**2.2. Radar echo signal measurements**

The possibility to recognize pedestrians with a static radar sensor using the Doppler effect has been shown in [15]. A moving vehicle is equipped with an automotive radar sensor with a built-in feature extraction and classification to recognize pedestrians. The feature extraction in the backscattered radar echo signals resulting from superposition of the reflection points of an object is done automatically in the radar sensor signal processing. Detected targets are therefore tracked in the environment and an additional feature extraction and classification is performed.

To distinguish between the echo signal characteristics of pedestrians and vehicles, a target recognition model is described which is based on the specific **velocity profile** and **range profile** for each object separately [4]. The velocity profile describes the extension of the different velocities of an object measured by the radar sensor, while the range profile shows the physical expansion of a target.

In case of a longitudinally moving pedestrian, different reflection points at the trunk, arms and legs with different velocities are characteristic in radar propagation. Therefore an *extended* velocity profile will be observed in a single radar measurement of a pedestrian as the velocity resolution Δ*v* of the radar sensor is higher than the occuring velocities. Carrying out several measurements with a time duration of 39 ms each, a sinusoidal spreading and contraction of the velocity profile can be observed in the case of a pedestrian, due to the movement of arms and legs for example in the swing and stand-phase of the legs. For a laterally moving pedestrian, the velocity profile is less extended due to the moving direction of the pedestrian. Furthermore, the extension depends mainly on the azimuth angle under which the pedestrian is measured. In contrast, the radar echo signal in case of a vehicle shows a very narrow (*point shaped*) velocity profile due to a uniform motion.

Additionally, a *point shaped* range profile will occur in the case of a longitudinally or laterally moving pedestrian as the physical expansion is small compared to the range resolution of Δ*R* = 1.0 m. In contrast, a vehicle shows an *extended* range profile, due to several reflection points spaced in several range cells. The measurement result of a single observation is shown in the range Doppler diagram in Figure 4.

**Figure 3.** Range profile and velocity profile of a single measurement.

4 Will-be-set-by-IN-TECH

<sup>2</sup> · <sup>1</sup> *f*sweep

> <sup>2</sup> · <sup>1</sup> *T*CPI

(3)

(4)

<sup>Δ</sup>*<sup>R</sup>* <sup>=</sup> *<sup>c</sup>*

<sup>Δ</sup>*<sup>v</sup>* <sup>=</sup> <sup>−</sup> *<sup>λ</sup>*

The table below shows the system parameters of the automotive radar sensor in detail. Carrier Frequency *f*<sup>T</sup> = 24 GHz Sweep Bandwdith *f*sweep = 150 MHz Maximum Range *R*max = 200 m Range Resolution Δ*R* = 1 m Chirp Length *T*CPI = 39 ms Maximum Velocity *v*max = 250 km/h Velocity Resolution Δ*v* = 0.6 km/h

Classical UWB-Radar Sensors have a sweep bandwidth of *f*sweep = 2 GHz. Using such a bandwidth, a high range resolution is determined, which allows also pedestrian classification. The technical challenge in this chapter is to realize pedestrian recognition based on a 24 GHz radar sensor with a bandwidth of only 150 MHz. This sensor is used in automotive applications, therefore an extension of the signal processing in terms of pedestrian

The possibility to recognize pedestrians with a static radar sensor using the Doppler effect has been shown in [15]. A moving vehicle is equipped with an automotive radar sensor with a built-in feature extraction and classification to recognize pedestrians. The feature extraction in the backscattered radar echo signals resulting from superposition of the reflection points of an object is done automatically in the radar sensor signal processing. Detected targets are therefore tracked in the environment and an additional feature extraction and classification is

To distinguish between the echo signal characteristics of pedestrians and vehicles, a target recognition model is described which is based on the specific **velocity profile** and **range profile** for each object separately [4]. The velocity profile describes the extension of the different velocities of an object measured by the radar sensor, while the range profile shows

In case of a longitudinally moving pedestrian, different reflection points at the trunk, arms and legs with different velocities are characteristic in radar propagation. Therefore an *extended* velocity profile will be observed in a single radar measurement of a pedestrian as the velocity resolution Δ*v* of the radar sensor is higher than the occuring velocities. Carrying out several measurements with a time duration of 39 ms each, a sinusoidal spreading and contraction of the velocity profile can be observed in the case of a pedestrian, due to the movement of arms and legs for example in the swing and stand-phase of the legs. For a laterally moving

**Table 1.** 24 GHz Radar Sensor Parameters.

**2.2. Radar echo signal measurements**

the physical expansion of a target.

classification is desirable.

performed.

