Estimación del Multiplicador Óptimo del Umbral CA-CFAR en Clutter Pareto de Parámetros Conocidos
DOI:
https://doi.org/10.18041/entramado.2017v13n1.25104Palabras clave:
Detectores de razón de falsas alarmas constante, distribución Pareto, clutter de radar, probabilidad de falsa alarma, selección adaptativa del umbral de detección, procesadores CFARResumen
El desempeño del procesador CA-CFAR es afectado por ciertas variaciones del clutter. Mientras que los problemas causados por los cambios repentinos del clutter han sido corregidos por múltiples propuestas CFAR, se ignora frecuentemente la influencia de las variaciones estadísticas lentas de la señal de fondo. Para resolver este problema, los autores estimaron los valores óptimos del multiplicador del umbral CA-CFAR necesarios para adaptar el procesador a los cambios estadísticos lentos, garantizando por tanto que la probabilidad de falsa alarma del detector exhibirá solamente una ligera desviación con respecto al valor concebido en el diseño. El clutter fue simulado con una distribución Pareto con parámetro de forma conocido de antemano, de acuerdo a publicaciones recientes que sugieren fuertemente el uso de esta distribución. La investigación actual completa un paso importante en el diseño de detectores adaptativos que operan sin el conocimiento a priori del parámetro de forma. Adicionalmente, los autores proporcionan expresiones matemáticas que permiten la aplicación directa de los resultados en el diseño de detectores de radar.
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Referencias
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3. ASIMIT, Alexandru V.; FURMAN, Edward; VERNIC, Raluca On a mul- tivariate pareto distribution. In: Insurance. 2010, vol. 46, no. 2, p. 308– 316.
4. CASO, Giuseppe.; DE NARDIS, Luca; Ferrante, Guido C.; Di Benede- tto, Maria-Gabriela. Cooperative Spectrum Sensing based on Majority decision under CFAR and CDR constraints. In: IEEE 24th Interna- tional Symposium on Personal, Indoor and Mobile Radio Communi- cations, Workshop on Cognitive Radio Medium Access Control and Network Solutions. (8-9 Sept: London, United Kingdom). 2013. doi: 10.1109/PIMRCW.2013.6707835
5. CHAKRAVARTHI, Paul R.; R.; Weiner, Donald D.; OZTURK, Aydin. On Determining the Radar Threshold from Experimental Data. In: Con- ference on Signals, Systems and Computers, Conference Record of the Twenty-Fifth Asilomar. (4-6 Nov.: Pacific Grove, California). 1991. p. doi: 10.1109/ACSSC.1991.186517 6. CHEN, Zhuo; LIU, Xianzu; WU, Zhensen; WANG, Xiaobing. The Analysis of Sea Clutter Statistics Characteristics Based on the Ob- served Sea Clutter of Ku-Band Radar. In: IEEE Proceedings of the In- ternational Symposium on Antennas & Propagation. (23-25 October: Nanjing, China). 2013.
7. CHLEBUS, Edward.; OHRI, Rahul. Estimating parameters of the pare- to distribution by means of zipf’s law: application to internet research. In: Paper presented at the IEEE Globecom. (28 Nov.- 2 Dic : St Louis, Missouri). 2005.
8. DE FIGUEIREDO, Felipe A. P.; Bianco, José A.; Lenzi, Karlo G.; Figuere- do, Fabrício L. LTE Random Access Detection Based on a CA-CFAR Strategy. In: International Workshop on Telecommunications. (3-6 May: Santa Rita do Sapucai, Brazil). 2013.
9. DONG, Yunhan. Distribution of X-Band High Resolution and High Grazing Angle Sea Clutter. Technical Report DSTO-RR-0316. Elec- tronic Warfare and Radar Division, Defence Science and Technology Organization: Edinburgh, South Australia. 2006
10. FARINA, Alfonso; STUDER, Francisco. A. A Review of CFAR Detec- tion Techniques in Radar Systems. In: Microwave Journal. 1986. vol. 29, no. 1, p. 115-118.
