In this study, we propose an accurate range-Doppler analysis algorithm for moving multiple objects in a short range using microwave (including millimeter wave) radars. As a promising Doppler analysis for the above model, we previously proposed a weighted kernel density (WKD) estimator algorithm, which overcomes several disadvantages in coherent integration based methods, such as a trade-off between temporal and frequency resolutions. However, in handling multiple objects like human body, it is difficult to maintain the accuracy of the Doppler velocity estimation, because there are multiple responses from multiple parts of object, like human body, incurring inaccuracies in range or Doppler velocity estimation. To address this issue, we propose an iterative algorithm by exploiting an output of the WKD algorithm. Three-dimensional numerical analysis, assuming a human body model in motion, and experimental tests demonstrate that the proposed algorithm provides more accurate, high-resolution range-Doppler velocity profiles than the original WKD algorithm, without increasing computational complexity. Particularly, the simulation results show that the cumulative probabilities of range errors within 10mm, and Doppler velocity error within 0.1m/s are enhanced from 34% (by the former method) to 63% (by the proposed method).

In this paper, we propose a cross-correlation method applied to multistatic ground penetrating radar (GPR) data sets to detect road pavement damage. Pavement cracks and delamination cause variations in electromagnetic wave propagation. The proposed method can detect velocity change using cross-correlation of data traces at different times. An artificially damaged airport taxiway model was measured, and the method captures the positions of damaged parts.

For automotive millimeter radar, a method using a multi-input multi-output (MIMO) array antenna is essential for high angle resolution with module miniaturization. MIMO enables us to extend an antenna array with virtual antennas, and a large antenna array aperture enables high resolution angle estimation. Time division multiplex (TDM) MIMO, which is a method to generate virtual array antennas, makes it easy to design radar system integrated circuits. However, this method leads to two issues in signal processing; the phase error reduces the accuracy of angle estimation of a moving target, and the maximum detectable velocity decreases in inverse proportion to the number of Tx antennas. We analytically derived this phase error and proposed a method to correct the error. Because the phase error of TDM-MIMO is proportional to the target velocity, accurate estimation of the target velocity is an important issue for phase error correction. However, the decrease of the maximum detectable velocity in TDM-MIMO reduces the accuracy of both velocity estimation and angle estimation. To solve these issues, we propose new signal processing for range-velocity estimation for TDM-MIMO radar. By using the feedback result of the estimated direction of arrival (DoA), we can avoid decreasing the maximum detectable velocity. We explain our method with our simulation results.

The concrete quality of supporting layer in ballastless track is important for the safe operation of a high-speed railway (HSR). However, the supporting layer is covered by the upper track slab and the functional layer, and it is difficult to detect concealed defects inside the supporting layer. To solve this problem, a method of elastic wave velocity imaging is proposed to analyze the concrete quality. First, the propagation path of the elastic wave in the supporting layer is analyzed, and a head-wave arrival-time (HWAT) extraction method based on the wavelet spectrum correlation analysis (WSCA) is proposed. Then, a grid model is established to analyze the relationships among the grid wave velocity, travel route, and travel time. A loss function based on the total variation is constructed, and an inverse method is applied to evaluate the elastic wave velocity in the supporting layer. Finally, simulation and field experiments are conducted to verify the suppression of noise signals and the accuracy of an inverse imaging for the elastic wave velocity estimation. The results show that the WSCA analysis could extract the HWAT efficiently, and the inverse imaging method could accurately estimate wave velocity in the supporting layer.

To deal with the recent explosion of mobile data traffic, heterogeneous cellular networks, in which a large number of small cells are deployed in a macro-cell coverage area, are considered to be a promising approach. However, when a mobile terminal (MT) traveling at a high velocity moves through several small cells in a short period of time, the frequent handovers (HOs) that occur between small cells lead to a deterioration of user quality of experience. To avoid such HO problems, while improving the network capacity in the heterogeneous cellular network, it is effective to introduce an inter-layer HO control policy where MTs traveling at high velocities are connected to the macro-cell layer to reduce the number of HOs and MTs traveling at low velocities or which are stationary are connected to the small-cell layer for offloading traffic from the macro-cells to the small-cells. However, to realize such inter-layer HO control policy in the heterogeneous cellular network, it is crucial to estimate the velocity of each MT. Due to the technological constraints of MT velocity estimation based on the Global Positioning Systems (GPS), we focus on MT velocity estimation algorithms which do not require information provided by GPS. First, we discuss the issues of the existing MT velocity estimation algorithms and then focus on a MT velocity estimation algorithm based on a conventional Doppler spread detection using Fast Fourier Transform (FFT). Since few studies have evaluated Doppler spread based MT velocity estimation techniques for practical communication systems in actual radio propagation environments, we implement the MT velocity estimation algorithm to a Long Term Evolution (LTE) based experimental system, and perform its field experiments. Based on these experimental results we also evaluate the high or low velocity decision accuracy for the inter-layer HO control policy and show that good decision accuracy is achieved in both line-of-sight (LOS) and non-line-of-sight (NLOS) outdoor propagation environment. These results show its feasibility for practical mobile communication systems in actual radio propagation environments.

