The classification of personnel targets with different sizes of baggage has the potential to improve the efficiency of security checks in airports [
1,
2,
3]. For example, individuals with no bag, with a small bag, and with large luggage can be categorized and arranged into different places for security checks. In this paper, we investigate how to automatically achieve this goal with continuous wave (CW) radar sensors because of its numerous benefits, including privacy, the robustness against illumination, accurate persistent monitoring capabilities, no physical discomfort caused by wearing motion sensor devices or modifying human normal behavior, and its low cost compared to other sensors such as high-resolution video camera systems [
4,
5,
6]. When radar observes a moving target, the main Doppler shift of the received signal corresponds to the bulk motion of the target, while the micro-Doppler effect represents the motions of the parts of the target relative to its main body [
7,
8]. Based on this fact, the micro-Doppler effect can be used as a powerful tool for inferring the motion status of the target [
9].
There have been many studies published on classification systems based on micro-Doppler information. The classification among different kinds of targets was shown in References [
10,
11,
12]. The authors extracted very basic information to recognize walking humans using a spectral analysis with a simple classifier in Reference [
13]. In Reference [
14], the authors used a continuous wave radar to distinguish between different persons or other moving objects. In Reference [
15], the authors classified seven kinds of human activities by selecting six features from the time-frequency spectrogram. In Reference [
16], specific components of micro-Doppler gait signatures related to parts of the body at a long range for identification purposes was shown. In Reference [
17], armed/unarmed personnel targets were distinguished based on multi-static micro-Doppler signatures. The authors of Reference [
18] classified eight types of specific finer-grained human activities using through-wall stepped frequency continuous wave (SFCW) radar. An automatic procedure to detect, in real-time, the presence of one or several human subjects behind walls is discussed in Reference [
19]. The method in Reference [
20] is capable of recognizing men and women. All of the above methods are based on the empirical selection of features in the time-frequency domain with single band radar. Other approaches of feature selection include the linear predictive coding (LPC) [
21], the principal component analysis (PCA) [
22], the singular value decomposition (SVD) [
23], the empirical mode decomposition (EMD) [
24], the hierarchical image classification architecture (HICA) [
25], and the deep convolutional neural networks (DCNN) [
26,
27], etc. The existing literature indicates that, (1) the extraction of proper features is of great importance for accurate classification and (2) the appropriate features may vary with different applications.
In this paper, we focus on the classification of three kinds of human walking postures in a realistic manner, i.e., walking with no bag, walking with only one carry-on baggage by one hand, and walking with one carry-on baggage by one hand and one handbag by another hand, with both X-band and K-band radar sensors. Different from most of the existing methods that only use single-band radar, the proposed method uses two micro-Doppler radar sensors operating simultaneously in different bands for the classification of personnel targets with baggage. The time-frequency analysis is firstly performed and three features, i.e., the period, the Doppler offset, and the bandwidth, are extracted from the time-frequency spectrogram at each radar sensor. Since the human object is observed with dual-band radar sensors at the same time, the dimension of the extracted features is increased by feature fusion, which is helpful to improve the classification accuracy compared to using only single radar sensor [
28,
29]. The fused features are input into the one-versus-one support vector machine (SVM) to achieve the classification. The experimental results based on measured data demonstrate that the proposed method works well to classify the three gaits of interest and the classification accuracy using dual-band radar is higher than that of single radar operating alone, whether the single band radar is X-band radar alone or K-band radar alone.