Programming language

Convolutional neural network

Philosophy

Artificial intelligence

Set (abstract data type)

Remote sensing

Telecommunications

Deep learning

Feature (linguistics)

Geology

Continuous-wave radar

Biology

Word error rate

Data set

Computer science

Botany

Radar imaging

Linguistics

Feature extraction

Real-time computing

Identification (biology)

Doppler radar

Computer vision

Radar

IEEE Transactions on Geoscience and Remote Sensing

Contemporary surveillance systems mainly use video cameras as their primary sensor. However, video cameras possess fundamental deficiencies, such as the inability to handle low-light environments, poor weather conditions, and concealing clothing. In contrast, radar devices are able to sense in pitch-dark environments and to see through walls. In this paper, we investigate the use of micro-Doppler (MD) signatures retrieved from a low-power radar device to identify a set of persons based on their gait characteristics. To that end, we propose a robust feature learning approach based on deep convolutional neural networks. Given that we aim at providing a solution for a real-world problem, people are allowed to walk around freely in two different rooms. In this setting, the IDentification with Radar data data set is constructed and published, consisting of 150 min of annotated MD data equally spread over five targets. Through experiments, we investigate the effectiveness of both the Doppler and time dimension, showing that our approach achieves a classification error rate of 24.70% on the validation set and 21.54% on the test set for the five targets used. When experimenting with larger time windows, we are able to further lower the error rate.

Indoor Person Identification Using a Low-Power FMCW Radar