Super-Resolution Range and Velocity Estimations for SFA-OFDM Radar

Sparse frequency agile orthogonal frequency division multiplexing (SFA-OFDM) signal brings excellent performance to electronic counter-countermeasures (ECCM) and reduces the complexity of the radar system. However, frequency agility makes coherent processing a much more challenging task for the radar, which leads to the discontinuity of the echo phase in a coherent processing interval (CPI), so the fast Fourier transform (FFT)-based method is no longer a valid way to complete the coherent integration. To overcome this problem, we proposed a novel scheme to estimate both super-resolution range and velocity. The subcarriers of each pulse are firstly synthesized in time domain. Then, the range and velocity estimations for the SFA-OFDM radar are regarded as the parameter estimations of a linear array. Finally, both the super-resolution range and velocity are obtained by exploiting the multiple signal classification (MUSIC) algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Dispersive MUSIC algorithm for Lamb wave phased array

By controlling the excitation time delay on each element, the conventional phased array can physically focus signals transmitted by different elements on a desired point in turn. An alternative and time-saving strategy is that every element takes turns to transmit the excitation and the remaining elements receive the corresponding response signals, which is known as the full matrix capture (FMC) method for data acquisition, and then let the signals virtually focus on every desired point by post-processing technique. In this study, based on the FMC, a dispersive multiple signal classification (MUSIC) algorithm for Lamb wave phased array is developed to locate defects. The virtual time reversal is implemented to back propagate the wave packets corresponding to the desired focusing point and a window function is adopted to adaptively isolate the desired packets from the other components. Then those wave packets are forward propagated to the original focusing point at a constant velocity. For every potential focusing point and all receivers, the virtual array focuses the signals from all transmitters so as to obtain the focusing signals. The MUSIC algorithm with the obtained focusing signals is adopted to achieve Lamb wave imaging. Benefiting from the post-processing operations, the baseline subtraction as well as the estimation for the number of the scattering sources is no longer required in the proposed algorithm. Experiments on an aluminum plate with three artificial defects and a compact circular PZT array are implemented and the results demonstrate the efficacy of the proposed algorithm.

Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array

In this paper, we investigate the problem of direction-of-arrival (DOA) estimation for massive multi-input multi-output (MIMO) radar, and propose a total array-based multiple signals classification (TA-MUSIC) algorithm for two-dimensional direction-of-arrival (DOA) estimation with a coprime cubic array (CCA). Unlike the conventional multiple signal classification (MUSIC) algorithm, the TA-MUSIC algorithm employs not only the auto-covariance matrix but also the mutual covariance matrix by stacking the received signals of two sub cubic arrays so that full degrees of freedom (DOFs) can be utilized. We verified that the phase ambiguity problem can be eliminated by employing the coprime property. Moreover, to achieve lower complexity, we explored the estimation of signal parameters via the rotational invariance technique (ESPRIT)-based multiple signal classification (E-MUSIC) algorithm, which uses a successive scheme to be computationally efficient. The Cramer–Rao bound (CRB) was taken as a theoretical benchmark for the lower boundary of the unbiased estimate. Finally, numerical simulations were conducted in order to demonstrate the effectiveness and superiority of the proposed algorithms.

Quantum algorithm for MUSIC-based DOA estimation in hybrid MIMO systems

The multiple signal classification (MUSIC) algorithm is a well-established method to evaluate the direction of arrival (DOA) of signals. However, the construction and eigen-decomposition of the sample covariance matrix (SCM) are computationally costly for MUSIC in hybrid multiple input multiple output (MIMO) systems, which limits the application and advancement of the algorithm. In this paper, we present a novel quantum method for MUSIC in hybrid MIMO systems. Our scheme makes the following three contributions. First, the quantum subroutine for constructing the approximate SCM is designed, along with the quantum circuit for the steering vector and a proposal for quantum singular vector transformation. Second, the variational density matrix eigensolver is proposed to determine the signal and noise subspaces utilizing the destructive swap test. As a proof of principle, we conduct two numerical experiments using a quantum simulator. Finally, the quantum labelling procedure is explored to determine the DOA. The proposed quantum method can potentially achieve exponential speedup on certain parameters and polynomial speedup on others under specific moderate circumstances, compared with their classical counterparts.

Very-small-aperture 3D infrasonic array for volcanic jet observation at Stromboli Volcano

Summary This study tested a very-small-aperture three-dimensional (VSA-3D) infrasonic array. A 3D array is ideal for resolving the back elevation angle (BEL), which has become important in the analysis of volcanic jet noise or geologic flows on steep mountain slopes. Although a VSA infrasonic array, with an aperture as small as a few tens of meters, has recently been shown to have a sufficient resolution of the back azimuth (BAZ) of incident signals, its BEL resolution is considered to be poor. We performed a four-element 3D array experiment with a 20-m aperture and 2-m height at the summit of Stromboli Volcano. We analyzed the direction of arrival (DOA) with the MUSIC algorithm as a function of frequency and conducted a cluster analysis for the estimated DOA–frequency functions of eruption signals. As a result, individual infrasonic signals were successfully related to eruptive vents. We also calculated the standard deviation (STD) of the DOAs in each cluster. Of the observed BAZ-STDs and BEL-STDs, 80 per cent were <2.0° and <4.6°, respectively. A comparison among the array geometries showed that the installation of a sensor above the ground, even at only 2 m, improved the BEL resolution, indicating that the VSA-3D array provides more detailed information about the wavefield than a planar array. The observed signals had higher BELs (−20° to 0°) than the vent direction (−30° to −25°) at 3–6 Hz, although signals above 20 Hz arrived from the vent direction. Our array verified that such DOA deviations were significant by the STD analysis and some tests with synthetic data. We infer that the DOA deviations do not indicate the source location and are caused by topographical diffraction.

Distributed Two-Dimensional MUSIC for Joint Range and Angle Estimation with Distributed FMCW MIMO Radars

To estimate range and angle information of multiple targets, FMCW MIMO radars have been exploited with 2D MUSIC algorithms. To improve estimation accuracy, received signals from multiple FMCW MIMO radars are collected at the data fusion center and processed coherently, which increases data communication overhead and implementation complexity. To resolve them, we propose the distributed 2D MUSIC algorithm with coordinate transformation, in which 2D MUSIC algorithm is operated with respect to the reference radar’s coordinate at each radar in a distributed way. Rather than forwarding the raw data of received signal to the fusion center, each radar performs 2D MUSIC with its own received signal in the transformed coordinates. Accordingly, the distributed radars do not need to report all their measured signals to the data fusion center, but they forward their local cost function values of 2D MUSIC for the radar image region of interest. The data fusion center can then estimate the range and angle information of targets jointly from the aggregated cost function. By applying the proposed scheme to the experimentally measured data, its performance is verified in the real environment test.

Research on 5G positioning technology for urban canyon

Aiming at the problem of poor positioning accuracy of GNSS for urban canyon environment, this paper proposes a TDOA/AOA combined positioning technology based on 5G.Firstly, the AOA estimation algorithm based on conventional beam forming is compared with that based on MUSIC algorithm. Then, the weighted matrix is smoothed forward and backward.Finally, according to the measurement data of AOA and TDOA, Chan and Taylor combined localization algorithm is used to obtain the estimated location of the user.