TY - GEN
T1 - A Comparative Study of the Performance of Parameter Estimation of a 2-D Field Using Line- and Point-Projection Sensors
AU - Gat, Shani
AU - Messer, Hagit
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - The signal's attenuation measured over wireless Commercial Microwave Links (CMLs) have proven to be an effective tool for opportunistic environmental sensing. By nature, a CML senses the projection along the propagation path of the 2-D field it monitors. Inspired by this application, we present a comparative theoretical study of two families of sensors: in one case the sensors are point samples of the field, and in the other case they are projections along a line of an arbitrary length and of arbitrary orientation, centered at the same locations. The study is done by Cramer-Rao Bounds, used for evaluating the performance of estimating a Gaussian-Shaped field by N sensors in given locations. This study is relevant to IoT applications, where in the first case the sensors are designated, local sensors (e.g., rain gauges) and in the second case the sensors are opportunistic, e.g., CMLs. We present appropriate algorithm-independent theoretical tools to deal with such problems, and relate the characteristics of the monitored field to the potential performance gain when using CMLs.
AB - The signal's attenuation measured over wireless Commercial Microwave Links (CMLs) have proven to be an effective tool for opportunistic environmental sensing. By nature, a CML senses the projection along the propagation path of the 2-D field it monitors. Inspired by this application, we present a comparative theoretical study of two families of sensors: in one case the sensors are point samples of the field, and in the other case they are projections along a line of an arbitrary length and of arbitrary orientation, centered at the same locations. The study is done by Cramer-Rao Bounds, used for evaluating the performance of estimating a Gaussian-Shaped field by N sensors in given locations. This study is relevant to IoT applications, where in the first case the sensors are designated, local sensors (e.g., rain gauges) and in the second case the sensors are opportunistic, e.g., CMLs. We present appropriate algorithm-independent theoretical tools to deal with such problems, and relate the characteristics of the monitored field to the potential performance gain when using CMLs.
KW - Fisher Information
KW - Opportunistic sensors
KW - environmental monitoring
KW - rain field estimation
UR - http://www.scopus.com/inward/record.url?scp=85082400379&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP45676.2019.9022443
DO - 10.1109/CAMSAP45676.2019.9022443
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AN - SCOPUS:85082400379
T3 - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
SP - 146
EP - 150
BT - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
Y2 - 15 December 2019 through 18 December 2019
ER -