Under the use of an MFSK modulation signal, a range profile and the velocity profile can be extracted from a series of received signals as shown in Figure 4. As an example, four consecutive range and velocity measurements are depicted in a range Doppler diagram. The red dots show a longitudinally walking pedestrian, the blue crosses an in front moving vehicle. The figures depicted are based on radar measurements taken in an urban area with an ego speed of 50 km/h. It can be observed that neither velocity profile nor a range profile can be seen in the first measurement, consequently, those feature values are zero. In the second measurement, however, several range and velocity measurements allow to calculate an extended range profile for the vehicle and an extended velocity profile for the pedestrian.

**Figure 4.** Sequence of range and velocity measurements.

#### 6 Will-be-set-by-IN-TECH 246 Ultra-Wideband Radio Technologies for Communications, Localization and Sensor Applications Pedestrian Recognition Based on 24 GHz Radar Sensors <sup>7</sup>

The range profile and velocity profile features do not depend on the modulation signal. Solely, the range and velocity resolution must be smaller than the expected extension. For example, in the case of a continous wave modulation signal, the range profile can be read directly from the Fourier transformed radar echo signal and the velocity profile can be evaluated from the Doppler spectrum. Also, instead of these spectra or the frequency spectrum and phase difference analysis, it is possible to calculate the extension of an object in range and velocity on the basis of target lists by applying a detection algorithm. On this basis, an extended range profile with a point-shaped velocity profile can also be measured for a vehicle. For a pedestrian, the profiles remain vice versa. Figure 5 depicts this context.

**3.1. Classification based on a single radar measurement**

a single radar measurement of *T*CPI = 39 ms.

*3.1.1. Feature extraction*

Radar sensors provide continuously available measurement results in an interval of a few milliseconds. This interval is determined by the duration of the transmission signal *T*CPI = 39 ms in which a single MFSK signal is transmitted. The echo signal is downconverted and Fourier-transformed, which allows the described features to be extracted continuously. Rather than examinating a received sequence of radar echoes, this subchapter will initially focus on

Pedestrian Recognition Based on 24 GHz Radar Sensors 247

Automotive radar sensors are an important source of information for security and comfort systems. The information is measured in terms of range, radial velocity and signal level. However, information about the object types do not exist. To fill this gap, features from the available information are extracted, which describe the object types and allow a decision of the related class on the basis of measured sensor data. To describe a detected object, this signal processing step calculates a number of features, which are discriminant for measurements containing different object types and match for objects from the same type. Thereby, moderately separated features achieve even in a perfect classification algorithm only moderate or even poor results ([16], [17]). An ideal feature extractor on the other hand shows good classification performance by using simple linear classifiers. This is why the feature extraction is so important. For a distinct classification, transformation-invariant features are sought. Still, there is no recipe to determine a feature set and since each sensor type describes an object specifically and each task is different, the feature set for pedestrian recognition based on an automotive radar sensor is explained shortly. Figure 6 shows the feature extraction with

the specific object description in the context of the signal processing chain.

Feature

Models

**Figure 6.** Context of the feature extraction in the signal processing chain and the object description using

The basis for feature extraction are the velocity profile and the range profile of a detected object which has been described previously. The term of the profile describes the physical and kinematic dimensions of an object in the distance, angle and velocity. This can be measured in the case of multiple reflection points with different velocities greater than zero for any object. It can involve an extended or a point-shaped profile for the range and velocity depending on the type of expansion. On this basis, a number *n* of features can be calculated which describe the object in terms of a radar measurement. All *n* calculated real valued features *x*1, ..., *xn* are

saved in a feature vector *x* and build the basis for further signal processing steps.

extraction Classification

*x* = (*x*1, *x*2, ..., *xn*) *n* ∈ **N**, *x*<sup>i</sup> ∈ **R** (5)

Radar measurements

the range profile and velocity profile.

**Figure 5.** Range profile and velocity profile of a pedestrian and a vehicle.

The longitudinally and laterally moving pedestrians are classified as **pedestrians**, longitudinally and laterally moving vehicles as **vehicles**, all other signals received from objects such as parked cars, poles, trees and traffic signs are classified as **other** objects.
