Abstract
Pyroelectric infrared (PIR) sensors are much promising for device-free localization (DFL) due to their advantages of lower cost, low power consumption, and privacy protection. Most PIR-based localization methods usually assume some geometric models according to the detection principle of PIR sensors, which are however not accurate or robust due to the various cases of infrared radiation from human body, especially the case of multiple persons. Recently, deep learning is utilized in the PIR-based localization method (i.e. PIRNet Yang et al.) and well handles the complex infrared radiation even in the multi-person case. However, this method requires a high training cost, and has very weak generalization ability as it assumes the PIR sensors' deployment in the testing environment is same to the deployment in training environment. To reduce the training cost and achieve high generalization ability, in this paper, we propose a robust method DeepPIRATES, which can be directly utilized in various deployment scenarios without retraining. DeepPIRATES combines deep learning and a geometric model. Specifically, DeepPIRATES divides the localization task into two steps. The first step utilizes a neural network to estimate the azimuth changes of multiple persons to a PIR sensor. Then, DeepPIRATES utilizes the persons' azimuth changes to infer their locations based on a geometric model. Extensive experimental results show that DeepPIRATES can achieve similar localization accuracy as PIRNet, while does not require to be retrained when the sensor deployment changes.
Highlights
Along with the booming requirement of the location-based service, device-free localization (DFL) has received much attention due to the advantage that the target does not need to carry any devices
The price of a pyroelectric infrared (PIR) sensor is less than 1 dollar, and the power consumption of a PIR sensor is at the microamp level [5]
Different from PIRATES, the step of azimuth change estimation of DeepPIRATES is realized through a neural network
Summary
Along with the booming requirement of the location-based service, device-free localization (DFL) has received much attention due to the advantage that the target does not need to carry any devices. We propose another deep-learning-based method named DeepPIRATES which has a much lower training cost than PIRNet. Compared with PIRNet, DeepPI-. Different from PIRATES, the step of azimuth change estimation of DeepPIRATES is realized through a neural network. To estimate the azimuth changes corresponding to a PIR sensor named ‘sensor A’, the neural network needs the signal of ‘sensor A’ and is independent with the deployment of other PIR sensors. To further improve the generalization ability of the neural network for azimuth change estimation, we propose a new data augmentation strategy which is based on adversarial learning. (3) The proposed method can obtain a similar localization accuracy as PIRNet, while do not need to retrain the model when the sensor deployment changes The contributions of this paper are as following: (1) a deep neural network architecture is proposed to predict the azimuth changes of multiple persons to a PIR sensor and a geometrical method is proposed to localize multiple persons through their azimuth changes. (2) an adversarial-learningbased data augmentation strategy for the task of PIR-based localization is proposed. (3) The proposed method can obtain a similar localization accuracy as PIRNet, while do not need to retrain the model when the sensor deployment changes
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