In this letter, we propose a new practical architecture of channel estimator that can compensate for the signal distortion due to variable mobile station velocity in WCDMA forward link. The proposed Channel Estimator consists of IIR filter for channel estimation and Velocity Estimator for selection of IIR filter coefficients matched to mobile station velocity. The combination of IIR filter and Velocity Estimator can overcome the divergence problem of IIR filter due to the mobile station velocity. The Velocity Estimator estimates the speed of mobile station velocity by observing power spectrum of the received signal and exhibits stable operation in low SNR environment. To improve the resolution of velocity estimation without additional complexity due to large FFT size, an interpolator is adopted in the velocity estimator. The proposed channel estimation architecture can not only be used for WCDMA forward link but also is applicable for CDMA-2000 system without major modifications. Also, the Velocity Estimator can be applied in the channel quality measurement for the selection of MCS (Modulation and Coding Scheme) level in HSDPA transmission.

This paper addresses the problem to extract moving object from the moving background in the mixed domain (MixeD), which makes it possible to carry the filtering in one dimension. Since the velocities of moving object and background are necessary for moving object extraction, we first estimate the velocities based on the appropriate spatial frequency point selection method in the MixeD. Then an optimal filter used for 1-D signal filtering is designed. By filtering 1-D signals over all spatial frequencies, signals with certain velocity vector are extracted, while the extracted image is obtained by applying the 2-D IDFT to the filtered signals. The simulation results show that the method can extract moving object based both on the correctly estimated velocity and the proposed optimal 1-D filter.

A robust moving object velocity estimation method in the mixed domain (MixeD) is proposed. By obtaining phase, that is, normalizing the 1-D complex sinusoid signals with their magnitudes, the velocity estimations of moving objects with conditions such as object rotation, shape and graylevel variation have been accomplished. Based on the appropriate spatial frequency selection, which choose the points where the signals are less influenced by the background and noise, the spectra of these 1-D temporal complex signals in selected points are estimated by FFT. The simulation results show that velocity vectors have been correctly estimated.

To estimate moving object velocity in an image sequence is useful for a variety of applications, such as velocity measurement, computer vision and monitoring systems. An effective way is to approach it in the transform/spatiotemporal mixed domain (MixeD), which transforms the 3-D signal processing problem into 1-D complex signal processing. But it remains a problem how to select several spatial frequency points in the MixeD which may influence the accuracy of velocity estimation and object detection. In this paper, a spatial frequency selection method has been proposed, which can choose the appropriate spatial frequency points out of a number of points in MixeD automatically. So the velocity estimation problem can be addressed by solving the coupled equations established over two selected appropriate points in 2-D spatial frequency domain other than searching for the spectral energy plane over a number of points selected by experience. In this method, evaluation functions corresponding to image sequence with one moving object and two moving objects are established firstly, and the selection is then achieved by using the established evaluation functions together with a threshold. The simulation results show that the proposed method is effective on the appropriate spatial frequency selection.

This paper describes a new single-unit underground radar for detecting underground buried pipes. The pipe depth can be calculated from the hyperbolic shape in the cross-sectional image of radar echoes. The edge contour of the image is extracted, and the buried depth is judged from the similarity between the extracted hyperbolic curve and the theoretical curve. A suitable amplification rate is estimated by choosing the best image from numerous cross-sectional images formed during one antenna movement repeated at different amplification rates. The best image has few pixels corresponding to weak and saturated signals. The new radar is very compact, so it can be operated by one person. Objects buried up to 2.0m deep can be detected.