Target Classification With a Radar System

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Target Classification with a Narrowband 24 GHz Radar System Hermann Rohling, Frank Kruse, Florian Fölster, Malte Ahrholdt Technical University of Hamburg-Harburg, Department of Telecommunications, Eißendorfer Straße 40, D-21073 Hamburg, phone: +49 (40) 42 878 – 3028, e-mail: { rohling | f.kruse | f.foelster | ahrholdt } Abstract-- Automotive radar systems offer the capability to measure extremely accurately target range, relative velocity, and azimuth angle for all objects inside the observation area. It is important that the target parameters can be measured simultaneously even in multiple target situations, which is a technical challenge for the waveform design. For future automotive applications like pedestrian safety systems, collision warning, turning-off and lanechanging assistance it will be necessary to have even more detailed information about the target type. Therefore, a target classification technique, which is purely based on the range profile measured by a so-called near distance radar sensor, is presented in this paper. Index terms-- object recognition, classification, automotive radar

I. INTRODUCTION Several radar sensors have been developed for different automotive applications. The general vision of automotive radar is shown in Figure 1 to explain the general view for future sensor development steps. To guarantee and realise a 360-degree security area surrounding an individual car several smart radar sensors are needed which are implemented in the front and rear bumper for example. Each sensor covers a certain azimuth angular area inside a maximum range of 60 m. Very powerful smart radar sensors have been developed recently which can be used for this task [1]. All present and future driver assistant systems will be based on such powerful and smart radar sensors. This security area is needed to avoid accidents and to increase traffic safety. Some important safety applications are currently under investigation like stop and go, stop and roll,

pre-crash, cut in situations etc. The detected objects and radar targets have an individual and different level of importance in the safety concept. It is therefore required in several street applications to distinguish between different object classes and to identify the observed targets. It would be a big step forward if the radar sensor is able to distinguish between cars and pedestrians. Then, a safety ring and a safety procedure can be established to avoid many accidents, where pedestrians are involved in. A target classification system is described in this paper, which has been tested successfully in real radar environment.

Figure 1: Full coverage security area surrounding an individual car

The technical task to distinguish between different radar targets based on a classification system is a real challenge. There are many publications about radar target classification

topics for several applications but the related performance figures in most of these applications are not convincing. It is therefore a real surprise that radar target classification procedures have been developed and tested successfully based on a 24 GHz radar network or even on a single radar sensor [2] applied to automotive applications.

II. NARROWBAND 24 GHZ RADAR SENSOR Research in radar target classification is based in this paper on a single 24 GHz monopulse radar sensor, which is described in detail in [1] and which has been integrated into the Technical University Hamburg-Harburg test car, see Figure 2. It is a licensed automotive radar in the 24 GHz band with a system bandwidth of 200 MHz only, which corresponds to a range resolution of 0.8 m. Therefore, this radar sensor is called Universal Medium Range Resolution (UMRR) system. The minimum range is 0.75 m and the maximum range is 60 m. Range and Doppler frequency are measured simultaneously by applying an FMCW waveform. The azimuth angle is measured with high accuracy by monopulse technique.

overlapping area of both antennas but it is not mandatory because each sensor measures already range, Doppler frequency and azimuth angle independently. Only a single smart radar sensor is used for target classification in this paper. In this case, all information from the received echo signal is analyzed in detail to distinguish between different radar objects or target types. The general objective is to distinguish between pedestrians and cars.

Figure 2: Multi Mode Monopuls Radar

Technical Data (narrowband mode) Bandwidth < 200 MHz Minimum Range 0.75 m Maximum Range 60 m + Cycle Time 25 ms Velocity Interval -25…+50 m/s Carrier Frequency 24.125 GHz Table 1: UMRR Technical Data [1] Figure 3: Radar Network

To establish a large safety area in front of a car two different radar sensors have been integrated behind the front bumper with only slightly overlapping antenna beams, see Figure 3. Each sensor measures the received signal independently, detects the targets and calculates the target parameter respectively. A sensor fusion technique can be applied in the

III. RADAR SENSOR BASED CLASSIFICATION The real challenging task for radar target classification is to classify the echo signals from pedestrians, cars and cyclists, which is shown in Figure 4. The radar sensor

functionality is completely unchanged but extended by the target classification procedure. Therefore, after target detection all echo signals are analyzed in detail to estimate and measure additional radar echo signal and target features, which can be used in the classification procedure. If the echo signal contains sufficient information, to distinguish between pedestrians and cars is still an open issue but has been validated for some radar sensors so far. Figure 4 shows briefly the general task of a radar target classification system to classify the echo signals coming from pedestrians, cars or cyclist.

a few features. This fluctuation consideration is determined by ten consecutive measurements of RCS values over time. The second feature group describes the Doppler frequency measurement over time. Again some fluctuations are analyzed on a statistical basis and described quantitatively by some features. An instance for a Doppler-related feature is the velocity dynamic over time. The third group considers signal features which describe the geometrical properties of any radar echo signal, like target extend etc. Geometric features depend in general on the expansion of the observed target. 100 90 80