11. FARSHCHIAn, Masoud; Posner, Fred L. The Pareto Distribution for Low Grazing Angle and High Resolution X-Band Sea Clutter. In: IEEE Radar Conference Proceedings. (10-14 May: Washington DC, USA). 2010. p. 789-793. doi: 10.1109/RADAR.2010.5494513
12. FORBES, Catherine; EVANS, Merran; HASTINGS, Nicholas; PEACOK, Brian Statistical Distributions. 4th edition, New York: Wiley, 2011. p. 151
13. GELB, James M.; HEATH, Ross E.; TIPPLE, George L. Statistics of Dis- tinct Clutter Classes in Midfrequency Active Sonar. In: IEEE Oceanic Engineering. 2010, vol. 35, no. 2, p. 220-229.
14. GONZÁLEZ PADILLA, Argel; BRAVO QUINTANA, Berta; MACHA- DO FERNÁNDEZ, José R.; BUENO GONZÁLEZ, Adrian. Clasifica- ción del Clutter Marino utilizando Redes Neuronales Artificiales. In: Revista de Ingeniería Electrónica, Automática y Comunicaciones (RIE- LAC). 2013. vol. 34, no. 1, p. 1-11.
15. GRECO, Maria; BORDONI, Federica; GINI, Fulvio X-Band Sea-Clu- tter nonstationarity: Influence of Long Waves. In: IEEE Journal of Oceanic Engineering. 2004. vol. 29, no. 2, p. 269-283, doi: 10.1109/ JOE.2004.828548
16. ISHII, Seishiro; SAYAMA, Syuji; MIZUTANI, Koichi Effect of Changes in Sea-Surface State on Statistical Characteristics of Sea Clutter with X-band Radar. In: Wireless Engineering and Technology. 2011. vol. 2, no. 3, p. 175-183. doi: 10.4236/wet.2011.23025
17. Yadav, Ajay Kumar; KaNt, Laxmi. Moving Target Detection using VI- CFAR Algorithm on MATLAB Platform. In: International Journal of Advanced Research in Computer Science and Software Engineering. 2013, vol. 3, no. 12, p. 915-918.
18. MACHADO FERNÁNDEZ, José R. Estimation of the Relation be- tween Weibull Distributed Sea clutter and the CA-CFAR Scale Factor. In: Journal of Tropical Engineering. 2015. vol. 25, no. 2, p. 19-28. doi: 10.15517/jte.v25i2.18209
19. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. MA- TE-CFAR: Ambiente de Pruebas para Detectores CFAR en MATLAB. In: Telem@tica. 2014. vol. 13, no. 3, p. 86-98.
20. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. Cell Averaging CFAR Detector with Scale Factor Correction through the Method of Moments for the Log-Normal Distribution (accepted). In: Ciencia e Ingeniería Neogranadina. 2016
21. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. Im- proved Shape Parameter Estimation in K Clutter with Neural Ne- tworks and Deep Learning. In: International Journal of Interactive Multimedia and Artificial Intelligence. 2016a. vol. 3, no. 7, p. 96-103. doi: 10.9781/ijimai.2016.3714
22. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. Mo- delación de la Distribución K en MATLAB para Aplicaciones de Radar. In: Revista de Ingeniería Electrónica, Automática y Comunicaciones (RIELAC). 2016b. vol. 37, no. 2, p. 54-66.
23. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. Op- timal Selection of the CA-CFAR Adjustment Factor for K Power Sea Clutter with Statistical Variations. In: Ciencia e Ingeniería Neogranadi- na. 2016c. vol. 27, no. 1, p. 61-76. doi: 10.18359/rcin.1714
24. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C.; Chávez Ferry, Nelson. A Neural Network Approach to Weibull Dis- tributed Sea Clutter Parameter’s Estimation. In: Inteligencia Artificial. 2015. vol. 18, no. 56, p. 3-13. doi: 10.4114/ia.v18i56.1090
25. MACHADO FERNÁNDEZ, José R.; SÁNCHEZ RAMS, Roberto C. Implementación de un Detector de Promediación de Clutter (CA- CFAR) usando VHDL. In: Telem@tica. 2016. vol. 15, no. 2, p. 52-61.
