Analysis of the Radar Doppler Signature of a Moving Human

Traian Dogaru*, Calvin Le and Getachew Kirose

U.S. Army Research Laboratory, Adelphi, MD 20783

Email: tdogaru@arl.army.mil

Abstract

In this paper we perform an analysis of the Doppler signature of a moving human. We investigate ways to use the Doppler spectrum in human discrimination or motion classification problems. The analysis is based on computer models that simulate the operation of a pulse-Doppler radar. Our goal is to distinguish patterns in the Doppler spectrograms that are characteristic to a certain human motion type. One problem we study is the possibility of detecting whether a moving human carries a weapon. Our approach is based on the ratio between the cross- and co-polarization signatures, which is significantly enhanced in the presence of a rifle-like object. We also attempt to discriminate a moving human from a moving dog based on the Doppler signature. While it is relatively easy to distinguish a walking human from a walking dog, it is shown that a crawling human presents a similar Doppler spectrum as the dog (although some subtle differences still exist).

1. Introduction

The U.S. Army Research Laboratory (ARL) has investigated the problem of radar detection and classification of moving humans, dating back to the Vietnam War era [1]. Using the Doppler spectrum of the radar response represents a common approach for detecting moving targets concealed behind obstacles, such as vegetation or building structures. The interest in this technology has been recently renewed by large-scale research and development efforts conducted by defense agencies in sensing through the wall (STTW) and foliage penetration (FOPEN) radar sensors. The major challenge with this approach is that any moving objects (such as blowing leaves, household appliances, etc.) or animals present in the scene can produce a Doppler response, thereby creating false alarms. In order to reliably discriminate human movers from other types of movers, we need to perform a more complex analysis of the Doppler signature and extract features characteristic to a certain target. Moreover, such analysis may enable us to extract biometric features of a person (for instance, tall vs. short person, or weapon-carrier vs. non-weapon-carrier).

Over the last decade, the Radio Frequency (RF) Signal Processing and Modeling Branch at ARL has made a significant investment in both electromagnetic modeling tools as well as wideband radar measurement instruments. We have applied both of these to the problem of detecting humans concealed behind obstacles. ARL is currently involved in three major defense programs related to sensing through the wall and foliage penetration radar, where the moving human detection and recognition problem is a key component.

In some preliminary work [2-4] we have thoroughly analyzed the radar signature of humans in static configurations, for various positions and radar parameters, based on computer simulations. These studies provided us with a wealth of information on the human radar cross-section variability with regard to body type, position, aspect angle and frequency. The next step consists of analyzing the radar response from a moving human, with the hope to extract certain features that allow classification of humans versus other movers. This problem has received an early interest within the defense research community, as

demonstrated by work done at the Harry Diamond Laboratories (now ARL) in the 1970s [1]. The initial approach consisted of using the Doppler spectrum for classification purposes. More recently, researchers started focusing on understanding the temporal changes in the Doppler spectrum via time-frequency analysis techniques [5-8]. Our previous work in this area attempted to relate features of the Doppler spectrograms to the underlying electromagnetic (EM) scattering phenomenology [9].

In this paper we present Doppler spectrograms of a walking human and show how they change with the incidence angle and frequencies (Section 3). Also, we propose Doppler-spectrum based techniques for discriminating moving humans from animals, and classifying different human motion types. More specifically, we look at the following problems: finding whether the walking human carries a weapon (Section 4); and discriminating a moving human from a moving dog (Section 5). It is important to emphasize that radar measurements were not available for all the cases considered in this paper (all our results were produced through computer modeling). This limited our ability to demonstrate the classification algorithms, since the computer modeling results are always perfectly repeatable, while the number of different scenarios under investigation is also limited. However, radar measurements performed at ARL in the case of a walking human [10] are in very good agreement with our models.

2. Modeling Approach

In this paper, we simulate the operation of a pulse-Doppler radar [11], where the responses from successive transmitted pulses are processed together in order to extract the Doppler frequency shift. In our computer models, the moving target is frozen in time for the duration of each pulse. Thus, we need to decompose the target’s motion into frames that succeed each other with the radar’s pulse repetition frequency (PRF). Then we use an EM solver to compute the radar return for a given excitation pulse, for each frame.

