Phase-modulated continuous-wave (PMCW) radar is a promising technology for internet of things (IoT) applications due to its high resilience against interference and compatibility with communication systems. To address the computational burden in PMCW radar, conventional approaches accumulate sequences in the time domain to reduce the number of samples used for Doppler processing in velocity estimation. However, the direct summation of sequences without phase compensation leads to severe performance degradation caused by the Doppler effect, including velocity ambiguity and signal-to-noise ratio loss. Therefore, this paper proposes a low-complexity sequence accumulation method for detecting fast-moving targets. By adjusting the desired velocity search region, the proposed method simultaneously resolves the velocity ambiguity and reduces computational complexity. Simulation results show that the proposed method achieves a processing time approximately 3.5 times faster than the non-accumulation method. In addition, the proposed method compensates for the Doppler phase difference along the slow-time to enable coherent accumulation over sequences, thereby preventing performance degradation and achieving higher detection probability compared to conventional accumulation methods.

Wireless communication systems under high mobility and multipath conditions suffer from severe channel variations in time and frequency. To overcome this issue, this study extends a differential space-time line coding (DSTLC) scheme to an orthogonal frequency-division multiplexing framework, i.e., a differential space-time-frequency line-coded (DSTFLC) system. In the DSTFLC system, communication performance depends on how the DSTFLC symbols are arranged on the time-frequency resource grid. To address this issue, three resource grid mapping (RGM) schemes are proposed: time-first RGM, frequency-first RGM, and Gilbert (generalized Hilbert) RGM schemes. As verified by the numerical results, three RGM schemes exhibit distinct benefits under different channel conditions, namely, time-varying channels with short delay spreads, frequency-selective channels in low-mobility scenarios, and time-varying frequency-selective channels. The proposed RGM schemes would provide practical design guidelines for deploying various Internet of Things applications under dynamic channel conditions.

Integrated sensing and communication has emerged as a key paradigm in next-generation wireless systems. In orthogonal frequency-division multiplexing (OFDM)-based radar systems with time-division duplexing (TDD), the separation of downlink (DL) and uplink (UL) slots leads to spectral replicas along the Doppler axis, degrading sensing performance. To address this issue, we propose a convolutional long short-term memory autoencoder that predicts the channel state information (CSI) associated with unobserved UL slots between DL transmissions in a TDD frame. By estimating the CSI in unobserved UL slots, the proposed method effectively suppresses spectral replicas in the range-Doppler (RD) map. Simulation results under 3rd Generation Partnership Project New Radio numerologies (μ=1,2,3) demonstrate that the proposed model accurately predicts CSI values across diverse channel conditions. The model achieves a magnitude mean squared error (MSE) of 0.014 and a phase MSE of 0.038, consistently outperforming other methods. In addition, under equal parameter budgets, it achieves superior accuracy–efficiency trade-offs over alternative deep learning models, requiring only 7.2 ms per frame on a modern Internet-of-Things edge device. RD map analysis further confirms replica suppression, improving velocity coverage from 13.05 m/s to 613.5 m/s and signal-to-noise ratio from 45.16 dB to 60.91 dB compared with conventional methods, while maintaining target detection rates above 0.99. These results confirm that accurate CSI reconstruction by the proposed model significantly improves sensing fidelity in TDD-based OFDM radar systems.

In this paper, we propose a method to mitigate interference from phase-modulated continuous-wave (PMCW) signals in frequency-modulated continuous-wave (FMCW) systems. In dense automotive sensing environments, PMCW signals enter FMCW receivers and produce broadband distortions after dechirping, which degrades target detectability. To address this issue, we exploit the distinct characteristics of the FMCW signal and the PMCW-induced interference. Specifically, the FMCW signal exhibits a low-rank property in the Hankel domain, whereas the PMCW-induced interference spreads over multiple scales in the wavelet domain due to its chip-wise phase modulation. Based on these properties, we formulate an optimization problem that enforces a low-rank representation for the FMCW signal and applies wavelet-domain regularization to represent the PMCW-induced interference. To efficiently solve this problem, we adopt a solver based on the alternating direction method of multipliers. This solver updates the FMCW signal under a low-rank constraint in the Hankel domain and separates the PMCW-induced interference in the wavelet domain. Simulation results demonstrate that the proposed method outperforms conventional interference mitigation methods, with significant noise floor reduction in the range–velocity map and improved target detectability. As a result, the proposed method achieves a signal-to-interference-plus-noise ratio of 13.151 dB and a correlation coefficient of 0.9651 under strong interference.

