Deep CNN-LSTM network for indoor location estimation using analog signals of passive infrared sensors

Indoor localization is a crucial component of IoT applications in many areas, such as healthcare, energy management, and security control. Passive infrared (PIR) sensor has been employed for a location estimation due to its cost effectiveness, low power consumption, and low electromagnetic interference. Compared with its binary output, PIR analog output which is an output voltage generated by a PIR sensor when its sensing elements detect changes in temperature in an environment can provide more information regarding a person’s location. However, only a few works focus on using analog signals for location estimation. During the past several years, deep learning approaches have emerged and achieved outstanding results in many applications. In this article, we harness the power of deep learning and propose a deep CNN-LSTM architecture for PIR-based indoor location estimation. In our architecture, an upper CNN network can extract features from PIR analog output automatically while a lower LSTM network can learn temporal dependencies between the extracted features. To evaluate the feasibility and performance of our proposed method, we conduct four different sets of experiments. Our results show that the proposed method can efficiently handle complex cases and can achieve the mean distance error of 0.23 m, and 80% of distance errors are within 0.4 m.