This letter proposes a joint discrete Fourier transform (DFT)-estimation of signal parameters via rotational invariance techniques (ESPRIT) estimator for time-of-arrival (TOA) and direction-of-arrival (DOA) in vehicle frequency-modulated continuous-wave (FMCW) radars. Since the vehicle FMCW radar should recognize vehicles in the side/rear area when the driver initiates a lane change, the estimation of the joint TOA/DOA between the radar and targets is an important issue for solving complicated location tasks. However, conventional joint estimation methods such as 2D-ESPRIT and 2D-multiple signal classification (MUSIC) cannot be adopted for real-time implementation due to their high computational loads. To satisfy the required accuracy specifications and reduce complexity compared with the conventional estimator, we propose a low-complexity joint TOA and DOA estimator that uses the combined DFT-ESPRIT algorithm for FMCW radars. The performance of the proposed estimation in multitarget environments was derived and compared with the Monte Carlo simulation results. The root-mean-square error (RMSE) of the proposed method was compared with that of 2D-ESPRIT with various parameters. To verify the performance of the proposed combination method, we implemented the FMCW radar and verified its performance in an anechoic chamber environment.

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The aim of this study was to assess the personality traits of young male patients with atopic dermatitis (AD), and to examine the correlations between temperament and character dimensions with clinical and other psychological factors. Fifty young adult male AD patients and 83 healthy controls were examined using the temperament and character inventory, the Beck depression inventory and the state-trait anxiety inventory. The AD patients scored higher on harm avoidance and lower on reward dependence, self-directedness and cooperativeness than the healthy controls. The illness duration and anxiety correlated negatively with the self-directedness score, and depression correlated negatively with reward dependence and the persistence scores in AD patients. These results suggest that AD patients have distinctive temperament and character dimensions compared to healthy controls. Moreover, illness duration and anxiety might be associated with some personality problems, and some temperament dimensions (e.g. reward dependence, persistence) may be linked to depressive symptoms in AD patients.

We propose a novel discrete Fourier transform (DFT)-based direction of arrival (DOA) estimation by a virtual array extension using simple multiplications for frequency modulated continuous wave (FMCW) radar. DFT-based DOA estimation is usually employed in radar systems because it provides the advantage of low complexity for real-time signal processing. In order to enhance the resolution of DOA estimation or to decrease the missing detection probability, it is essential to have a considerable number of channel signals. However, due to constraints of space and cost, it is not easy to increase the number of channel signals. In order to address this issue, we increase the number of effective channel signals by generating virtual channel signals using simple multiplications of the given channel signals. The increase in channel signals allows the proposed scheme to detect DOA more accurately than the conventional scheme while using the same number of channel signals. Simulation results show that the proposed scheme achieves improved DOA estimation compared to the conventional DFT-based method. Furthermore, the effectiveness of the proposed scheme in a practical environment is verified through the experiment.

We propose and implement a low-complexity dual-chirp FMCW radar system for surveillance applications. The FMCW radar is suitable for the detection of various positions of multiple targets for the monitoring of concealed humans. For a surveillance FMCW radar system, it is necessary to distinguish between stationary and moving targets while maintaining a low level of complexity. However, conventional FMCW radar systems are very complex with many chirps to distinguish between stationary and moving targets. Even in hardware with low complexity levels, in order to distinguish between a stationary and a moving target, the proposed algorithm employs only dual chirps. Experimental results show that the proposed algorithm can effectively distinguish between moving and stationary targets despite its low complexity and low-level hardware.

This paper proposes a low-complexity frequency-modulated continuous wave (FMCW) surveillance radar algorithm using random dual chirps in order to overcome the blind-speed problem and reduce the computational complexity. In surveillance radar algorithm, the most widely used moving target indicator (MTI) algorithm is proposed to effectively remove clutter. However, the MTI algorithm has a so-called ‘blind-speed problem’ that cannot detect a target of a specific velocity. In this paper, we try to solve the blind-speed problem of MTI algorithm by randomly selecting two beat signals selected for MTI for each frame. To further reduce the redundant complexity, the proposed algorithm first performs one-dimensional fast Fourier transform (FFT) for range detection and performs multidimensional FFT only when it is determined that a target exists at each frame. The simulation results show that despite low complexity, the proposed algorithm detects moving targets well by avoiding the problem of blind speed. Furthermore, the effectiveness of the proposed algorithm was verified by performing an experiment using the FMCW radar system in a real environment.

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This paper proposes a high-efficiency super-resolution frequency-modulated continuous-wave (FMCW) radar algorithm based on estimation by fast Fourier transform (FFT). In FMCW radar systems, the maximum number of samples is generally determined by the maximum detectable distance. However, targets are often closer than the maximum detectable distance. In this case, even if the number of samples is reduced, the ranges of targets can be estimated without degrading the performance. Based on this property, the proposed algorithm adaptively selects the number of samples used as input to the super-resolution algorithm depends on the coarsely estimated ranges of targets using the FFT. The proposed algorithm employs the reduced samples by the estimated distance by FFT as input to the super resolution algorithm instead of the maximum number of samples set by the maximum detectable distance. By doing so, the proposed algorithm achieves the similar performance of the conventional multiple signal classification algorithm (MUSIC), which is a representative of the super resolution algorithms while the performance does not degrade. Simulation results demonstrate the feasibility and performance improvement provided by the proposed algorithm; that is, the proposed algorithm achieves average complexity reduction of 88% compared to the conventional MUSIC algorithm while achieving its similar performance. Moreover, the improvement provided by the proposed algorithm was verified in practical conditions, as evidenced by our experimental results.