  
Author(s):  
Lucy R. Wyatt ◽  
Jasmine B. D. Jaffrés ◽  
Mal L. Heron
Keyword(s):  
Radar Data ◽  
Hf Radar ◽  
High Temporal Resolution ◽  
Spatial Averaging ◽  
Spectral Estimates ◽  
Wave Measurements ◽  
Temporal Averaging ◽  
Time Periods ◽  
The Impact ◽  
Very High

Abstract HF radar data are often collected for time periods that are optimized for current measurement applications where, in many cases, very high temporal resolution is needed. Previous work has demonstrated that this does not provide sufficient averaging for robust wave measurements to be made. It was shown that improvements could be made by averaging the radar data for longer time periods. HF radar provides measurements over space as well as in time, so there is also the possibility to average in space. However, the radar data are correlated in space because of the range and azimuth processing. The implications of this are discussed and estimates of the impact on the reduction in variance in the radar Doppler spectral estimates are obtained. Spatial inhomogeneities and temporal nonstationarity in the ocean wave field itself also need to be taken into account. It is suggested that temporal averaging over periods of up to one hour and spatial averaging over 9–25 nearest neighbors may be suitable, and these will be explored in later work.

A Comparison of SIR-B Synthetic Aperture Radar Data with Ocean Internal Wave Measurements

Science ◽  
1986 ◽  
Vol 232 (4757) ◽  
pp. 1529-1531 ◽  
Author(s):  
R. F. GASPAROVIC ◽  
J. R. APEL ◽  
D. R. THOMPSON ◽  
J. S. TOCHKO
Keyword(s):  
Synthetic Aperture Radar ◽  
Internal Wave ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Wave Measurements ◽  
Aperture Radar

Real-time estimation of ocean wave fields from marine radar data

2015 ◽  
Author(s):  
David R. Lyzenga ◽  
Okey G. Nwogu ◽  
Robert F. Beck ◽  
Andrew O'Brien ◽  
Joel Johnson ◽  
...  
Keyword(s):  
Real Time ◽  
Time Estimation ◽  
Radar Data ◽  
Ocean Wave ◽  
Wave Fields ◽  
Real Time Estimation ◽  
Marine Radar
Sign in / Sign up

Export Citation Format

Share Document