26. Magaz, Boualem.; Belouchrani, Adel; Hamadouche, M’hamed. Automa- tic Threshold Selection in OS-CFAR Radar Detection using Informa- tion Theoretic Criteria. In: Progress In Electromagnetics Research B. 2011. vol. 30 ,no. 30 ,p. 157-175 doi: 10.2528/PIERB10122502
27. METCALF, Justin; BLUNT, Shannon. D.; HIMED, Braham. A Machine Learning Approach to Cognitive Radar Detection. In: 2015 IEEE Ra- dar Conference (RadarCon). (27-30 Oct: Johannesburg, South Africa). 2015. p. 1405-1411
28. MEZACHE, Amar.; CHALABI, Izzeddine.; SOLTANI, Faouzi.; SAHED, Mohamed. Estimating the Pareto plus Noise Distribution Parameters using Non-Integer Order Moments and [zlog(z)] approaches. In: IET Radar, Sonar and Navigation. 2016. vol. 10, no. 1, p. 192-204.
29. NOHARA, Tim J.; HAYKIN, Simon. Canadian East Coast Radar Trials and the K-Distribution. In: IEE Proceedings on Radar and Signal Pro- cessing. 1991. vol. 138, no. 2, pp. 80-88.
30. O’CONNOR, Andrew. N. Probability Distributions Used in Reliability Engineering. Maryland: Center for Reliability Engineering, 2011. p. 125- 130
31. PALAMA, Riccardo.; MARIA, Greco; STINCO, Pietro; GINI, Fulvio. Statistical Analysis of Netrad High Resolution Sea Clutter. In: Procee- dings of the 21st European Signal Processing Conference (EUSIPCO). (día mes: lugar). 2013. p.
32. PING, Quinqwei. Analysis of Ocean Clutter for Wide-Band Radar Based on Real Data. In: Proceedings of the 2011 International Con- ference on Innovative Computing and Cloud Computing. (13-14 Aug: Wuhan, China). 2011. p. 121-124
33. PIOTRKOWSKI, Michal. Some Preliminary Experiments with Distri- bution-Independent EVT-CFAR based on Recorded Radar Data. In: IEEE Radar Conference 08. (26-30 May: Rome, Italy). 2008. p. 1-6
34. Qin, Yuhua.; GONG, Huili. A New CFAR Detector based on Automa- tic Censoring Cell Averaging and Cell Averaging. In: Telkomnika. 2013. vol. 11, no. 6, p. 3298 - 3303.
35. RICHARDS, Mark A.; SCHEER, James A.; HOLM, William A. Principles of Modern Radar Vol I Basic Principles. New York: Scitech Publishing, 2010.
36. ROHLING, Hermann. Radar CFAR Thresholding in Clutter and Multiple Target Situations. In: IEEE Transactions on Aerospace and Electronic Systems. 1983. vol. 19, no. 4, p. 608-621. doi: 10.1109/ TAES.1983.309350
37. ROSENBERG, Luke.; BOCQUET, Stephen. Application of the Pare- to Plus Noise Distribution to Medium Grazing Angle Sea-Clutter. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015. vol. 8, no. 1, p. 255-261. doi: 10.1109/JS- TARS.2014.2347957
38. RYTGAARD, Mette. Estimation in the pareto distribution. In: ASTIN Bulletin. 1990. vol. 20, no. 2, p. 201–216.
39. TAKAHASHI, Satoshi. A CFAR Circuit of Detecting Spatially Correla- ted Target for Automotive UWB Radars. Technical Report, Faculty of Information Sciences, Hiroshima City University.
40. VAN CAO, Tri-Tan Non-homogeneity Detection in CFAR Refe- rence Windows using the Mean-to-Mean Ration Test. Tech report ADA554930. 2012. DSTO Defence Science and Technology Organi- sation, Edinburg, Australia.
