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Classification of radar data by detecting and identifying spatial and temporal anomalies

Tutkimustuotosvertaisarvioitu

Standard

Classification of radar data by detecting and identifying spatial and temporal anomalies. / Väilä, Minna; Venäläinen, Ilkka; Jylhä, Juha; Ruotsalainen, Marja; Perälä, Henna; Visa, Ari.

Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI; Defence, Security, and Sensing 2010: Sensor Data and Signal, Image and Neural Processing, Orlando, FL, USA, April 5-9, 2010. Proceedings of SPIE. Bellingham, WA : SPIE, 2010.

Tutkimustuotosvertaisarvioitu

Harvard

Väilä, M, Venäläinen, I, Jylhä, J, Ruotsalainen, M, Perälä, H & Visa, A 2010, Classification of radar data by detecting and identifying spatial and temporal anomalies. julkaisussa Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI; Defence, Security, and Sensing 2010: Sensor Data and Signal, Image and Neural Processing, Orlando, FL, USA, April 5-9, 2010. Proceedings of SPIE. SPIE, Bellingham, WA. https://doi.org/10.1117/12.852252

APA

Väilä, M., Venäläinen, I., Jylhä, J., Ruotsalainen, M., Perälä, H., & Visa, A. (2010). Classification of radar data by detecting and identifying spatial and temporal anomalies. teoksessa Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI; Defence, Security, and Sensing 2010: Sensor Data and Signal, Image and Neural Processing, Orlando, FL, USA, April 5-9, 2010. Proceedings of SPIE Bellingham, WA: SPIE. https://doi.org/10.1117/12.852252

Vancouver

Väilä M, Venäläinen I, Jylhä J, Ruotsalainen M, Perälä H, Visa A. Classification of radar data by detecting and identifying spatial and temporal anomalies. julkaisussa Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI; Defence, Security, and Sensing 2010: Sensor Data and Signal, Image and Neural Processing, Orlando, FL, USA, April 5-9, 2010. Proceedings of SPIE. Bellingham, WA: SPIE. 2010 https://doi.org/10.1117/12.852252

Author

Väilä, Minna ; Venäläinen, Ilkka ; Jylhä, Juha ; Ruotsalainen, Marja ; Perälä, Henna ; Visa, Ari. / Classification of radar data by detecting and identifying spatial and temporal anomalies. Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI; Defence, Security, and Sensing 2010: Sensor Data and Signal, Image and Neural Processing, Orlando, FL, USA, April 5-9, 2010. Proceedings of SPIE. Bellingham, WA : SPIE, 2010.

Bibtex - Lataa

@inproceedings{0152c9c453d445c5a680453470be7efd,
title = "Classification of radar data by detecting and identifying spatial and temporal anomalies",
abstract = "For some time, applying the theory of pattern recognition and classification to radar signal processing has been a topic of interest in the field of remote sensing. Efficient operation and target indication is often hindered by the signal background, which can have similar properties with the interesting signal. Because noise and clutter may constitute most part of the response of surveillance radar, aircraft and other interesting targets can be seen as anomalies in the data. We propose an algorithm for detecting these anomalies on a heterogeneous clutter background in each range-Doppler cell, the basic unit in the radar data defined by the resolution in range, angle and Doppler. The analysis is based on the time history of the response in a cell and its correlation to the spatial surroundings. If the newest time window of response in a resolution cell differs statistically from the time history of the cell, the cell is determined anomalous. Normal cells are classified as noise or different type of clutter based on their strength on each Doppler band. Anomalous cells are analyzed using a longer time window, which emulates a longer coherent illumination. Based on the decorrelation behavior of the response in the long time window, the anomalous cells are classified as clutter, an airplane or a helicopter. The algorithm is tested with both experimental and simulated radar data. The experimental radar data has been recorded in a forested landscape.",
author = "Minna V{\"a}il{\"a} and Ilkka Ven{\"a}l{\"a}inen and Juha Jylh{\"a} and Marja Ruotsalainen and Henna Per{\"a}l{\"a} and Ari Visa",
note = "Contribution: organisation=sgn,FACT1=1",
year = "2010",
doi = "10.1117/12.852252",
language = "English",
isbn = "978-0-8194-8180-1",
booktitle = "Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI; Defence, Security, and Sensing 2010: Sensor Data and Signal, Image and Neural Processing, Orlando, FL, USA, April 5-9, 2010. Proceedings of SPIE",
publisher = "SPIE",
address = "United States",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - Classification of radar data by detecting and identifying spatial and temporal anomalies

AU - Väilä, Minna

AU - Venäläinen, Ilkka

AU - Jylhä, Juha

AU - Ruotsalainen, Marja

AU - Perälä, Henna

AU - Visa, Ari

N1 - Contribution: organisation=sgn,FACT1=1

PY - 2010

Y1 - 2010

N2 - For some time, applying the theory of pattern recognition and classification to radar signal processing has been a topic of interest in the field of remote sensing. Efficient operation and target indication is often hindered by the signal background, which can have similar properties with the interesting signal. Because noise and clutter may constitute most part of the response of surveillance radar, aircraft and other interesting targets can be seen as anomalies in the data. We propose an algorithm for detecting these anomalies on a heterogeneous clutter background in each range-Doppler cell, the basic unit in the radar data defined by the resolution in range, angle and Doppler. The analysis is based on the time history of the response in a cell and its correlation to the spatial surroundings. If the newest time window of response in a resolution cell differs statistically from the time history of the cell, the cell is determined anomalous. Normal cells are classified as noise or different type of clutter based on their strength on each Doppler band. Anomalous cells are analyzed using a longer time window, which emulates a longer coherent illumination. Based on the decorrelation behavior of the response in the long time window, the anomalous cells are classified as clutter, an airplane or a helicopter. The algorithm is tested with both experimental and simulated radar data. The experimental radar data has been recorded in a forested landscape.

AB - For some time, applying the theory of pattern recognition and classification to radar signal processing has been a topic of interest in the field of remote sensing. Efficient operation and target indication is often hindered by the signal background, which can have similar properties with the interesting signal. Because noise and clutter may constitute most part of the response of surveillance radar, aircraft and other interesting targets can be seen as anomalies in the data. We propose an algorithm for detecting these anomalies on a heterogeneous clutter background in each range-Doppler cell, the basic unit in the radar data defined by the resolution in range, angle and Doppler. The analysis is based on the time history of the response in a cell and its correlation to the spatial surroundings. If the newest time window of response in a resolution cell differs statistically from the time history of the cell, the cell is determined anomalous. Normal cells are classified as noise or different type of clutter based on their strength on each Doppler band. Anomalous cells are analyzed using a longer time window, which emulates a longer coherent illumination. Based on the decorrelation behavior of the response in the long time window, the anomalous cells are classified as clutter, an airplane or a helicopter. The algorithm is tested with both experimental and simulated radar data. The experimental radar data has been recorded in a forested landscape.

U2 - 10.1117/12.852252

DO - 10.1117/12.852252

M3 - Conference contribution

SN - 978-0-8194-8180-1

BT - Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI; Defence, Security, and Sensing 2010: Sensor Data and Signal, Image and Neural Processing, Orlando, FL, USA, April 5-9, 2010. Proceedings of SPIE

PB - SPIE

CY - Bellingham, WA

ER -