Millimeter Wave High Resolution Radar Accuracy in Fog Conditions-Theory and Experimental Verification

Analysis of Perception Accuracy of Roadside Millimeter-Wave Radar for Traffic Risk Assessment and Early Warning Systems

Millimeter-wave (MMW) radar is essential in roadside traffic perception scenarios and traffic safety control. For traffic risk assessment and early warning systems, MMW radar provides real-time position and velocity measurements as a crucial source of dynamic risk information. However, due to MMW radar's measuring principle and hardware limitations, vehicle positioning errors are unavoidable, potentially causing misperception of the vehicle motion and interaction behavior. This paper analyzes the factors influencing the MMW radar positioning accuracy that are of major concern in the application of transportation systems. An analysis of the radar measuring principle and the distributions of the radar point cloud on the vehicle body under different scenarios are provided to determine the causes of the positioning error. Qualitative analyses of the radar positioning accuracy regarding radar installation height, radar sampling frequency, vehicle location, posture, and size are performed. The analyses are verified through simulated experiments. Based on the results, a general guideline for radar data processing in traffic risk assessment and early warning systems is proposed.

Investigating the effect of road lighting color temperature on road visibility in night foggy conditions

Night foggy road conditions limit visibility distance of drivers and are associated with higher accident and fatality rates than other weather conditions. Therefore, ensuring road visibility in night foggy road is critical. However, it is difficult to reproduce fog on a real road and only a few studies have researched foggy road conditions and visibility in a laboratory as a small scale. Previous studies have suggested that a color temperature of road lighting is related to visibility. However, many have only investigated the effects of relative transmittance in limited indoor experiments, and the impacts of differences in transmittance on visibility have thus far not been studied in real-scale conditions. In this study, a real-scale test involving 91 subjects was conducted to investigate how the visibility distance under night foggy conditions is affected by different lighting color temperatures. Based on the real scale experiments, the correlation between the visibility distance and lighting color temperature was derived. Road lighting with a low color temperature (i.e., yellow) was found to provide longer visibility distances than that with high color temperatures under night foggy conditions having measured visibility of approximately 102m. The impact of the differences in lighting color increased as the visibility distance decreased. In contrast, road lighting with a high color temperature (i.e., white) improved driver visibility in higher-visibility conditions. Therefore, this study confirmed the correlation between lighting color temperature and visibility distance for different visibility conditions and could serve as a foundation for the development of roadway design standards as well as future studies.

Detection of Low RCS Supersonic Flying Targets with a High-Resolution MMW Radar

In this study, the detection of a low radar cross-section (RCS) target moving at a very high speed using a high-resolution millimeter-wave radar is presented. This real-time detection is based on the transmission of a continuous wave and heterodyning of the received signal reflected from the moving target. This type of detection enables one to extract the object’s movement characteristics, such as velocity and position, while in motion and also to extract its physical characteristics. In this paper, we describe the detection of a fired bullet using a radar operating at an extremely high-frequency band. This allowed us to employ a low sampling rate which enabled the use of inexpensive and straightforward equipment, including the use of small antennas that allow velocity detection at high resolution and with low atmospheric absorption.

A Novel Multi-Input Bidirectional LSTM and HMM Based Approach for Target Recognition from Multi-Domain Radar Range Profiles

Radars, as active detection sensors, are known to play an important role in various intelligent devices. Target recognition based on high-resolution range profile (HRRP) is an important approach for radars to monitor interesting targets. Traditional recognition algorithms usually rely on a single feature, which makes it difficult to maintain the recognition performance. In this paper, 2-D sequence features from HRRP are extracted in various data domains such as time-frequency domain, time domain, and frequency domain. A novel target identification method is then proposed, by combining bidirectional Long Short-Term Memory (BLSTM) and a Hidden Markov Model (HMM), to learn these multi-domain sequence features. Specifically, we first extract multi-domain HRRP sequences. Next, a new multi-input BLSTM is proposed to learn these multi-domain HRRP sequences, which are then fed to a standard HMM classifier to learn multi-aspect features. Finally, the trained HMM is used to implement the recognition task. Extensive experiments are carried out on the publicly accessible, benchmark MSTAR database. Our proposed algorithm is shown to achieve an identification accuracy of over 91% with a lower false alarm rate and higher identification confidence, compared to several state-of-the-art techniques.