We start the computer modeling of a human with the “fit man” mesh in the basic standing position. This mesh was introduced in [1]. It describes only the outer shell of the human body, so we must assume that the body is made of a uniform dielectric material. We picked r = 50 and = 1 S/m for the body material, which are close to the skin dielectric properties. In references [1] and [3] we compared the uniform dielectric model of a human body with the full model (where each different tissue is assigned the actual permittivity) and concluded that both models produce very similar radar cross section (RCS) in the frequency bands of interest. In Section 5 we also introduce the mesh of a dog. Although the dielectric properties of the dog body tissues were not available to us, we utilized the same dielectric constant and conductivity as in the human case. Throughout the entire paper, we placed the targets of interest in the open space (i.e., not behind walls or other obstacles).

A software package named Maya (produced by Autodesk, Inc. [12]) allowed us to articulate these meshes in various body positions. Moreover, Maya can create realistic animation of a human or animal in motion by interpolating the mesh in an arbitrary number of frames between only a few reference positions. In Figure 1 we represented several frames of the walking human mesh obtained with Maya. In our study, we typically considered 80 frames per motion cycle. The PRF (or number of frames per second) represents the total number of frames in a cycle divided by the cycle duration.

Figure 1. Successive frames of the fit man mesh in walking motion, created by the Maya software.

The radar signature of each frame is computed using AFDTD. This is a computational electromagnetic (CEM) code entirely developed at ARL for radar signature calculations, based on the Finite Difference Time Domain (FDTD) technique [13]. The FDTD meshes that we used in these simulations have a resolution (cell size) of 5 mm, which is sufficient for frequencies up to 1 GHz (for more details see [1]). We also need to mention that all the calculations in this report are performed in the far-field, and involve plane-wave excitation at a given set of incidence angles. In all cases presented in this paper, the depression angle (measured from the x-y plane) is 0°.

For the Doppler analysis in this study we use relatively narrowband excitation pulses. The bandwidth (BW) is 80 MHz and the center frequency (fc) is 1 GHz (typical for STTW radar applications). The EM scattering data is obtained in the frequency domain and the pulse is synthesized back to the time domain by using an appropriate frequency window. We sample the returned narrowband pulse corresponding to each frame in order to obtain the in-phase (I) channel data (all the pulses need to be sampled at the same moment in time relative to a fixed reference). We also obtain the quadrature (Q) channel data by sampling the Hilbert transform [14] of the returned narrowband pulse described above. One requirement for this type of analysis is that the entire motion cycle be contained inside one down-range bin, such that the sampling instant “catches” some part of each pulse received during the cycle.

The I-Q data is used in extracting the Doppler frequency shift information by taking Fourier transforms. Throughout our paper, we present two approaches to the Doppler signature analysis of a target. In one approach, we compute the Fourier transform of the entire data in one motion cycle (notice that integrating data from more than one cycle does not add any information, since the modeled data repeats itself exactly cycle-by-cycle). Prior to the Fourier transform, we window the I-Q data in the time domain using a Hanning window. We then look at the magnitude of the Fourier transform as a function of Doppler frequency (or velocity).

In the other approach, we are interested in the time variation of the Doppler spectrum and employ short-time Fourier transforms (STFT) [14] that use only part of the I-Q data sequence at a time. The length of

Figure 2. Schematic diagram showing the steps involved in obtaining Doppler spectrograms of a walking human based on computer models.

the time window in the STFT is called the coherent processing interval (CPI), or dwell time, and is typically just a fraction of a motion cycle period. Since we use a Hanning window [14] in the time domain, we list the effective CPI (which is the value listed for each of the cases considered in this report) as half the time interval corresponding to the window length. We use the maximum possible overlap between the time-domain windows by setting the shift between two successive windows at only one slow time sample. Since we consider the target motion as cyclical, we can wrap around the I-Q sequence corresponding to one cycle into an infinite loop, thus making sure that there are no spurious jumps in the STFT data at the beginning and end of the cycle. The sequence of STFTs is arranged in a matrix format (with slow time variation by rows and frequency variation by columns) and the magnitude is plotted as a two-dimensional pseudo-color map (in dB scale), also known as the spectrogram. The abscissa represents the slow time, whereas the ordinate represents the velocity (which is proportional to the Doppler frequency shift). One spectrogram typically represents four walking cycles, in which the data generated by the first cycle is simply repeated another three times.

