Simulation and Signal Processing of Through Wall

Simulation and Signal Processing of Through Wall UWB radar for Human Being's Periodic Motions Detection Jing Li*a,b, Fengshan Liua, Penglong Xua, Zhaofa Zengb a Applied Mathematics Research Center, Delaware State University, Dover, DE, USA; b College of Geo-exploration Science and Technology, Jilin University, Changchun, China ABSTRACT The human’s Micro-Doppler signatures resulting from breathing, arm, foot and other periodic motion can provide valuable information about the structure of the moving parts and may be used for identification and classification purposes. In this paper, we carry out simulate with FDTD method and through wall experiment with UWB radar for human being’s periodic motion detection. In addition, Advancements signal processing methods are presented to classify and to extract the human’s periodic motion characteristic information, such as Micro-Doppler shift and motion frequency. Firstly, we apply the Principal Component Analysis (PCA) with singular value decomposition (SVD) to denoise and extract the human motion signal. Then, we present the results base on the Hilbert-Huang transform (HHT) and the S transform to classify and to identify the human’s micro-Doppler shift characteristics. The results demonstrate that the combination of UWB radar and various processing methods has potential to detect human’s Doppler signatures effectively. Keywords: UWB Radar, Human periodic motion, FDTD Simulation, Signal Processing

INTRODUCTION Human localization and motion detection with ultra wideband (UWB) radar have attracted a lot of attention nowadays by many researchers due to its big impact on multitude applications, such as rescue missions, earthquake, surveillance, et al [1][2]. Unlike rigid targets commonly encountered in many radar applications, a human in motion is a complex non-rigid body. In addition to the main Doppler shift due to the human beings is the periodic motions such as breathing, arm swing and other body movement [3]. When the human target is exposed under the incidence of UWB electromagnetic wave source [4], radar signals reflected from human beings contain biometric information related to the periodic contraction of lungs, fluctuations of the body movements. The frequency or phase of the incident wave can be changed according to the characteristics of the periodic motions. We can extract the characteristics to identify and distinguish different human status which is reflected by the time frequency shift. It is helpful to better understand the activities of human objects and to enhance the detection capability. For example, in security surveillance, besides detecting the locations of the terrorists, the analysis through Micro-Doppler effect can be used to judge whether the terrorists carry any weapons or not. In rescuing applications, it is important to search earthquake survivors through their vital signs. In addition, we can also apply Doppler shift in monitoring the respiration of patients in hospitals. However, such micro-Doppler (m-D) signals are concealed within other naturally varying signals which make detection extremely difficult [5]. The different periodic motions have different micro-Doppler characteristics. In most of the papers relative to this subject, detection and recognition of the micro-Doppler effect are only based on the motion’s rhythm analysis. Fourier transform and more recently time frequency transforms (STFT) are used to analyze and to identify the different parts of the human body during the motion. Since the weak human being’s periodic motion signal, strong reflections from the walls, and the interference signals due to multiple reflections from the operator, other rescuers, and other objects, the above method can not obtain satisfy result for the Micro-Doppler shift research. In this paper, we mainly focus on identification and classification human being's periodic motions characteristics via FDTD simulation, through wall human detection experiment with GSSI radar with advance signal processing method. The remainder of this paper is organized as follows. In Section II, briefly describes the model building and FDTD numerical simulation. In Section III, the signal processing for human periodic motion simulation data with advancements method is presented. In Section IV, the through wall human detection experiments and data analysis are discussed. In Section V, we provide the conclusion with ideas for future work. Active and Passive Signatures IV, edited by G. Charmaine Gilbreath, Chadwick Todd Hawley, Proc. of SPIE Vol. 8734, 873402 · © 2013 SPIE · CCC code: 0277-786X/13/$18 · doi: 10.1117/12.2015284