41. WANG, Jianing.; XU, Xiaojian. Simulation of Pareto Distributed Tem- porally and Spatially Correlated Low Grazing Angle Sea Clutter. In: 2014 International Radar Conference. (19-23 May : Ohio, USA: lugar). 2014. p. 1-6
42. WARD, Keith.; TOUGH, Robert; WATTS, Simon. Sea Clutter Scat- tering, the K Distribution and Radar Performance. 2nd Edition, The Institution of Engineering and Technology: London, 2013.
43. WATTS, Simon; ROSENBERG, Luke. A Review of High Grazing Angle Sea Clutter. In: IEEE 2013 International Conference on Radar. (29 Apr - 3 May: Ottawa, Canada). 2013. p. 240-245
44. WEINBERG, Graham. V. Estimation of False Alarm Probabilities in Cell Averaging Constant False Alarm Rate Detectors via Monte Carlo Me- thods. Tech report ADA429631. 2004. DSTO Systems Sciences Labo- ratory, Salisbury, Australia.
45. WEINBERG, Graham. V. An Investigation of the Pareto Distribution as a Model for High Grazing Angle Clutter. Technical Report DS- TO-TR-2525. 2011. Electronic Warfare and Radar Division, DSTO, Edinburgh, Australia.
46. WEINBERG, Graham. V. Assessing Detector Performance, with Appli- cation to Pareto Coherent Multilook Radar Detection. In: IET Radar, Sonar and Navigation. 2013. vol. 7, no. 4, p. 401-412. doi: 10.1049/iet- rsn.2012.0127
47. WEINBERG, Graham. V. Constant False Alarm Rate Detectors for Pa- reto Clutter Models. In: IET Radar, Sonar and Navigation. 2013a. vol. 7, no. , p. 153-163. doi: 10.1049/iet-rsn.2011.0374.
48. WEINBERG, Graham. V. Estimation of Pareto clutter parameters using order statistics and linear regression. In: IET Electronics Letters 20th. 2013b. vol. 49, no. 13, p. 845- 846. doi: 10.1049/el.2013.0916
49. WEINBERG, Graham. V. Constant False Alarm Rate Detection in Pa- reto Distributed Clutter: Further Results and Optimality Issues. In: Contemporary Engineering Sciences. 2014, vol. 7, no. 6, p. 231-261. doi: dx.doi.org/10.12988/ces.2014.3737
2. BARKAT, Mourad. Signal Detection and Estimation. 2nd edition, Lon- don: Artech House, 2005, p. 317-326.
3. ASIMIT, Alexandru V.; FURMAN, Edward; VERNIC, Raluca On a mul- tivariate pareto distribution. In: Insurance. 2010, vol. 46, no. 2, p. 308– 316.
4. CASO, Giuseppe.; DE NARDIS, Luca; Ferrante, Guido C.; Di Benede- tto, Maria-Gabriela. Cooperative Spectrum Sensing based on Majority decision under CFAR and CDR constraints. In: IEEE 24th Interna- tional Symposium on Personal, Indoor and Mobile Radio Communi- cations, Workshop on Cognitive Radio Medium Access Control and Network Solutions. (8-9 Sept: London, United Kingdom). 2013. doi: 10.1109/PIMRCW.2013.6707835
5. CHAKRAVARTHI, Paul R.; R.; Weiner, Donald D.; OZTURK, Aydin. On Determining the Radar Threshold from Experimental Data. In: Con- ference on Signals, Systems and Computers, Conference Record of the Twenty-Fifth Asilomar. (4-6 Nov.: Pacific Grove, California). 1991. p. doi: 10.1109/ACSSC.1991.186517 6. CHEN, Zhuo; LIU, Xianzu; WU, Zhensen; WANG, Xiaobing. The Analysis of Sea Clutter Statistics Characteristics Based on the Ob- served Sea Clutter of Ku-Band Radar. In: IEEE Proceedings of the In- ternational Symposium on Antennas & Propagation. (23-25 October: Nanjing, China). 2013.