The entire process of creating Doppler spectrograms is illustrated in Figure 2. More details on this procedure can be found in [9]. All the electromagnetic modeling of radar signatures was run on high-performance computing (HPC) platforms at the ARL Major Shared Resource Center (MSRC), while desktop personal computers (PC) were used for processing the meshes and creating the spectrograms. The signal processing routines were implemented in MATLAB.

3. Doppler Spectrogram Samples

In the first example we consider a human walking straight toward the radar (which means 0° azimuth). In this section, we consider vertical-vertical (V-V) polarization. As mentioned before, the radar operates around 1 GHz, with a bandwidth is 80 MHz, CPI = 0.3 seconds and PRF = 40 Hz. Also, we consider that the human advances at an average velocity of 0.6 m/s and a walking cycle takes 2 seconds. The spectrogram is shown in Figure 3.

Figure 3. Spectrogram of the human body in walking motion (directly towards the radar) at 1 GHz, showing various walking cycle parameters, as well as the velocity of a point on the human’s chest.

The most striking pattern that we notice in the spectrogram in Figure 3 is the zigzag formed by the high-intensity (red) feature in the middle of the diagram. It is easy to prove that this feature closely follows the velocity of a point on the human’s chest. Thus, in Figure 3, we overlaid a thin black line representing the velocity of the point marked on the human’s chest (as obtained directly from the mesh files). The important conclusion is that the velocity of the human’s torso is not constant during walking, but it accelerates and decelerates according to the pattern visible in Figure 3. If we draw a line through the middle of this pattern, we obtain the average velocity, which is about 0.6 m/s. As shown in [9], the main contribution to the radar return (at these angles and frequencies), which creates the highest intensity feature in the spectrogram, comes from the human’s torso. The lower intensity “spikes” (high velocity features) that we notice at the upper edge of the spectrogram represent the arms and legs contribution. As expected, there are instances when their velocity is higher than that of the torso, but the radar return is generally weaker.

If we change the azimuth incidence angle (corresponding to a different walking direction with respect to the radar line of sight), we expect the Doppler spectrogram to change, both in terms of absolute velocities (proportional with cos) and the qualitative appearance of the temporal patterns (since the EM scattering phenomenology changes, as explained in [9]). For example, Figure 4 presents the Doppler spectrograms obtained for = 30° (a) and = 90° (b). Whereas we can still distinguish the zigzag pattern in the spectrogram in Figure 4a (notice that the two half-cycles become asymmetric in this case), the spectrogram in Figure 4b has a very different character (in that case, there is no radial component of the average velocity). The conclusion is that any attempt to classify the type of human motion based on the Doppler spectrograms must take into account the significant changes in these spectrograms with the aspect angle.

(b)(a)

Figure 4. Doppler spectrograms of the walking human for (a) = 30° and (b) = 90°.

In Figure 5 we consider head-on incidence, but the radar operates in the UHF band around 300 MHz (with a bandwidth of 40 MHz). This frequency range is more characteristic to FOPEN radar. Qualitatively, the spectrogram looks very different from the one obtained at 1 GHz (Figure 3). The phenomenology of the scattering was described in more detail in [9]. Essentially, at these relatively large wavelengths, the various body parts are coupled by the EM waves, so their contributions to the spectrograms are difficult to separate. The bright flashes in the spectrogram in Figure 5 correspond to

frames where the backscattered energy peaks out (it turns out that, for those particular frames, the leg and arm contribution is dominant). In such a case, separating spectrogram features corresponding to scattering from various body parts becomes a much more difficult task.

Figure 5. Spectrogram of the human body in walking motion (directly towards the radar) at 300 MHz.

4. Detecting a Human Carrying a Weapon

In this section, we explore the idea of discriminating a moving human carrying a weapon from one who does not carry a weapon. It has previously been suggested that this could be accomplished by analyzing the Doppler spectrogram of a walking human, by exploiting the fact that, when the human holds a weapon, one or both arms stop swinging. However, while the arm swinging motion can be clearly detected in a Doppler spectrogram, its absence does not necessarily indicate the presence of a weapon (the human may walk with the hands in the pockets, for instance). We propose a more reliable weapon detection scheme based on polarimetric techniques.

