In this paper, a vision-assisted multipath recognition and suppression method is proposed for the problem of millimeter wave (mmWave) radar producing false targets under the influence of multipath interference. First, object detection is performed on the image and rectangular clustering is performed on the mmWave radar point cloud to complete the data preprocessing. Subsequently, nearest-neighbor frame matching and direct linear transform (DLT) algorithms are used to achieve spatio-temporal calibration of the two sensors. An axial adaptive cost-normalized matching algorithm is then proposed to associate targets from the two sensors, thereby establishing target association pairs. Finally, multipath ghosts in mmWave radar are recognized and suppressed based on the target association results. Experimental results show that the proposed method efficiently recognizes and suppresses multipath ghosts in traffic scenarios.

Non-line-of-sight (NLOS) detection is crucial in applications such as autonomous driving and surveillance. This paper proposes a bistatic multiple-input multiple-output (MIMO) radar-based joint estimation algorithm to localize diffraction corners and estimate NLOS targets. By leveraging the direction of departure (DoD) and direction of arrival (DoA) of diffraction signal, the algorithm first estimates corner position. Furthermore, target motion state is estimated based on the estimated corner and Doppler information. Electromagnetic simulations confirm the accuracy and robustness of the proposed method under various noise conditions.

This paper solves the through-wall imaging (TWI) problem with a narrow-band system, and proposes an adaptive TWI method based on data fusion of multiple scan paths. First, we use a Wentzel-Kramers-Brillouin-based (WKB-based) approximation to model the interaction of the transmitted wave with the unknown area. Then we use Radon inverse transform to reconstruct the image from the received signal strength data of different paths. Furthermore, we evaluate the impact of scan paths on imaging. Finally, finite-difference time-domain (FDTD) simulation results demonstrate the validity of proposed method.

This paper deals with the tracking problems for multiple human targets hidden behind the wall using through-wall imaging radar (TWIR). We propose a robust tracking algorithm in image domain, combining mean-shift algorithm with Kalman filter. Comparing with the traditional mean-shift algorithm, the proposed algorithm has a greater performance in multiple human targets tracking, especially considering the case of the temporary loss of target. Real data validates the robustness of the proposed algorithm.

This paper considers the location problem for Moving targets behind a corner. Exploiting multi-path and the algorithm based on phase comparison among the multiple channels can obtain the position of the target behind a corner. To localize the moving target, a scanning radar system with multiple channels is suggested. The false target range can be achieved by the fast Fourier transform(FFT) technique. In addition, the false target azimuth is derived via exploiting the phase differences between the return signals among the multiple channels. Due to false targets and real targets are geometric symmetry, true targets can be localized by the radar system. Finally the experiment results validate this method, and demonstrate the effectiveness.