Analysis Of Atmospheric Radar Echoes Using Fft And Matched Filter For Doppler Profile Detection

2012 ◽  
Vol 1 (2) ◽  
pp. 1-5
Author(s):  
N. Padmaja ◽  
S Varadarajan
Keyword(s):  
Matched Filter ◽  
Radar Echoes ◽  
Doppler Profile

Object Detection Using an Ideal Observer Model

2020 ◽  
Vol 2020 (16) ◽  
pp. 41-1-41-7
Author(s):  
Orit Skorka ◽  
Paul J. Kane
Keyword(s):  
Object Detection ◽  
Matched Filter ◽  
Image Sensors ◽  
Ideal Observer ◽  
Noise Power ◽  
Relative Evaluation ◽  
Detection And Identification ◽  
Cross Correlation Analysis ◽  
Observer Model ◽  
The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

Digital Tapped Delay Lines for HWIL Testing of Matched Filter Radar Receivers

2009 ◽  
Author(s):  
Richard F. Olson ◽  
William J. Braselton ◽  
Richard D. Mohlere
Keyword(s):  
Matched Filter ◽  
Delay Lines ◽  
Tapped Delay Lines

Matched Filter Processor for Detection and Discrimination of Unexploded Ordnance

2003 ◽  
Author(s):  
Jeffrey Marqusee ◽  
George Robitaille ◽  
Thomas Bell
Keyword(s):  
Matched Filter ◽  
Unexploded Ordnance

Feature extraction of precession target based on Doppler profile sequences by maximum likelihood estimation

2019 ◽  
Vol 55 (9) ◽  
pp. 554-556
Author(s):  
Ning Wang ◽  
Di Mo ◽  
Ziqi Song ◽  
Ran Wang ◽  
Guangzuo Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Likelihood Estimation ◽  
Doppler Profile

scholarly journals Cloud Detection Based on High Resolution Stereo Pairs of the Geostationary Meteosat Images

2020 ◽  
Vol 12 (3) ◽  
pp. 371 ◽  
Author(s):  
Sahar Dehnavi ◽  
Yasser Maghsoudi ◽  
Klemen Zakšek ◽  
Mohammad Javad Valadan Zoej ◽  
Gunther Seckmeyer ◽  
...  
Keyword(s):  
High Resolution ◽  
Matched Filter ◽  
Stereo Pair ◽  
Cloud Detection ◽  
Operating Characteristics ◽  
Detection Techniques ◽  
Infrared Imager ◽  
Detection Approach ◽  
Considerable Impact ◽  
Image Pairs

Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

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