We consider the case of a walking human carrying an AK47 rifle in the port-arm position (Figure 6a). Our rifle discrimination method relies on the fact that the cross-polarization response of the tilted rifle is much stronger than that of the human body. At the same time, the co-polarization return from the human body is not significantly altered by the presence of the rifle. This can be inferred from the plots shown in Figure 6, where we compare the radar cross section (RCS) of the human (standing still) carrying the rifle to that of the human without the rifle, for V-V polarization (b) and V-H polarization (c). In order to discriminate the two scenarios, one would take the ratio (difference in dB) between the cross- and co-polarization signatures and compare that to a threshold (established through some calibration procedure). Although it is obvious that at 0° azimuth (when the human faces the radar) the cross- to co-polarization ratio is much larger for the human carrying the rifle (since the human himself is mostly symmetric at this aspect angle, making its cross-polarization signature very low), the difference between the ratios in the

two scenarios is still significant for most other aspect angles (Figure 6d). Notice that, for azimuth angles beyond -90° or 90°, there is not much difference between the two scenarios, because for those angles the rifle is masked by the body. It should also be mentioned that the strong cross-polarization signature of the rifle holds for virtually any rifle orientation (tilt angle).

(a) (b)

(c) (d)

Figure 6. RCS of a human with or without a rifle at 1 GHz. (a) human with AK47 mesh. (b) RCS for V-V polarization. (c) RCS for V-H polarization. (d) RCS ratio between V-H and V-V.

In the context of a Doppler radar, we perform the same type of analysis as in the previous sections, by modeling the frame-by-frame radar return from a human in walking motion. The human walks straight at the radar (the azimuth angle is 0°). The motion of the human carrying the AK47 differs from that of the free walking motion in Section 3 only by the fact that the arms are fixed, as shown in Figure 7a (the rest of the body, including the head, torso and legs, has the same position for each frame). In this section, we simply compute the cross- to co-polarization ratio of the Fourier transform magnitudes (there is no need to analyze the entire spectrogram). The length of the Fourier transform is not critical in this case – we

consider the I-Q data characterizing one full walking cycle. All the other radar parameters are identical to those in Section 3. The plots in Figure 7 show the Doppler spectrum for V-V polarization (b), the V-H polarization (c) and the difference (in dB) between the V-H and V-V magnitudes (d), for both the walking human carrying a rifle and not carrying a rifle. Notice that, before we plot the difference between V-H and V-V magnitudes, we threshold out the spectrum data below -40 dB (for V-V) and -55 dB (for V-H), in order to eliminate spurious data points that characterize the difference between very low signal levels. The graphs in Figure 7c show that, for the main part of the Doppler spectrum (between 0 and 1 m/s), the V-H to V-V ratio is significantly larger for the human carrying a rifle than for the human without a rifle. However, the Doppler response at the edges of the spectrum (between -0.5 to 0 m/s and between 1 and 2 m/s) shows a larger V-H to V-V ratio for the human without a rifle, which can be explained by the dominance of the swinging arm response in those regions of the spectrum.

(a) (b)

(c) (d)

Figure 7. Doppler spectrum of a walking human with or without a rifle. (a) mesh of the walking human with AK47. (b) Doppler spectrum for V-V polarization. (c) Doppler spectrum for V-H polarization. (d) Doppler spectra ratio between V-H and V-V.

To conclude this section, we would like to remark that this weapon discrimination technique does not rely on some particularities of the Doppler spectrum, but is based on the strength differential between the cross- and co-polarization signatures. As such, a simple measurement of the scattering matrix in a stationary scenario would be sufficient. However, since this paper focuses on Doppler radar, we showed how such a system could be used to detect whether the moving target carries a rifle. Also, we would like to recognize the fact that such a technique can only work for objects with large aspect ratios such as a rifle, but it would not be effective on smaller weapons, such as handguns.

5. Discrimination of a Moving Human from a Moving Dog

Another goal of our study is to investigate whether the Doppler signature can be utilized to discriminate a moving human from some other type of mover, such as an animal. For this purpose we simulated the motion of a walking dog and computed the frame-by-frame radar return from this target (the azimuth angle is 0°). We compared the Doppler spectrum obtained through the procedure outlined in Section 2 with that of a walking human and a crawling human, respectively.

In Figure 8a we show the Doppler spectrogram characterizing the dog’s walking motion. In this case, we considered that a dog walking cycle takes 1 sec, and we plotted 8 cycles along the slow time scale. The radar parameters are similar to those in Section 3 (fc = 1 GHz, BW = 80 MHz, CPI = 0.15 sec, PRF = 80 Hz), and the polarization is V-V. The spectrogram in Figure 8a has a more uniform structure than the one characterizing a walking human (Figure 8b). The dog average speed is about 0.2 m/s, with most velocity components confined between -0.3 and 1 m/s. The relatively bright spots in the spectrogram (occurring twice for every cycle) correspond to the frames where the front legs are aligned vertically - in that geometry, the radar return is enhanced.

