Eugin Hyun

A meothod for multi-target range and velocity detection in automotive FMCW radar

FMCW(Frequency Modulation Continuous Wave) radar has many useful applications but a serious problems can occur in multi-target situations. Range-velocity processing should suppress so-called ghost targets and detect missing targets presented by beat frequency shift with Doppler frequency. In this paper, a new method is proposed for effective identification of the correct pairs of beat frequencies received from real targets.

A Pedestrian Detection Scheme Using a Coherent Phase Difference Method Based on 2D Range-Doppler FMCW Radar.

For an automotive pedestrian detection radar system, fast-ramp based 2D range-Doppler Frequency Modulated Continuous Wave (FMCW) radar is effective for distinguishing between moving targets and unwanted clutter. However, when a weak moving target such as a pedestrian exists together with strong clutter, the pedestrian may be masked by the side-lobe of the clutter even though they are notably separated in the Doppler dimension. To prevent this problem, one popular solution is the use of a windowing scheme with a weighting function. However, this method leads to a spread spectrum, so the pedestrian with weak signal power and slow Doppler may also be masked by the main-lobe of clutter. With a fast-ramp based FMCW radar, if the target is moving, the complex spectrum of the range- Fast Fourier Transform (FFT) is changed with a constant phase difference over ramps. In contrast, the clutter exhibits constant phase irrespective of the ramps. Based on this fact, in this paper we propose a pedestrian detection for highly cluttered environments using a coherent phase difference method. By detecting the coherent phase difference from the complex spectrum of the range-FFT, we first extract the range profile of the moving pedestrians. Then, through the Doppler FFT, we obtain the 2D range-Doppler map for only the pedestrian. To test the proposed detection scheme, we have developed a real-time data logging system with a 24 GHz FMCW transceiver. In laboratory tests, we verified that the signal processing results from the proposed method were much better than those expected from the conventional 2D FFT-based detection method.

Two-Step Moving Target Detection Algorithm for Automotive 77 GHz FMCW Radar

Today, 77GHz FMCW (Frequency Modulation Continuous Wave) radar sensors are used for automotive applications. In typical automotive radar, the target of interest is a moving target. Thus, to improve the detection probability and reduce the false alarm rate, an MTD(Moving Target Detection) algorithm should be required. This paper describes the proposed two-step MTD algorithm. The 1st MTD processing consists of a clutter cancellation step and a noise cancellation step. The two steps can cancel almost all clutter including stationary targets. However, clutter still remains among the interest beat frequencies detected during the 1st MTD and CFAR (Constant False Alarm) processing. Thus, in the 2nd MTD step, we remove the rest of the clutter with zero phase variation.

Automotive FMCW Radar with Adaptive Range Resolution

In this paper, the proposed technique can provide the range profile with adaptive range resolution for automotive frequency modulation continuous wave (FMCW radar.) For long-distance targets, the range is extracted with rough resolution using the basic FFT. The high resolution algorithm is used for near-distance targets, for which the collision probability is high.

Development of 24GHz FMCW level measurement radar system

In this paper, a 24 GHz FMCW level measurement system is developed. For low cost design, the PLL solution using a frequency synthesizer is employed instead of the traditional method based on a digital direct synthesizer. The developed transceiver can generate a frequency modulated wave with a high linearity over 2GHz bandwidth in order to support a high accuracy gauging. We also develop the prototype of a signal processing module in order to verify the developed transceiver module.

Design and Implementation of 24 GHz Multichannel FMCW Surveillance Radar with a Software-Reconfigurable Baseband

We designed and developed a 24 GHz surveillance FMCW (Frequency Modulated Continuous Wave) radar with a software-reconfigurable baseband. The developed radar system consists of transceiver, two selectable transmit antennas, eight parallel receive antennas, and a back-end module for data logging and to control the transceiver. The architecture of the developed radar system can support various waveforms, gain control of receive amplifiers, and allow the selection of two transmit antennas. To do this, we implemented the transceiver using a frequency synthesizer device and a two-step VGA (Variable Gain Amplifier) along with switch-controlled transmit antennas. To support high speed implementation features along with good flexibility, we developed a back-end module based on a FPGA (Field Programmable Gate Array) with a parallel architecture for the real-time data logging of the beat signals received from a multichannel 24 GHz transceiver. To verify the feasibility of the developed radar system, signal processing algorithms were implemented on a host PC. All measurements were carried out in an anechoic chamber to extract a 3D range-Doppler-angle map and target detections. We expect that the developed software-reconfigurable radar system will be useful in various surveillance applications.

A New OS-CFAR Detector Design

For radar systems, target detection procedures need to be conducted with a received signal amplitude that exceeds the CFAR (Constant False Alarm Rate) threshold. An Ordered Statistics CFAR (OS-CFAR), based on a sorting algorithm, is useful for an automotive radar system in a multi-target situation. However, it has a limitation in that it requires a higher computation effort than a typical Cell Averaging CFAR. In this paper, we propose a new OS-CFAR architecture that has a low computational effort. In the proposed approach, a single sorting step is performed as a pre-processing for the decision of the CFAR threshold, and this can reduce the computing time for the entire CFAR process. When the fast sorting algorithm is employed for OS-CFAR, the computing effort is always low compared with normal OS-CFAR architecture regardless of the data size.

Detection scheme for a partially occluded pedestrian based on occluded depth in lidar–radar sensor fusion

Abstract. Object detections are critical technologies for the safety of pedestrians and drivers in autonomous vehicles. Above all, occluded pedestrian detection is still a challenging topic. We propose a new detection scheme for occluded pedestrian detection by means of lidar–radar sensor fusion. In the proposed method, the lidar and radar regions of interest (RoIs) have been selected based on the respective sensor measurement. Occluded depth is a new means to determine whether an occluded target exists or not. The occluded depth is a region projected out by expanding the longitudinal distance with maintaining the angle formed by the outermost two end points of the lidar RoI. The occlusion RoI is the overlapped region made by superimposing the radar RoI and the occluded depth. The object within the occlusion RoI is detected by the radar measurement information and the occluded object is estimated as a pedestrian based on human Doppler distribution. Additionally, various experiments are performed in detecting a partially occluded pedestrian in outdoor as well as indoor environments. According to experimental results, the proposed sensor fusion scheme has much better detection performance compared to the case without our proposed method.

Multi-target tracking scheme using a track management table for automotive radar systems

In this paper, a multiple target tracking scheme for automotive radars is presented. First, a track table for efficient track management is proposed; then, a track management function is designed using a state machine. The proposed scheme can initiate, confirm, and delete all tracks of multiple targets. Moreover, it can determine whether the detected target is a real target or a ghost track, and a terminated track or a sudden missing track. The simulation results demonstrate that the proposed tracking scheme is processed normally.

A Low-Complexity Scheme for Partially Occluded Pedestrian Detection Using LIDAR-RADAR Sensor Fusion

Object detection has been researched using a camera, a LIDAR and a RADAR. However, camera-based techniques have heavy image processing and are sensitive for light intensity. LIDAR can measure precise distance from objects, but it is difficult to classify objects. Further, it is known that when pedestrians are partially occluded, detecting them is extremely difficult because of insufficient data to determine them. To address this problem, we use LIDAR and RADAR sensors to improve the detection accuracy. We present a sensor fusion scheme for detecting partially occluded pedestrian with low-complexity.