We propose an artificial intelligence (AI)-based interference mitigation method to enable the coexistence of phase-modulated continuous wave (PMCW) and orthogonal frequency-division multiplexing (OFDM) systems in spectrum-sharing internet of things (IoT) environments. Existing mitigation techniques struggle to address the complex mutual interference arising from the structural mismatches of heterogeneous systems in dense IoT deployments. To overcome this challenge, we first formulate a signal model that accounts for PMCW–OFDM coexistence and characterizes bidirectional interference between the two systems. Subsequently, we propose a U-Net-based deep learning model that first applies time-domain thresholding as a pre-processing step. The model uses complex-valued inputs and an atrous spatial pyramid pooling bottleneck to capture multi-scale contextual features in the latent space, where ambiguous structures caused by interference can be distinguished from true target components. The proposed model is trained across various vehicular scenarios to enhance robustness under varying interference conditions. Simulation results for both systems under diverse vehicular scenarios demonstrate that the proposed method achieves signal-to-interference-plus-noise ratio improvements of up to 14.47 dB and masked peak signal-to-noise ratio improvements of up to 22.19 dB across various signal-to-noise ratios, compared to conventional baselines such as denoising convolutional neural network (DnCNN) and denoising diffusion probabilistic model (DDPM). In addition, the proposed method reduces latency by 12.5% and 58.7% compared to DnCNN and DDPM, respectively, indicating improved computational efficiency. These results highlight its potential for reliable sensing and offer spatial awareness to facilitate coexistence strategies in large-scale IoT networks.

Phase modulated continuous wave (PMCW) radar is one of promising options for the enhanced sensing capability and the multiple access availability. In this paper, we focus on the advantage of the high-resolution multi-input multi-output (MIMO) radar with fast-time code division multiplexing (CDM). First, we formulate the signal model of the fast-time CDM-MIMO PMCW radar by assigning different code sequences to each transmitter (Tx). We investigate the level of self-code interference induced by simultaneously transmitting Tx signals, as a function of the number of Tx and the type of sequence. For self-code interference cancellation, we propose compressive sensing-based greedy algorithms with significantly reduced computation complexity using the property of the circulant matrix. We show via simulation that the proposed methods can effectively mitigate self-code interference and generate radar images by implementing virtual antenna array with significantly enhanced peak to sidelobe ratio and range/angle estimation accuracy. Since the proposed method can fully leverage the advantages of CDM whose transmit signals share time-frequency resources, it can be utilized for high density Internet of Things networks and increase the fundamental performance of sensing while the reduced computations are suitable for the real-time operation.

This paper presents a method to associate detected objects between automotive vision sensors and radar sensors. Recently, studies on classifying objects by combining radar sensors and deep learning have been conducted. However, it is not easy to acquire labeled radar data to train deep learning models. Labeling these training data requires human effort, which is time-consuming. Furthermore, points obtained using radar sensors lack the ability to describe visual features such as the shape of objects, making the labeling process even more challenging. In this paper, we propose a method to annotate radar sensor data by associating the detection result of radar sensor with vision sensor using the iterative closest point algorithm. The proposed method can be used to annotate additional information such as object’s class information to radar data without human effort.

Urban air mobility (UAM) technologies are evolving rapidly, accompanied by a growing demand for advanced sensing and communication systems to enhance environmental awareness. The orthogonal frequency-division multiplexing (OFDM) radar-based joint radar and communication (JRC) systems have emerged as a key solution. The utilization of these systems in UAM applications allows for efficient use of limited frequency bands and eliminates the need for extra hardware installations. However, employing communication protocols such as long-term evolution presents a challenge due to the diminished range and velocity resolution in the OFDM radar system. Thus, a method for accurate estimation of range and velocity is necessary for OFDM radars. In this study, a geometric sequence decomposition-based accurate range and velocity estimation method is introduced. This method decomposes the received data into a superposition of geometric sequences, where each geometric sequence contains the propagation delay and Doppler shift (i.e., range and velocity of the target). Multiple simulations were conducted to validate the efficacy of our proposed method. When the signal-to-noise ratio is over 8 dB, the difference between the true and estimated range and velocity was less than the ± 1 m or ± 1 m/s. Furthermore, air-to-everything communications efficacy is validated based on the bit error rate analysis. According to the results, utilizing the proposed method can enhance the performance of the JRC system for the application in UAM.

This article explores the application of a stationary multiple-input and multiple-output frequency-modulated continuous-wave radar system for indoor environment mapping. In static situations, it is 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 to 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 root mean-square error (RMSE) of the estimated indoor map compared to the ground truth was 0.18 m.

In this paper, we propose methods to estimate the moving direction and size of a target vehicle based on point cloud data detected by high-resolution automotive radar sensor. Previous studies using automotive radar sensors have proposed methods to roughly estimate the moving direction of vehicles, such as left, straight, or right. This study proposes methods to estimate not only the specific moving direction but also the approximate size of the vehicle. First, we use the high-resolution frequency-modulated continuous wave radar to acquire point cloud data for vehicles moving at various angles. In the point cloud data, radar signals are strongly reflected from the side of the vehicle and detected as a line segment. The proposed moving direction and size estimation method is based on line segment extracted from the Hough transform (HT). To extract the line segment from the point cloud data, the Hough transform is used. Using the extracted line segment, methods for estimating the moving direction and size of the vehicle are proposed. And then, the quick hull algorithm is used to estimate the center point of the vehicle to match the position of the target in the coordinate system. Finally, the direction, width, and length of the vehicle estimated from the proposed methods show average errors of 1.94$^\circ$, 4.32%, and 6.32%, respectively. In addition, when compared to the conventional principal component analysis (PCA)-based method, our proposed method exhibits superior performance in terms of estimation accuracy. Moreover, we have validated the effectiveness of the proposed method even in scenarios with multiple vehicles and in noisy road environments.

