TY - GEN
T1 - Bayesian framework and radar
T2 - 19th IEEE Statistical Signal Processing Workshop, SSP 2016
AU - Richmond, Christ D.
AU - Basu, Prabahan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/24
Y1 - 2016/8/24
N2 - The Bayesian framework is versatile as it allows for incorporation of prior knowledge or experience in making inference. The case of no prior knowledge at all is likewise seamlessly supported. The Bayesian framework is naturally suited to many fields of science and engineering including the discipline of radar design, analysis, and function. This paper will highlight recent findings on (i) Bayesian bounds under model misspecification that include the impact of using incorrect prior information, and (ii) parameter estimation for a cooperative radar-communication system.
AB - The Bayesian framework is versatile as it allows for incorporation of prior knowledge or experience in making inference. The case of no prior knowledge at all is likewise seamlessly supported. The Bayesian framework is naturally suited to many fields of science and engineering including the discipline of radar design, analysis, and function. This paper will highlight recent findings on (i) Bayesian bounds under model misspecification that include the impact of using incorrect prior information, and (ii) parameter estimation for a cooperative radar-communication system.
UR - http://www.scopus.com/inward/record.url?scp=84987925049&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987925049&partnerID=8YFLogxK
U2 - 10.1109/SSP.2016.7551792
DO - 10.1109/SSP.2016.7551792
M3 - Conference contribution
AN - SCOPUS:84987925049
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
BT - 2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
PB - IEEE Computer Society
Y2 - 25 June 2016 through 29 June 2016
ER -