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.

Synthetic aperture radar automatic target recognition (SAR ATR) is a powerful tool for recognizing targets. SAR ATR needs a massive database of inverse synthetic aperture radar (ISAR) images from computer aided design (CAD) models for multiple aspect angles. To rapidly build the multi-view database, we present a fast method to simulate the ISAR images over multiple aspect angles using the shooting and bouncing ray (SBR) technique. Compared to the previous fast method where a ray tracing process must be performed at each aspect angle of the ISAR images, the proposed method generates the ISAR images over multiple aspect angles through only a one-time ray tracing process at an arbitrary angle. Therefore, the computational time is decreased as much as the number of ray tracing processes is decreased. The ISAR images of a tank CAD model are simulated over multiple aspect angles to verify the performance of the proposed method.

In this paper, a joint multiple-input multiple-output (MIMO OFDM) radar and communication (RadCom) system is proposed, in which orthogonal frequency division multiplexing (OFDM) waveforms carrying data to be transmitted to the information receiver are exploited to get high-resolution radar images at the RadCom platform. Specifically, to get two-dimensional (i.e., range and azimuth angle) radar images with high resolution, a compressive sensing-based imaging algorithm is proposed that is applicable to the signal received through multiple receive antennas. Because both the radar imaging performance (i.e., the mean square error of the radar image) and the communication performance (i.e., the achievable rate) are affected by the subcarrier allocation across multiple transmit antennas, by analyzing both radar imaging and communication performances, we also propose a subcarrier allocation strategy such that a high achievable rate is obtained without sacrificing the radar imaging performance.

This paper presents a robust method of moment (MoM) using matrix equilibration to conduct scattering analysis of a complex stealth aircraft in very high frequency (VHF) band. The MoM is a well-known technique used to solve the integral equations in the frequency domain. The Rao Wilton Glisson (RWG) basis function is widely applied as source and test basis functions. However, regarding the scattering problem of the stealth aircraft, which is complicated and electrically large, condition number is significantly increased by using the conventional MoM. Using preconditioning matrices, we decrease the condition number of the linear problem for the stealth aircraft in VHF band. Numerical simulations for realistic aircraft are presented to demonstrate the performance of the proposed method.

In this paper, a partially binarized deep learning-based MUltiple SIgnal Classification (MUSIC) algorithm for estimating the angle-of-arrivals (AoAs) of multiple targets using wireless sensor array systems is proposed. Since the sensor array system has limited computing power, it is not desirable to put the entire neural network on a single sensor node. Accordingly, the neural network was partitioned into two parts: the sensor node and the ground server. The neural network output (that is, the partially processed data) at the node was forwarded to the server through a noisy backhaul link channel. By modeling the noisy backhaul link as a binarized feedforward layer, we developed a new neural network architecture suitable for AoA estimation using wireless sensor array systems, and we trained it using a straight-through gradient estimator. Furthermore, unlike conventional deep learning-based MUSIC in which the MUSIC pseudospectrum for multiple targets is exploited as a label for training neural networks, a new training dataset generation method is proposed. Specifically, we generated the label by using the weighted sum of the MUSIC pseudospectra of each single target, resulting in more apparent peaks in the target angles and enhancing multiple target angle estimation accuracy.

A 2-D Inverse synthetic aperture radar (ISAR) imaging method is proposed, which only requires a short period of time for target observation. Typically, a long period of observing time is necessary to achieve high cross-range resolution in ISAR imaging since a wide observing angle is critical. However, long-term observation accompanies disadvantage in discriminating the target especially when the target has complicated motion. In this paper, we propose a 2-D ISAR imaging method with short-term observation. The down-range of scatterers is obtained by inverse fast Fourier transform (IFFT) and the cross-range of scatterers is obtained by estimation of signal parameters via rotational invariance technique (ESPRIT). To improve computational efficiency, we sort out the signal region and noise region with a certain threshold level and then the ESPRIT method is only applied to the signal region. A set of simulated and measured data of Boeing 727 as an aircraft target is used to validate the proposed imaging method in this paper. By adopting the proposed method, the required observation time of target can be reduced to 12.5%.