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Doppler radar node occupancy sensors are promising for applications in smart buildings due to their simple circuits and price advantage compared to quadrature radar sensors. However, single-channel sensitivity limitations may result in low sensitivity and misinterpreted motion rates if the detected subject is at or close to “null” points. We designed and tested a novel method to eliminate such limits, demonstrating that passive nodes can be used to detect a sedentary person regardless of position. This method is based on characteristics of chest motion due to respiration, found via both simulations and experiments based on a sinusoidal model and a more realistic model of cardiorespiratory motion. In addition, respiratory rate variability is considered to distinguish a true human presence from a mechanical target. Sensor node data were collected simultaneously with an infrared camera system, which provided a respiration signal reference, to test the algorithm with 19 human subjects and a mechanical target. The results indicate that a human presence was detected with 100% accuracy and successfully differentiated from a mechanical target in a controlled environment. The developed method can greatly improve the occupancy detection accuracy of single-channel radar-based occupancy sensors and facilitate their adoption in smart building applications. 

Building occupancy information is significant for a variety of reasons, from allocation of resources in smart buildings to responding during emergency situations. As most people spend more than 90% of their time indoors, a comfortable indoor environment is crucial. To ensure comfort, traditional HVAC systems condition rooms assuming maximum occupancy, accounting for more than 50% of buildings’ energy budgets in the US. Occupancy level is a key factor in ensuring energy efficiency, as occupancy-controlled HVAC systems can reduce energy waste by conditioning rooms based on actual usage. Numerous studies have focused on developing occupancy estimation models leveraging existing sensors, with camera-based methods gaining popularity due to their high precision and widespread availability. However, the main concern with using cameras for occupancy estimation is the potential violation of occupants’ privacy. Unlike previous video-/image-based occupancy estimation methods, we addressed the issue of occupants’ privacy in this work by proposing and investigating both motion-based and motion-independent occupancy counting methods on intentionally blurred video frames. Our proposed approach included the development of a motion-based technique that inherently preserves privacy, as well as motion-independent techniques such as detection-based and density-estimation-based methods. To improve the accuracy of the motion-independent approaches, we utilized deblurring methods: an iterative statistical technique and a deep-learning-based method. Furthermore, we conducted an analysis of the privacy implications of our motion-independent occupancy counting system by comparing the original, blurred, and deblurred frames using different image quality assessment metrics. This analysis provided insights into the trade-off between occupancy estimation accuracy and the preservation of occupants’ visual privacy. The combination of iterative statistical deblurring and density estimation achieved a 16.29% counting error, outperforming our other proposed approaches while preserving occupants’ visual privacy to a certain extent. Our multifaceted approach aims to contribute to the field of occupancy estimation by proposing a solution that seeks to balance the trade-off between accuracy and privacy. While further research is needed to fully address this complex issue, our work provides insights and a step towards a more privacy-aware occupancy estimation system. 

One of the challenges in direct conversion Doppler radar lies in the dc offset resulted from antenna coupling. The dc offset may saturate the baseband amplifiers, preventing sufficient amplification of the received signal. In this work, a Coupling-Cancellation-Antenna (CCA) was implemented in the radar front end to enhance radar detection accuracy by minimizing the TX-RX antenna coupling. The idea is to have two transmitting antennas fed by signals with 180° phase difference such that the two signals cancel at the RX antenna. As a result, a larger receiver gain can be used to improve the signal to noise ratio without saturating the baseband output. Experimental validations of the CCA concept demonstrate 37-dB reduction in the TX-RX coupling. Furthermore, the CCA method reduces the detection error from 15.8% to 2.4%. 

Contact-based cardiac motion detection using Quadrature Doppler radar faces a challenge of the I/Q-formed non-arc constellation. In this work, a hypothesis is brought forward that such complicated constellation originates from not one, but two moving targets. The dual-motion model may very well explain that contact-based Doppler radar detects both atrium and ventricle motions during cardiac cycles. In this work, dual-motion simulation and phantom measurements are presented, verifying that the atrial-ventricular motions are the reason that I/Q baseband signals transcribe a complex non-arc constellation. It offers the first evidence that contact-based Doppler radar measures actual heart motion. 

In this paper a phase shifter based multi-arc circle fitting method was proposed to improve accuracy of Doppler radar remote motion sensing. Experiments were conducted to validate the approach by measuring displacement of 3 mm using 2.4 GHz quadrature continuous wave (CW) Doppler radar. It was demonstrated that mean error drops from 4.529% to 1.073% when multiple shifting arcs are utilized to enhance detection accuracy. A greater improvement in accuracy is observed when more arcs are applied.