Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Akshay S. Bondre"'
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Autor:
Akshay S. Bondre, Christ D. Richmond
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-17
A procedure is developed to generate theoretical receiver operating characteristic (ROC) curves for the two-stage change detector proposed by Cha et al., in order to compare its detection performance with other detectors. A modification, however, to
Autor:
Akshay S. Bondre, Christ D. Richmond
Publikováno v:
2022 56th Asilomar Conference on Signals, Systems, and Computers.
Publikováno v:
2022 IEEE Radar Conference (RadarConf22).
Autor:
Akshay S. Bondre, Christ D. Richmond
Publikováno v:
2022 IEEE Radar Conference (RadarConf22).
Publikováno v:
Proceedings of Meetings on Acoustics.
Publikováno v:
ACSSC
The constrained Cramer-Rao bound (CRB) has been used successfully to study parameter estimation in flat-fading scenarios, and established the value of side information such as known waveform properties (e.g. constant modulus) and known training symbo
Publikováno v:
2020 IEEE Radar Conference (RadarConf20).
The analysis of the generalized likelihood ratio test (GLRT) radar receiver when the radar is coexisting with a cooperative in-band communication (comm.) system is challenged by the discrete nature of the comm. symbols. While the asymptotic performan
Autor:
Akshay S. Bondre, Christ D. Richmond
Publikováno v:
2020 IEEE International Radar Conference (RADAR).
The sample intensity ratio estimator and the sample coherence estimator are test statistics which are generally used for detecting areas of change between two synthetic aperture radar (SAR) images of the same scene, taken at different times. Cha et a
Publikováno v:
The Journal of the Acoustical Society of America. 151:A100-A100
Data driven based approaches to signal processing including deep neural networks (DNN) have shown promise in various fields. Such techniques tend to require significant training for good convergence. Model-based approaches, however, provide practical