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MODEL BUILDING AND FDTD NUMERICAL SIMULATION The FDTD method has a wide range application in GPR simulation. It can simulate complex, nonlinear or anisotropic media, solve step by step in time and have a good stability and convergence, as it is simple to illustrate and easy to understand [6-8]. Using the FDTD method to simulate the UWB radar response from a human’s cardio-respiratory movement, it mainly depends on the proper material physical properties and tempo-spatial discretization. The target of human’s breathing and heartbeat is used the slice of magnetic resonance image (MRI) of a human chest with 45 instants (or statuses). It contains two cycles of breathing (lung movement) and 11 cycles of heartbeat (heart deformation). Consequently, it gives a breathing frequency of 0.22 Hz and a heartbeat frequency of 1.22 Hz. The motion amplitude of breathing and heartbeat are set to be 5-15mm and 2-3mm, respectively [9]. We build the earthquake ruins model with two trapped human, which use the MRI slice to describe the two trapped human (Fig. 1a). We set the 2D computational domain with 3700 × 2000 uniform grid and Δx = Δy = Δz = 0.01m to simulate the model. We use the generic values of dielectric constants for air and wall, as well as the published values for human tissues [9]. The source is Ricker wavelet whose center frequency is 1 GHz which is placed on the ground surface, while two human chest models are trapped in the earthquake ruins, human 1 is face up and human 2 is face right. The total recording length is set to be 67ns, identical to that of the physical experiment, with a total of 33,500 time steps at the sampling interval of 0.002 ns.

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Figure 1. (a) Model of two human subjects buried in a collapsed building at an earthquake hazard site with a size of 3.74 m x 1.97 m. Also see Fig. 2 of [9]; (b) The snapshot of the FDTD simulation wave

DATA PROCESSING WITH CORRELATION ANALYSIS AND CURVELET TRANSFORM 1.1 Remove Background Clutter with Curvelet Transform It is hard to identify the reflection signal of the human targets from the original synthetic data (Fig.2a). Due to the human target signal is very weak, we need to apply effective signal enhancement techniques to remove the background clutter and not loss the target signal. Curvelet transform (CT) is an advanced multi-scale signal processing algorithm. The nature of Curvelet is that it efficiently represent a piecewise smooth curve which contain singularities normal to the direction of smoothness. This property is the precise nature of GPR data, which suggests that the Curvelet coefficients can provide a localized sparse representation where nonlinear operations, such as threshold, can be performed [10]. It combines the advantages of ridgelet and wavelet transforms which are good at expressing the characteristic of lines or points. The transform has improved directional capability and exhibits highly anisotropy, better ability to represent edges and other singularities along curves as compared to other traditional multi-scale transforms, e.g. wavelet transform [11]. The Fig.2b is the result after Curvelet transform which is showing the two trapped human’s position and signal energy distribution. It can sufficiently improve signal to noise radio (SNR) and give higher signal fidelity at the same time [18]. The result is beneficial for analyze further.

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Sample Number

Figure 2. (a)The original record in terms of the sampler number; (b) The curvelet transform to remove background signal [18].

1.2 Extract Human Target Signal with PCA Method As far as the statistical analysis is concerned, the Principal Component Analysis method (PCA) has been proven to be a beneficial technique for signal classification as well as relationship among variables, face recognition and in other fields [12], [13]. Yet it is the most frequently applied classification method for pre-processed GPR data [14]-[16]. Furthermore, PCA plays a significant role in the through-wall imaging, owing to its feature extraction property [17]. Contrary to that, in this paper, we apply the PCA method to classify and to extract the human periodic motion signal. The PCA is a linear transformation method based on the minimum mean square error. The nature of PCA is to use the special orthogonal matrix to carry out orthogonal transform for the original data and to obtain the diagonal component matrix. It can be described as:

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(1)

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The original data set X ( X ∈ R ) (where rows denote particular measurements and columns stand for data samples) is expressed by a new transformed data matrix S through the transformation matrix W. The rows of W represent eigenvectors principal components of X. S1 , S 2 , S3 ....., S n are the principal element of X. The transformation matrix W is the eigenvector of C = X • X . For the GPR data R, we use the SVD method, it can be defined as: T

