Classification of Human Motion Using Radar Micro-Doppler Signatures with Hidden Markov Models

PADAR, Mehmet Onur
ERTAN, Ali Erdem
Candan, Çağatay
One of the desirable features in a ground surveillance radar is to provide information about what a detected person is doing. This would give a law enforcement organization ability to detect suspicious activities remotely and act accordingly. Previously, micro-Doppler radar signatures from humans were shown to have the necessary features to make that distinction. Typically, micro-Doppler signal spectrograms are used to obtain features to classify what the person is doing. However, most of these techniques treat the spectrogram as an image, and obtain features through some image processing techniques. In this work, we propose the use of hidden Markov models as an alternative method to statistically model both instantaneous and correlated long-term variations within the micro-Doppler signal to classify a motion. In addition, we propose use of principle component analysis (PCA) as a data driven feature extraction approach that captures vital statistics of the input at a much reduced dimension. Experiments show that with the proposed methods, perfect classification of four different motions can be attained when training and testing set both contains data from same people, and 90% accuracy is obtained when training and testing set has data from different people.


Classification of human motion using radar micro-doppler signatures with hidden markov models
Padar, Mehmet Onur; Candan, Çağatay; Department of Electrical and Electronics Engineering (2016)
The detection and classification of a moving person is one of the important missions of a ground surveillance radar. Classification information gives the opportunity of announcing a warning message on the suspicious activity of detected person. The studies show that radar micro-Doppler signatures can be used to obtain the needed features to make the classification of different types of human motions. In general, spectrograms of micro-Doppler signals obtained from human in motion are used to analyze the nece...
Detection of mini/micro unmanned air vehicle (UAV) under clutter presence and environmental effects
Dere, İsmail Gökhan; Kuzuoğlu, Mustafa; Department of Electrical and Electronics Engineering (2019)
Detection, tracking and classification of Unmanned Air Vehicles (UAVs) is an emerging and crucial capability of radars in recent years. In the presence of clutter such as a crowded city or a foggy weather above sea surface, mini and micro UAVs become very difficult for radars to detect, track and classify. Classification information of UAV targets can be very useful for the critical infrastructures in order to provide security. Examined studies imply that kinematic and characteristic features such as Dopple...
Development of beam scheduling algorithm for frequency diverse array /
Çetiner, Ramazan; Demir, Şimşek; Department of Electrical and Electronics Engineering (2015)
Electronic scanning is the most desirable feature of radar systems. With electronic scanning, it is possible to steer the main beam of an array antenna instantaneously into a desired direction where no mechanical mechanism is involved in the scanning process. Electronic scanning methods including phase scanning, time delay scanning, and frequency scanning have been used in various radar applications; however new and cheaper scanning methods are still being investigated. It is the purpose of this thesis to i...
The Radar Application of Micro Doppler Features from Human Motions
ALEMDAROGLU, Ozge Topuz; Candan, Çağatay; Koç, Seyit Sencer (2015-05-15)
This study aims to experimentally investigate the feasibility of discriminating human motions with the help of micro Doppler features by using radar. In the first phase of the work, the synthetic data is generated through the human walking simulator by V. Chen and different time-frequency transformations are applied on the data and the results of the simulator are compared with field experiments. In the following phase, several field experiments which are in the scope of the simulator are conducted and the ...
Guldogan, M. B.; Gustafsson, F.; Orguner, Umut; Bjorklund, S.; Petersson, H.; Nezirovic, A. (2011-05-27)
Monitoring and tracking human activities around restricted areas is an important issue in security and surveillance applications. The movement of different parts of the human body generates unique micro-Doppler features which can be extracted effectively using joint time-frequency analysis. In this paper, we describe the simultaneous tracking of both location and micro-Doppler features of a human using particle filters (PF). The results obtained using the data from a 77 GHz radar prove the successful usage ...
Citation Formats
M. O. PADAR, A. E. ERTAN, and Ç. Candan, “Classification of Human Motion Using Radar Micro-Doppler Signatures with Hidden Markov Models,” 2016, Accessed: 00, 2020. [Online]. Available: