A Human Gait Classification Method Based on Radar Doppler Spectrograms

Abstract

An image classification technique, which has recently been introduced for visual pattern recognition, is successfully applied for human gait classification based on radar Doppler signatures depicted in the time-frequency domain. The proposed method has three processing stages. The first two stages are designed to extract Doppler features that can effectively characterize human motion based on the nature of arm swings, and the third stage performs classification. Three types of arm motion are considered: free-arm swings, one-arm confined swings, and no-arm swings. The last two arm motions can be indicative of a human carrying objects or a person in stressed situations. The paper discusses the different steps of the proposed method for extracting distinctive Doppler features and demonstrates their contributions to the final and desirable classification rates.

Classification of Unarmed/Armed Personnel Using the NetRAD Multistatic Radar for Micro-Doppler and Singular Value Decomposition Features

“…The differences between the armed and unarmed case is easily noticeable by eye for both monostatic and bistatic data, in particular the fact that in the armed case the micro-Doppler signature is more compressed around the main torso contribution at around 20 Hz, without the peaks associated to the free swinging movements of the limbs. This is valuable information, as it has been shown that limited and confined limbs movement may be linked to people carrying potentially hostile objects, or to the presence of injured people or hostages [12,13]. These differences between spectrograms in the armed and unarmed cases will be numerically quantified and converted into features to use as input to a classifier.…”

Aspect angle dependence and multistatic data fusion for micro‐Doppler classification of armed/unarmed personnel

“…In the latter case the person had both arms free to swing as in natural walking. The use of micro-Doppler to distinguish between free and confined arms swinging may be of interest, as confined arms and reduced limbs movement could be related to people carrying potentially hostile objects, or to the presence of hostages or injured people [25][26][27]. Walking on the spot removes the main Doppler shift contribution from the micro-Doppler signatures, and ensures that targets remain within the main beam of the transmitting and receiving antennas during the recording.…”

Analysis of polarimetric multistatic human micro-Doppler classification of armed/unarmed personnel

“…The use of micro-Doppler to distinguish between free and confined arms swinging may be of interest, as confined arms and reduced limbs movement could be related to people carrying potentially hostile objects or to the presence of hostages or injured people. This type of analysis can exploit positive and negative micro-Doppler caused by the arms and its periodicity, and was the core contribution of these works [11][12][13] and investigated in our previous paper [14]. These observations seem to disagree with those proposed in [15], where it is claimed that carrying objects with one or both hands does not change the micro-Doppler signature for a walking person.…”