7. CHLEBUS, Edward.; OHRI, Rahul. Estimating parameters of the pare- to distribution by means of zipf’s law: application to internet research. In: Paper presented at the IEEE Globecom. (28 Nov.- 2 Dic : St Louis, Missouri). 2005.
8. DE FIGUEIREDO, Felipe A. P.; Bianco, José A.; Lenzi, Karlo G.; Figuere- do, Fabrício L. LTE Random Access Detection Based on a CA-CFAR Strategy. In: International Workshop on Telecommunications. (3-6 May: Santa Rita do Sapucai, Brazil). 2013.
9. DONG, Yunhan. Distribution of X-Band High Resolution and High Grazing Angle Sea Clutter. Technical Report DSTO-RR-0316. Elec- tronic Warfare and Radar Division, Defence Science and Technology Organization: Edinburgh, South Australia. 2006
10. FARINA, Alfonso; STUDER, Francisco. A. A Review of CFAR Detec- tion Techniques in Radar Systems. In: Microwave Journal. 1986. vol. 29, no. 1, p. 115-118.
11. FARSHCHIAn, Masoud; Posner, Fred L. The Pareto Distribution for Low Grazing Angle and High Resolution X-Band Sea Clutter. In: IEEE Radar Conference Proceedings. (10-14 May: Washington DC, USA). 2010. p. 789-793. doi: 10.1109/RADAR.2010.5494513
12. FORBES, Catherine; EVANS, Merran; HASTINGS, Nicholas; PEACOK, Brian Statistical Distributions. 4th edition, New York: Wiley, 2011. p. 151
13. GELB, James M.; HEATH, Ross E.; TIPPLE, George L. Statistics of Dis- tinct Clutter Classes in Midfrequency Active Sonar. In: IEEE Oceanic Engineering. 2010, vol. 35, no. 2, p. 220-229.
14. GONZÁLEZ PADILLA, Argel; BRAVO QUINTANA, Berta; MACHA- DO FERNÁNDEZ, José R.; BUENO GONZÁLEZ, Adrian. Clasifica- ción del Clutter Marino utilizando Redes Neuronales Artificiales. In: Revista de Ingeniería Electrónica, Automática y Comunicaciones (RIE- LAC). 2013. vol. 34, no. 1, p. 1-11.
15. GRECO, Maria; BORDONI, Federica; GINI, Fulvio X-Band Sea-Clu- tter nonstationarity: Influence of Long Waves. In: IEEE Journal of Oceanic Engineering. 2004. vol. 29, no. 2, p. 269-283, doi: 10.1109/ JOE.2004.828548
16. ISHII, Seishiro; SAYAMA, Syuji; MIZUTANI, Koichi Effect of Changes in Sea-Surface State on Statistical Characteristics of Sea Clutter with X-band Radar. In: Wireless Engineering and Technology. 2011. vol. 2, no. 3, p. 175-183. doi: 10.4236/wet.2011.23025
17. Yadav, Ajay Kumar; KaNt, Laxmi. Moving Target Detection using VI- CFAR Algorithm on MATLAB Platform. In: International Journal of Advanced Research in Computer Science and Software Engineering. 2013, vol. 3, no. 12, p. 915-918.
18. MACHADO FERNÁNDEZ, José R. Estimation of the Relation be- tween Weibull Distributed Sea clutter and the CA-CFAR Scale Factor. In: Journal of Tropical Engineering. 2015. vol. 25, no. 2, p. 19-28. doi: 10.15517/jte.v25i2.18209
19. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. MA- TE-CFAR: Ambiente de Pruebas para Detectores CFAR en MATLAB. In: Telem@tica. 2014. vol. 13, no. 3, p. 86-98.
20. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. Cell Averaging CFAR Detector with Scale Factor Correction through the Method of Moments for the Log-Normal Distribution (accepted). In: Ciencia e Ingeniería Neogranadina. 2016
21. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. Im- proved Shape Parameter Estimation in K Clutter with Neural Ne- tworks and Deep Learning. In: International Journal of Interactive Multimedia and Artificial Intelligence. 2016a. vol. 3, no. 7, p. 96-103. doi: 10.9781/ijimai.2016.3714
22. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. Mo- delación de la Distribución K en MATLAB para Aplicaciones de Radar. In: Revista de Ingeniería Electrónica, Automática y Comunicaciones (RIELAC). 2016b. vol. 37, no. 2, p. 54-66.
23. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C. Op- timal Selection of the CA-CFAR Adjustment Factor for K Power Sea Clutter with Statistical Variations. In: Ciencia e Ingeniería Neogranadi- na. 2016c. vol. 27, no. 1, p. 61-76. doi: 10.18359/rcin.1714
24. MACHADO FERNÁNDEZ, José R.; BACALLAO VIDAL, Jesús C.; Chávez Ferry, Nelson. A Neural Network Approach to Weibull Dis- tributed Sea Clutter Parameter’s Estimation. In: Inteligencia Artificial. 2015. vol. 18, no. 56, p. 3-13. doi: 10.4114/ia.v18i56.1090
25. MACHADO FERNÁNDEZ, José R.; SÁNCHEZ RAMS, Roberto C. Implementación de un Detector de Promediación de Clutter (CA- CFAR) usando VHDL. In: Telem@tica. 2016. vol. 15, no. 2, p. 52-61.
26. Magaz, Boualem.; Belouchrani, Adel; Hamadouche, M’hamed. Automa- tic Threshold Selection in OS-CFAR Radar Detection using Informa- tion Theoretic Criteria. In: Progress In Electromagnetics Research B. 2011. vol. 30 ,no. 30 ,p. 157-175 doi: 10.2528/PIERB10122502
27. METCALF, Justin; BLUNT, Shannon. D.; HIMED, Braham. A Machine Learning Approach to Cognitive Radar Detection. In: 2015 IEEE Ra- dar Conference (RadarCon). (27-30 Oct: Johannesburg, South Africa). 2015. p. 1405-1411
28. MEZACHE, Amar.; CHALABI, Izzeddine.; SOLTANI, Faouzi.; SAHED, Mohamed. Estimating the Pareto plus Noise Distribution Parameters using Non-Integer Order Moments and [zlog(z)] approaches. In: IET Radar, Sonar and Navigation. 2016. vol. 10, no. 1, p. 192-204.
29. NOHARA, Tim J.; HAYKIN, Simon. Canadian East Coast Radar Trials and the K-Distribution. In: IEE Proceedings on Radar and Signal Pro- cessing. 1991. vol. 138, no. 2, pp. 80-88.
30. O’CONNOR, Andrew. N. Probability Distributions Used in Reliability Engineering. Maryland: Center for Reliability Engineering, 2011. p. 125- 130
31. PALAMA, Riccardo.; MARIA, Greco; STINCO, Pietro; GINI, Fulvio. Statistical Analysis of Netrad High Resolution Sea Clutter. In: Procee- dings of the 21st European Signal Processing Conference (EUSIPCO). (día mes: lugar). 2013. p.
32. PING, Quinqwei. Analysis of Ocean Clutter for Wide-Band Radar Based on Real Data. In: Proceedings of the 2011 International Con- ference on Innovative Computing and Cloud Computing. (13-14 Aug: Wuhan, China). 2011. p. 121-124
33. PIOTRKOWSKI, Michal. Some Preliminary Experiments with Distri- bution-Independent EVT-CFAR based on Recorded Radar Data. In: IEEE Radar Conference 08. (26-30 May: Rome, Italy). 2008. p. 1-6
34. Qin, Yuhua.; GONG, Huili. A New CFAR Detector based on Automa- tic Censoring Cell Averaging and Cell Averaging. In: Telkomnika. 2013. vol. 11, no. 6, p. 3298 - 3303.
