DeepPIRATES: Enabling Deployment-Independent Supervised PIR-Based Localization

Among existing device-free localization (DFL) methods, the methods based on pyroelectric infrared (PIR) sensor networks are much promising due to their advantages of low cost and privacy protection. Recently, we proposed a deep-learning-based method PIRNet which much decreases the deployment density of PIR-based DFL methods in multi-person scenarios. However, since PIRNet utilizes an end-to-end neural network that receives all the deployed PIR sensors' signals as input for localization, it has a defect of deployment-dependence: it assumes the PIR sensors' deployment in the testing environment is same to the training environment. Otherwise, it requires to be retrained. To address this problem, in this paper, we propose a deployment-independent method DeepPIRATes, which can be applied in environments of any deployments without retraining. DeepPIRATES has the character of deployment-independence because it divides the localization task into two steps and only utilizes deep learning in the first step. Especially, the first step aims at estimating the information about the persons' relative locations to a PIR sensor. Therefore, the utilized neural network only needs to receive a single PIR sensor's signal as input and is independent to the sensors' deployment. In the second step, DeepPIRATES further infers the persons' absolute locations by a particle filter which fuses the predicted information about the persons' relative locations to each sensor and does not require training data. Through DeepPIRATes, we achieve average localization errors of 0.55m, 0.73m, and 0.88m in scenarios of 1-person, 2-persons, and 3-persons with a deployment density of 0.08 sensors/m2.