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D1,1 ≥ D2,2 ≥ D3,3 .... ≥ DN , N . U and V are the eigenvector matrix for RRT ∈ R m×m and RRT ∈ R n×n , respectively. T In addition, the eigenvectors of U and V are also ranked decreasingly. Set yi = Di ,i vi as the principal element. The GPR signal matrix R can be defined as the weighted sum of the principal element yi and the characteristic signal ui , N

R = ∑ ui yi . The PCA method decomposes the signal into subspace which is composed by the maximum variance i =1

vectors. Due to the clutter signal's little change, the principal element is considered to be the same. Thus the rank of clutter main component matrix is 1. For the human periodic motion signal matrix, the matrix rank is greater than 1. The amplitude of the clutter is much higher than that of the target signal. The PCA method can reduce the main clutter and improve the SNR of the target signal matrix.

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Amplitude(A/m)

Through analyzing the energy distribution of each eigenvector, there are amplitude peaks in the clutter and the target signal. The PCA method can find the amplitude peaks of the target signal eigenvectors and extract the human life signal. Fig.3 is the human 2 breathing and heartbeat signal after using the PCA method.

Recording time(Sec) Figure 3: Extract the Human 2’s life signal with PCA method

1.3 Human Micro-Doppler Characteristic Analysis with HHT and GS Transform With the above processing step, we could obtain the human periodic motion signal in different recording time. In this section, the Hilbert-Huang transform (HHT) and S transform carried out onto the life signal to indentify and classify the human periodic motion characteristics, such as the frequency or Micro-Doppler shift. The HHT is a novel nonlinear and non-stationary signal analysis technique based on combination of the empirical mode decomposition (EMD) and the Hilbert spectral analysis (HAS) approaches [18]. The EMD separates the intrinsic mode functions (IMFs) from the original signal one by one, until the residue is monotonic. The original signal is thus decomposed into a finite and a small number of IMFs, where an IMF is any function with the same number of extremes and zero crossings, with symmetric envelopes. The time-frequency spectrum is obtained by using HHT to every IMF, which means different frequency scales in human’s micro-Doppler signals are decomposed in different IMF (Fig.4a). The c0 is the input signal and decomposed into c1-c6 IMFs which include different frequency information. Next, the IMFs can be transformed from the recording time domain to frequency domain by using the Hilbert transform. The result of HHT analysis for the synthetic simulation is shown in Fig.4b. It appears as though the signal is composed of broadband energy as the instantaneous frequencies are widely distributed. The breathing signal is clearly identified around 0.25 Hz and the frequency response is very stationary. In addition, a slight energy concentration can be found around 1.0-1.2Hz, which is likely associated with heartbeat but far from being a convincing evidence of the ability to detect heartbeat by this particular UWB radar system. The S transform (ST) has got the inspiration from the short time Fourier transform (STFT) based on Gaussian window as well as the scale of wavelet transform (WT), and become a new kind of time-frequency analysis methods absorbing the thoughts of the two methods [19]. When compared with STFT, the time-frequency resolution of ST can automatically adapt according to frequency, which overcomes the defect of fixed resolution of STFT, that is, high frequency has high time resolution and low frequency has high frequency resolution. When compared with WT, the time-frequency spectrum of ST has direct relation with Fourier spectrum and the basic transformation function needn’t satisfy the admissible condition, which overcomes the difficulty brought by time-scale spectrum of WT when interpreted. Due to the excellent capability of processing time-variant signal, ST is widely used in data processing [20]. The ST is proposed by Stockwell [19] here:

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THROUGH WALL HUMAN PERIODIC MOTIOM DETECTION EXPERIMENTAL In order to test the signal processing method in real GPR detection, we use the SIR-3000 radar from GSSI company to carry out the through wall human periodic motion detection experiment. The experimental system is a good choice for data accuracy and versatility, and it had been used in through wall human’s life signal detection with good results [21]. In this experiment, we use the horn antenna which has a center frequency of 2GHz and a frequency range from 1GHz to 4GHz. The human stands about 1.5m away from the wall with different periodic motions, such as stationary, swing arms and foot motion, respectively (Fig. 6). The wall thickness is 0.2m. The radar is set at one fixed position outside the room with 0.05m away from the wall. We set the antenna and human target in the fixed positions and use the time sample mode to collect the radar signal for about 40sec.