Although we can notice obvious differences between the walking dog and walking man Doppler spectrograms, we also look at the case of a crawling man (Figure 8c), which has more similarities with the walking dog. The Doppler spectrogram of the crawling human has a cycle of 2 sec, and an average velocity of 0.3 m/s (fc = 1 GHz, BW = 80 MHz, CPI = 0.3 sec, PRF = 40 Hz, V-V polarization). Notice that, although the oscillations in the spectrograms in Figures 8a and 8c have different frequencies (due to different durations of a motion cycle), a single cycle of the spectrogram looks similar in the two cases.

The differences between the three cases (walking man, crawling man and walking dog) can be recognized more easily if we plot the magnitude of the Fourier transform of the I-Q data characterizing a complete motion cycle (Figure 9). Clearly, the walking human displays the largest bandwidth, since there are high velocity components associated with the arm and leg motion (notice also that the entire spectrum peaks at higher velocity, since the average speed is higher here than for the other types of motion). On the other hand, the Doppler spectra of the crawling man and the walking dog look very similar, and have much sharper roll-offs than the walking man. That can be explained by the fact that in the former two cases, the limbs do not swing at velocities much higher than the rest of the body. However, we notice that the dog displays a Doppler spectrum with slightly slower roll-off than the crawling human, which indicates that its legs move faster than the crawling human’s limbs in order to achieve a similar average velocity. One metric that could be used to discriminate a dog from a crawling human is the “shape factor” of the Doppler spectrum (with the dog generally displaying a larger shape factor).

(a)

(b)

(c)

Figure 8. Doppler spectrograms of (a) a walking dog; (b) a walking human; and (c) a crawling human.

Figure 9. Doppler spectra obtained from one motion cycle of (a) a walking dog; (b) a crawling human; and (c) a walking human.

6. Conclusions

The current state-of-the-art radar technology for detecting human targets concealed behind obstacles relies on the Doppler signature of the human motion. In order to be able to discriminate human targets from other moving entities present in the scene, classify human motion or infer certain biometric features of the target, an analysis of the Doppler spectrum is necessary. In some cases, analyzing one realization of the Doppler spectrum over a relatively long period of time can produce sufficient information for certain human detection or classification problems. In other cases, a more complex time-frequency analysis of the Doppler signature is necessary in order to reveal subtle temporal patterns in the Doppler spectrum that characterize a certain type of motion. Our paper demonstrates these techniques based on realistic computer models of humans or animals in various motion types.

One of our main objectives was to understand the phenomenological aspects of the radar Doppler signature of a moving human. More specifically, we looked at how various components of the spectrogram map onto the motion of human body parts and how do the spectrograms modified as a function of the radar parameters (incidence angle and frequency). We concluded that the Doppler spectrograms change dramatically with the aspect angle, especially as this angle is farther from head-on incidence. Also, at low radar frequencies (UHF band), the contribution of different body parts to the spectrogram cannot be clearly separated, since the various scattering centers are coupled when the wavelength is large.

Another problem we tackled was detecting whether a walking human carries a rifle. Our approach was based on the difference in polarimetric response created by the presence of the rifle. More specifically, the cross-polarization response of a human carrying a rifle is much stronger than that of a human not carrying a rifle. The two scenarios can be discriminated by computing the cross- to co-polarization ratio of the radar signature. We demonstrated that by looking both at the RCS of a stationary human, and at the

Doppler spectrum of a walking human.

Finally, we studied the Doppler signatures of a moving human versus a moving dog. The results, presented both as Doppler spectra and Doppler spectrograms, show clear differences between a walking dog and a walking human, but display more similarities between the walking dog and a crawling human. However, a more careful analysis of the Doppler spectra reveals that, in the dog case, the spectrum has a larger shape factor than in the crawling man case, which is indicative of faster limb motion for the dog. It is important to emphasize that all these results need to be verified by experimental radar measurements. Nevertheless, we believe that the methods outlined in this paper, based on computer simulated data, offer important ideas on how to approach these discrimination problems in a Doppler radar context.

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