Analytical Frameowork for Optimized Feature Extraction for upgrading Occupancy Sensing Performance

Preethi Krishna Rao Mane


Adoption of the occupancy sensor has become an inevitable in any commercial as well as non-commercial security devices owing to its proficiency in energy management. It has been found that usages of conventional sensors are shrouded with operational related problems and hence usage of Doppler radar offers better mitigation of such problems. However, the usage of doppler radar towards occupancy sensiing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system.


Occupancy Sensing; Doppler Radar; Optimization; Machine Learning; Surveillance; Motion Sensing

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