Figure 6: The through wall human periodic motion detection experiment We carry out the above signal processing method in the experiment data. Fig 7a is the GPR data after de-noising by CT method. From the result we can see that the human body reflection signal is at 1.5m. The signal is almost a straight line. The arm motion signal is in the range of 1.0-1.5m. Since swing arm is periodic motion, the signal is approximately in hyperbolic curve form and the reflection position is changed according to the human movement velocity. Meanwhile, when the speed of swing arm is faster, the human echo also becomes stronger. We can obtain the arm motion signal in Fig7b with the PCA method which reflects the arm motion characteristics. The HHT analysis result shows the Micro-Doppler shift of arm motion. The swing frequency is mainly in the range of 0.5-0.8Hz. The different motion speeds have obvious characteristics in the Micro-Doppler shift. The Fig .7c is the time and frequency characteristic result with S transform. From the result we can distinguish the human body frequency and arm motion frequency. The body frequency is about 0.2Hz and the signal is stationary, and the arm motion frequency is obviously reflected on the range of 0.40.6Hz. The energy is strong at the recording time between 10 Sec and 15 Sec. This phenomenon is also found in the HHT result.

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(c) Figure 7: The signal processing for the through wall human arm motion signal (a: The lab data after de-noising; b: The HHT for the life signal; c: The time and frequency analysis with S transform). The second experiment is the human foot motion. All parameters are same as above. Fig.8 is the processed result with the above method. In Fig. 8a we can see that the foot motion has regular reflection signal in the range of 1.2~1.5m. It has smaller range reflection than arm motion due to that the foot motion range is shorter than the swing arm. But, the signal amplitude and resolution of foot motion are stronger and higher than swing arm since the foot motion shift is violent. The results of HHT analysis and S transform show regular Micro-Doppler shift characteristic (Fig.8b and Fig.8c). It is changed according to the human’s motion status. If the human motion is faster, the amplitude and Micro-Doppler frequency is also stronger and higher. With the HHT analysis and the S transform, we not only obtain the different frequency characteristics but also get the whole Micro-Doppler shift variation characteristics.

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The third experiment is the human’s breathing motion test. It is aimed to use the method to extract the weak periodic motion characteristics. The human stands about 3ft (0.93m) from the wall. The other parameters are same as above. The human keeps the standard breathing status. Fig.9a is the real data after CT de-noise which clearly shows the periodic radar wave signal. From the HHT analysis and ST results (Fig.9b and Fig.9c) we can find that the breathing frequency is about 0.3Hz. The breathing Micro-Doppler shift has regular periodic pattern which is different from other body signal.

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CONCLUSION In this paper, the simulation and experiment for through wall human being’s periodic motion are developed. According to the Micro-Doppler shift characteristics of human periodic motion, we apply advance signal processing method to identify and to classify the signal characteristics. The Principal Component Analysis (PCA) with use singular value decomposition (SVD) is helpful to de-noise and extract the human life signal from the raw data. With the processing results, the Hilbert-Huang transform (HHT) and the S transform can classify the human’s micro-Doppler shift characteristic and motion frequency effectively. Human with different periodic motion would display different microDoppler shift and motion frequency information. The results show that the UWB radar with advance signal process methods can identify the human’s different Doppler signatures effectively. It is plausible to use this approach in human subject detection in complex media such as at the earthquake and the fire disaster sites.

ACKNOWLEDGEMENT The research work in this paper is supported by the US Army Research Laboratory and the US Army Research Office under cooperative agreement number W911NF-11-2-0046.

REFERENCES

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