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The main question of a target classification procedure is to generate features from the echo signal, which have sufficient information to characterize the different targets. The detailed range profile of a car and pedestrian is shown in Figure 5 and Figure 6 respectively. Theses single shot measurements can be the basis for some single shot echo signal features. It can be clearly seen that the range profile contains sufficient information to characterize the individual target type. The different features within a radar based classification system depend on the physical properties of each individual target class. For the considered single monopulse radar system the target features can be grouped into three characteristic groups. One group describes the radar cross section (RCS) aspect. The fluctuation of the received RCS values is measured on a statistical basis and described by






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Figure 5: Samples of received echo signals of a car

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Figure 4: General task for a radar target classification procedure

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Figure 6: Samples of received echo signals of a pedestrian

Starting with a single target feature, which has been calculated from the echo signal a histogram of this radar target specific feature distribution can be calculated. This result is shown as an example in Figure 7. The red

curve shows the feature distribution for the car, the green curve for the cyclist and the blue for the pedestrian respectively. It can be observed in this first example that a single feature is not sufficient in general for automotive target classification because the target specific feature value distribution overlaps too much and the classification rate will be too low in this case. Therefore, a multiple feature analysis has been established. Figure 7 again shows the situation for a two-feature situation case. The target specific feature value distribution is shown for the two considered features on the left. Both histograms show a strong overlapping situation. But already in a two-dimensional space it can be observed that it becomes much easier to find certain thresholds, which distinguish between the three radar target classes, see the scatter plot in Figure 7.

d = AΤ ⋅ x .


The final decision about the target type ωˆ is made by evaluating the maximum of the discriminator vector.

ωˆ = i | d i = max (d)


IV. EXPERIMENTAL RESULTS The target classification procedure has been implemented into a single UMRR radar sensor for tests in real target environments. A target classification technique has been developed, which considers three target types, cars, cyclists and pedestrians. One of the test scenarios is shown in Figure 8. Three different objects are inside the observation area of the UMRR sensor, a single car, a cyclist and a pedestrian respectively. The radar sensor has detected all three objects and calculated the target range, azimuth angle and radial velocity (Doppler frequency). After target detection, the radar target classification procedure has been applied to the feature vector described in this paper. A single result (snap shot) is shown in Figure 8. The car, the cyclist and the pedestrian echo signals have been classified correctly.

Figure 7: Feature distribution and two dimensional scatter plot

Finally, a set of 12 different features has been established. A classification system [3] has been applied, which uses these features in a two-step procedure. In a first step all feature vectors are labeled by the radar target type. This information is used in the learning period to calculate a coefficient matrix A for the mapping procedure between feature vector x and radar target type. Figure 8: Target classification result

During the classification phase, this coefficient matrix A is applied to all generated feature vectors x to determine a discriminator vector

The overall performance of this target classification system is shown in Figure 9. In a 90 s time frame the car and the cyclist were on

a fixed position (stationary targets). Only the pedestrian was walking from the radar sensor position up to a distance of 35 m including a few short stops. The pedestrian run back later to the radar sensor position quickly. The range measurement accuracy and target classification performance is shown in Figure 9. Range accuracy calculated by the UMRR is quite large for all considered targets. The car (red dots) is classified correctly in almost all signal echo cases. The vulnerable road users pedestrian person (blue dots) and cyclist (green dots) belong to the same layer of safety importance. Therefore each classification error between these two classes can be neglected from a system point of view.

In a realistic test environment it was shown and demonstrated experimentally that the developed target classification system is able to distinguish successfully between the radar echo signals coming from pedestrian and cars. All these measurements were based on a single UMRR monopulse radar.




There are only a very few radar echo signal situations where the pedestrian and the cyclist are observed on the same range. In these cases the target classification system made some errors and decided in such radar echo signals for a car. This is indicated in Figure 9 by the red colored dots. 40 35

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Figure 9: Classification result. Decision for a car is indicated by a red dot and for a person by a blue dot

V. CONCLUSION It has been shown in this paper that the radar target classification procedure has an extremely high performance for automotive applications.

[4] [5]

Mende, Ralph; Marc, Behrens; Meinecke, Marc-Michael; Bartels, Arne; To, Thanh-Binh: “The UMRR-S: A HighPerformance 24 GHz Multi Mode Automotive Radar Sensor for Comfort and Safety Applications”, International Radar Symposium IRS 2003, Dresden, Germany, 2003 Kruse, Frank; Fölster, Florian; Ahrholdt, Malte; Meinecke, Marc-Michael; Rohling, Hermann: “Object Classification with Automotive Radar”, International Radar Symposium IRS 2003, Dresden, Germany, 2003 Ahrholdt, Malte; Rohling, Hermann: “Characterization of Aerospace CFRP Structures by an Automatic Classification System”, 8th European Conference on Non-Destructive Testing, Barcelona, 2002 Klotz, Michael; Rohling, Hermann: “A 24GHz Short Range Radar Network for Automotive Applications”, CIE International Conference on Radar, Beijing, 2001 Oprişan, Dan; Rohling, Hermann: “Tracking Systems for Automotive Radar Networks”, IEE Radar 2002, Edinburgh, 2002

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