This letter studies the problem of estimating direction-of-departure (DOD) and direction-of-arrival (DOA) for bistatic multiple-input multiple-output (MIMO) radar with unknown source number, and a modified multiple signal classification (MUSIC) algorithm for weak scattering targets is proposed. We utilize the weighting coefficients to weight the corresponding eigenvectors to reduce the influence of signal subspace vectors that are erroneously classified as noise subspace vectors on angle estimation, which can effectively avoid ignoring weak scatterers by incorrect source number estimation. Some simulations are conducted to validate the effectiveness and robustness of the improved MUSIC algorithm.

Because of the distortion of scatter pattern when measuring multiple-scattering targets in the condition of not satisfying the far-field condition, existing measurements are not favorable. We present a novel algorithm to research near-field to far-field transformation (NFFFT) for multiple-scattering targets. This paper introduces the method of NFFFT using “multiple emission multiple reception” and then chooses a dihedral angle, which has multiple-scattering structure to simulate. The results show a good agreement with the far-field radar cross section (RCS) pattern. Taking into account the feasibility of design, the cost of system, and other limitations in practical application, the bistatic measurement system with a full 360° is obviously hard to achieve. In the bandwidth-limited scattering field, we give the rule of angular truncation based on the transfer function for reducing receiving region. To make the conclusion more reliable, simulations should be set in different parameters, concluding testing frequencies, target size and measuring distance, to observe their effects on the receiving region. We get the mean error of different receiving region by comparing the results of NFFFT and far-field RCS. As a result, a principle for choosing a proper receiving region, say angular truncation, is summarized for multiple-scattering targets, with mean errors 0.5 dB below the full 360° results near the center of far-field pattern.

It is known that the eigenvalue decomposition and singular value decomposition (SVD) of multi-static data matrices (MDMs) are the basis of time-reversal (TR) imaging. However, the computational loads of the SVD can be significantly high when the MDMs are large. The computational cost of the propagator method (PM) without any SVD of MDMs is much lower than that of SVD-based methods. In this paper, to reduce the computational complexity in TR imaging, first a novel space-frequency propagator method (SF-PM) for TR imaging in large MDMs is proposed. The computational complexity of the proposed SF-PM is then analyzed. Moreover, the theoretical analysis is presented to show that the SF-PM has a significantly lower computational complexity than the conventional SVDbased approach. Finally, the reasonable imaging results and superior computational efficiency of the SF-PM are verified by simulation.

The multichannel or wide-angle imaging performance of synthetic aperture radar (SAR) can be improved by applying the compressed sensing (CS) theory to each channel or sub-aperture image formation independently. However, this not only neglects the complementary information between signals of each channel or sub-aperture, but also may lead to failure in guaranteeing the consistency of the position of a scatterer in different channel or sub-aperture images which will make the extraction of some scattering information become difficult. By exploiting the joint sparsity of the signal ensemble, this paper proposes a novel CS-based method for joint sparse recovery of all channel or sub-aperture images. Solving the joint sparse recovery problem with a modified orthogonal matching pursuit algorithm, the recovery precision of scatterers is effectively improved and the scattering information is also preserved during the image formation process. Finally, the simulation and real data is used for verifying the effectiveness of the proposed method. Compared with single channel or sub-aperture independent CS processing, the proposed method can not only obtain better imaging performance with fewer measurements, but also preserve more valuable scattering information for target recognition.

To address the problem of imaging instability when space-frequency (SF) time-reversal (TR) imaging algorithm chooses different ranks of signal subspaces in TR imaging for multiple targets, a modified SF-TR is proposed in this letter, termed W-SF-TR, in which the vectors of noise subspace are weighted to provide the imaging stability. The simulation results show that W-SF-TR significantly reduces the sensitivity of imaging to different ranks, and improves the image resolution as well.

Long-term evolution (LTE) deployment in the unlicensed band has been a promising solution to handle the ever-increasing data traffic growth. However, spectrum sharing on unlicensed band poses a significant challenge regarding the interaction between LTE licensed assisted access (LAA) and Wi-Fi. In this paper, a radio access technology (RAT) heterogeneous network that consists of an LAA tier and a Wi-Fi tier is constructed, to achieve the coexistence and alleviate the intra-RAT and inter-RAT interference, a listen-and-talk (LAT) scheme is utilized in Wi-Fi while LAA adopts the listen-before-talk (LBT). By leveraging the full-duplex (FD) techniques in Wi-Fi, the collision can be avoided and the utilization of unlicensed spectrum can be improved. Furthermore, the sensing errors are derived based on the FD strategy and an enhanced Markov model is presented to analyze the performance of the heterogeneous network with consideration of the residual self-interference (RSI). The fairness between LAA and Wi-Fi is also investigated. At last, to ensure the performance of Wi-Fi when serious RSI exists, a switched MAC (S-MAC) that can adaptively switch between the FD mode and half-duplex (HD) mode is presented.