We propose a drone classification method for polarimetric radar, based on convolutional neural network (CNN) and image processing methods. The proposed method improves drone classification accuracy when the micro-Doppler signature is very weak by the aspect angle. To utilize received polarimetric signal, we propose a novel image structure for three-channel image classification CNN. To reduce the size of data from four different polarization while securing high classification accuracy, an image processing method and structure are introduced. The data set is prepared for a three type of drone, with a polarimetric Ku-band frequency modulated continuous wave (FMCW) radar system. Proposed method is tested and verified in an anechoic chamber environment for fast evaluation. A famous CNN structure, GoogLeNet, is used to evaluate the effect of the proposed radar preprocessing. The result showed that the proposed method improved the accuracy from 89.9% to 99.8%, compared with single polarized micro-Doppler image. We compared the result from the proposed method with conventional polarimetric radar image structure and achieved similar accuracy while having half of full polarimetric data.

This paper presents an efficient mutual interference suppression method using wavelet denoising in automotive radar systems. When a radar signal transmitted from another radar-equipped vehicle flows into our radar system, it acts as an interference signal and degrades the target detection performance. With wavelet denoising, the interference signal can be extracted from the time-domain low-pass filter output. Then, the effect of the interference can be mitigated by subtracting the interference signal from the original low-pass filter output. As a result, the beat frequency containing the target information can be estimated accurately. Simulation results show that the proposed method can enhance the estimation accuracy of the target's distance, velocity, and angle. In addition, the performance of the proposed method is verified through actual experiments using heterogeneous radars. In the measurement results, even though the exact specifications of the radar signal transmitted from the other vehicle are not identified, the interference is effectively suppressed. Unlike other existing methods, the proposed method using wavelet denoising does not need to generate a specific radar waveform and it can mitigate interference only by signal processing without changing the existing hardware.

A 79-GHz frequency modulated continuous wave (FMCW) radar prototype for use in autonomous vehicles (Level-4), and its basic performance was evaluated. The system comprised 12 Tx and 16 Rx multiple-input-multiple-output (MIMO) antennas with high resolution in both the horizontal and vertical directions. The system was loaded on the vehicle, and the main functions, such as detection, tracking, and classification, were assessed via a road driving test. Furthermore, a grid map radar image was implemented to image a parked vehicle next to the road.

This paper proposes an efficient road structure recognition method using statistical characteristics of received signals in automotive frequency-modulated continuous wave radar systems. Generally, roads consist of various structures, some of which, such as tunnels and soundproof walls made of iron, generate undesired echoes, called clutter. When the clutter flows into the radar system, the target detection performance cannot be guaranteed completely. This causes great danger to the driver using the radar function such as adaptive cruise control. Thus, an efficient method to recognize the structures that deteriorate the radar detection performance is desired. Depending on the types of road structures, frequency components of the received signals have distinctive distributions. Focusing on this point, parameters that reflect statistical properties of each distribution are extracted. These parameters can be used as standards for the recognition because they show different values according to the road structures. For more enhanced recognition, we use a support vector machine method with a linear classifier or a Gaussian kernel, and the resulting confusion matrices are derived. According to the results, the proposed method successfully classifies the structures with high accuracy. If the recognition of the road structures that degrade radar's function is performed effectively, the safety of the driver in the radar-equipped vehicle can be ensured by applying additional signal processing or giving a warning message to the driver.

This study demonstrates a signal‐to‐noise ratio (SNR) analysis and estimation algorithm for efficient phase noise mitigation that can be practically applied to single‐carrier frequency‐domain‐equalisation (SC‐FDE) systems that operate in millimetre‐wave bands. First, the effect of phase noise in SC‐FDE systems is investigated on each of the packet reception processes, namely, channel estimation, SNR estimation, and data‐field reception. According to the analysis, an SNR estimation algorithm is proposed. The performance of minimum‐mean‐square‐error equalisation and conventional phase noise mitigation algorithm can be enhanced using the proposed SNR estimation. The effectiveness of the proposed analysis and SNR estimation algorithm is verified through the link‐level simulation. Compared with the conventional SNR estimation and the iterative phase noise mitigation algorithms, the proposed algorithm provides a lower packet‐error rate without any iterative decoding process.

In this paper, radar cross section (RCS) measurement for human subjects and vehicles in 77 GHz automotive frequency modulated continuous wave (FMCW) radar system is presented. In this system, it is impossible to utilize conventional RCS definition due to high frequency band and the modulation technique used. Therefore, we introduce a new parameter called pseudo-RCS that can replace the conventional RCS. Then, we conduct actual experiment in the road to measure the RCS of the human subjects and the vehicles. In the measurement, data of four human subjects and four different kinds of vehicles are recorded with the 77 GHz FMCW radar. From the actual measured data, we find RCS distributions of the human subjects and the vehicles. For the human subjects, the RCS values are distributed following the Nakagami distribution. On the other hand, the log-normal distribution is well fit for the RCS values in the case of the vehicle.