In this paper, we propose a method for performing the simultaneous localization and mapping (SLAM) in indoor environments using only multiple-input and multiple-output frequency-modulated continuous wave radar. The SLAM is a technology that maps the surrounding environment and position of a platform simultaneously. The following are the steps of the overall the SLAM implementation of the proposed method. First, the ego-velocity of the platform is estimated using the relative velocity with respect to stationary objects. In this case, a random sample consensus algorithm is used in the velocity-angle map generated from the detection results to identify stationary objects. Second, the rotation angle of the platform is estimated using the linear component extracted from the walls in the xy plane. Then, we determine the ego-motion of the platform using the estimated ego-velocity and rotation angle. Finally, we map the position of the platform and the indoor environment simultaneously in the absolute coordinate system. We validate the mapping result generated using our proposed method by comparing it with the ground truth-based mapping result.

In this paper, we propose a method of classifying the overhead target in automotive radar systems. The conventional automotive radar systems struggle to distinguish overhead road structures (e.g., bridges, overpasses, traffic signs) and ground-level targets (e.g., vehicles and pedestrians) due to the lack of elevation resolution. Consequently, the forward collision-avoidance assist (FCA) system or the autonomous emergency braking (AEB) system, may perceives detected overhead road structures as being on the road. Therefore, unnecessary operation of the FCA or AEB may occur. Our proposed method exploits the different patterns of changes in the estimated range, relative velocity, and signal amplitude as the radar approaches the overhead structure and ground-level targets. These patterns can be used to classify overhead structures by designing the appropriate classifier. Therefore, additional antenna elements arranged in the vertical direction required for elevation angle estimation are unnecessary. First, we obtain the radar signals by measuring the metal reflector installed at different heights. Then, by processing the received signal, we generate the input data for the proposed bidirectional long short-term memory-based height classifier. Our proposed method can classify the installation height of the reflector with an average accuracy of 98.18% within the range of 13 m, and an average accuracy of 94.97% within the range of 20 m in a complicated environment.

This paper explores the application of a stationary multiple-input multiple-output frequency-modulated continuous-wave radar system for indoor environment mapping. In static situations, it’s challenging to create indoor maps using fixed radar systems because only a few strong reflection points are prominently visible in the detection results. Our proposed method uses a stationary radar system, leveraging multipath echoes of radar signals to produce detailed indoor maps. Ghost targets caused by multipath propagation of signals contain information about the geometry of propagation paths. Unlike existing studies that primarily aimed to mitigate ghost targets, our method uses the information provided by both real and ghost targets to improve the mapping of indoor environments. For this purpose, we first extract detected peaks in the range-azimuth domain and compare the estimated direction of arrival (DoA) and direction of departure (DoD) to identify ghost targets and real targets. Then, the positions of paired real and ghost targets are used to estimate the geometry of reflection points, aiding in the creation of maps for surrounding indoor environments. Finally, we use density based spatial clustering of applications with noise algorithm and running average filter and eliminate outliers, improving the accuracy and clarity of the generated indoor map. Simulation and experimentation results validate the effectiveness of the proposed method. The average RMSE of the estimated indoor map compared to the ground truth was 0.18 m.

This paper proposes a method for estimating the number of sources using the time-domain received signal in frequency-modulated continuous-wave radar systems. Conventional approaches typically estimate the number of sources in the frequency-domain via the fast Fourier transform (FFT) on the received signals. However, these conventional methods face challenges in distinguishing targets that have identical distance and velocity, resulting in an underestimation of the source count on the range-Doppler map derived by applying FFT to the received signal. Consequently, this underestimation can lead to inaccurate results in subspace-based direction-of-arrival (DOA) estimation algorithms. In contrast, our method directly uses the received signal in the time-domain, which can distinguish between targets that have identical distance and velocity. We introduce a complex-valued neural network that estimates the number of sources using signal vectors extracted from analog-to-digital converter samples of a received signal matrix. The proposed method achieved an average accuracy of 91% in various simulations and experiments, and its effectiveness was verified through comparison with conventional source estimation algorithms. In addition, we can also obtain accurate results in the subspace-based DOA estimation algorithms using the number of sources estimated by the proposed method.

In radar-based air-writing, the continuous measurements of the hand movements may result in the addition of unnecessary strokes for certain characters or digits (e.g., 4 and 5), making it difficult to accurately recognize the air-written results when observed by human eyes. Therefore, we propose a deep neural network (DNN)-based classifier designed to identify unnecessary strokes and clutter that arise during the radar-based air-writing. First, while air-writing the digits from 0 to 9, the range, angle, and signal amplitude of the hand movement are obtained through a radar system. Then, we represent the hand’s trajectory in the form of x and y coordinates by using the information of range and angle. Next, we train the DNN-based classifier using the acquired x and y coordinates, signal amplitude, and frame index as input features. To ensure the classifier’s performance would not be impacted by changes in the position and size of the air-writing area, we apply the normalization to x and y coordinates. Finally, the performance of the classifier is verified using the results of air-writing digits from 0 to 9. The proposed method identifies unnecessary strokes and clutter regardless of the position and size of the air-writing area, demonstrating an average classification accuracy of 94.57%. Furthermore, when the classifier was validated with different individuals conducting the air-writing, the classifier exhibited an average classification accuracy of 93.9%.