35. RICHARDS, Mark A.; SCHEER, James A.; HOLM, William A. Principles of Modern Radar Vol I Basic Principles. New York: Scitech Publishing, 2010.
36. ROHLING, Hermann. Radar CFAR Thresholding in Clutter and Multiple Target Situations. In: IEEE Transactions on Aerospace and Electronic Systems. 1983. vol. 19, no. 4, p. 608-621. doi: 10.1109/ TAES.1983.309350
37. ROSENBERG, Luke.; BOCQUET, Stephen. Application of the Pare- to Plus Noise Distribution to Medium Grazing Angle Sea-Clutter. In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015. vol. 8, no. 1, p. 255-261. doi: 10.1109/JS- TARS.2014.2347957
38. RYTGAARD, Mette. Estimation in the pareto distribution. In: ASTIN Bulletin. 1990. vol. 20, no. 2, p. 201–216.
39. TAKAHASHI, Satoshi. A CFAR Circuit of Detecting Spatially Correla- ted Target for Automotive UWB Radars. Technical Report, Faculty of Information Sciences, Hiroshima City University.
40. VAN CAO, Tri-Tan Non-homogeneity Detection in CFAR Refe- rence Windows using the Mean-to-Mean Ration Test. Tech report ADA554930. 2012. DSTO Defence Science and Technology Organi- sation, Edinburg, Australia.
41. WANG, Jianing.; XU, Xiaojian. Simulation of Pareto Distributed Tem- porally and Spatially Correlated Low Grazing Angle Sea Clutter. In: 2014 International Radar Conference. (19-23 May : Ohio, USA: lugar). 2014. p. 1-6
42. WARD, Keith.; TOUGH, Robert; WATTS, Simon. Sea Clutter Scat- tering, the K Distribution and Radar Performance. 2nd Edition, The Institution of Engineering and Technology: London, 2013.
43. WATTS, Simon; ROSENBERG, Luke. A Review of High Grazing Angle Sea Clutter. In: IEEE 2013 International Conference on Radar. (29 Apr - 3 May: Ottawa, Canada). 2013. p. 240-245
44. WEINBERG, Graham. V. Estimation of False Alarm Probabilities in Cell Averaging Constant False Alarm Rate Detectors via Monte Carlo Me- thods. Tech report ADA429631. 2004. DSTO Systems Sciences Labo- ratory, Salisbury, Australia.
45. WEINBERG, Graham. V. An Investigation of the Pareto Distribution as a Model for High Grazing Angle Clutter. Technical Report DS- TO-TR-2525. 2011. Electronic Warfare and Radar Division, DSTO, Edinburgh, Australia.
46. WEINBERG, Graham. V. Assessing Detector Performance, with Appli- cation to Pareto Coherent Multilook Radar Detection. In: IET Radar, Sonar and Navigation. 2013. vol. 7, no. 4, p. 401-412. doi: 10.1049/iet- rsn.2012.0127
47. WEINBERG, Graham. V. Constant False Alarm Rate Detectors for Pa- reto Clutter Models. In: IET Radar, Sonar and Navigation. 2013a. vol. 7, no. , p. 153-163. doi: 10.1049/iet-rsn.2011.0374.
48. WEINBERG, Graham. V. Estimation of Pareto clutter parameters using order statistics and linear regression. In: IET Electronics Letters 20th. 2013b. vol. 49, no. 13, p. 845- 846. doi: 10.1049/el.2013.0916
49. WEINBERG, Graham. V. Constant False Alarm Rate Detection in Pa- reto Distributed Clutter: Further Results and Optimality Issues. In: Contemporary Engineering Sciences. 2014, vol. 7, no. 6, p. 231-261. doi: dx.doi.org/10.12988/ces.2014.3737
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Estimación del Multiplicador Óptimo del Umbral CA-CFAR en Clutter Pareto de Parámetros Conocidos. (2017). Entramado, 13(1), 252-261. https://doi.org/10.18041/entramado.2017v13n1.25104
