The joint estimation of the spectrum, carrier, and direction of arrival (DOA) is significant in radar, sonar, wireless communications, and cognitive radio systems. The traditional parameter measurement methods based on the Shannon–Nyquist theorem require very high sampling rates, which put a lot of pressure on sampling, processing, and storage devices. In this article, novel beamforming modulated wideband converter (BMWC) system for joint estimation of the spectrum, carrier, and DOA is proposed to improve the robustness of estimation and reduce structural complexity. From the spatial signal model of array MWC, combined with the spatial filtering and signal enhancement characteristics of adaptive beamforming, the spatial target signals are focused and separated to enhance the robustness of the system. Due to the spatial filtering of beamforming, the MWC structure only needs to reconstruct one signal at a time. The number of channels of MWC connected to each antenna gets rid of the dependence on the number of signals. We utilize the MWC channel expansion technology, which makes it possible to reconstruct the signal well even when each receiving antenna is only connected to a single MWC channel. Compared with previous works, the proposed system enhances the sparsity property of the signals and greatly simplifies the MWC structure. Simulation and hardware experimental results demonstrate the effectiveness and robustness of the proposed method.

Measurement of multiple sinusoidal signals is significant in instrumentation and measurement, radar, communications, and many other electric systems. Measurement methods based upon Shannon sampling theory require very high sampling rates and heavy processing to estimate the frequencies and amplitudes. In this paper, we propose a multichannel time-staggered sampling system to measure multiple sinusoidal signals with short-observation sub-Nyquist samples. The sampling scheme is composed of N' time-staggered sampling channels, where the staggered time is allowed to be greater than the Nyquist sampling interval. The estimation method is based on the frequency domain sparse common support (SCS) model and the Chinese remainder theorem (CRT). To estimate K frequency components in the signal without the image frequency aliasing, N'≥2 sampling channels and N ≥ K + K/N' samples for each channel are enough. In the situation when the image frequency aliasing exists, N ' ≥ 2K,N ≥ 2K samples are sufficient. We further explore the analysis of noisy signals and generalize our proposal to real-valued signals. Simulation and hardware experimental results demonstrate the effectiveness and robustness of the proposed method.

Measurement of linear frequency modulation (LFM) signal is significant for radar, communication, and electronic reconnaissance fields. An LFM signal is a wideband signal whose frequency varies linearly with time, and traditional measurement methods require very high sampling rates and heavy processing to estimate parameters of the LFM signal. In this article, we propose a multichannel cooperative sampling (MCS) system based on the finite rate of innovation (FRI) theory to sample and estimate the parameters of the real-valued LFM pulse sequence (LFMPS). The MCS system consists of three parts: the autocorrelation sampling structure (ACSS), the time-delayed ACSS, and the quadrature time-staggered sampling structure (QTSS). These three parts can sample the LFMPS with sub-Nyquist sampling rate, and using the subspace method in time or frequency domain, the discontinuity locations (DLs), chirp rates, and initial frequencies (IFs) of the LFMPS can be estimated with the sub-Nyquist samples, respectively. The sampling rate of the MCS system is determined by the rate of innovation of the LFMPS, instead of signal bandwidth (BW). The minimum number of samples required for parameter estimation is proven by theoretical analysis. In addition, a nuclear norm denoising algorithm is proposed based on the low-rank property of the signal subspace, which significantly improved the performance of the measurement system in the noise environment. Simulation and hardware experimental results demonstrate the effectiveness and robustness of the proposed method.

In this work, we present a novel radar-communication spectrum sharing framework for transmission design. The design degrees of freedom (DoFs) under control are the radar transmit waveforms and the communication system code-book. The formulation of the spectrum sharing problem is stated as the maximization of the radar signal-to-interference-plus-noise ratio (SINR) under the constraints concerning the transmit energy, the radar similarity with a radar reference waveform, the communication system rate achievable. Compared with the traditional alternating convex transformation approach for the resulting non-convex problem, a novel algorithm is proposed based on the derived local design for radar waveform (LDFRW) to reduce the computational complexity. The numerical results show the merits of the proposed design.

This paper considers a novel algorithmic framework for the joint spectral sharing design of a colocated MIMO pulse radar and a MIMO communication system under signal direction mismatch, wherein both radar transceiver and communication transmitter designs are investigated, and the mutual information (MI) is maximized under a number of constraints to suppress the mutual interference of the coexistence system. The designed multi-variate formulation is non-convex, an iterative procedure is derived to compute a sequence of simpler sub-problems which admit a low complexity closed-form solution for each primal variable. The convergence of all devised algorithms is guaranteed. Finally, a thorough performance assessment is conducted to demonstrate the merits of the proposed solutions.

The recent finite rate of innovation (FRI) framework provides effective sub-Nyquist sampling of pulse streams, allowing recovery of such signals from a set of Fourier coefficients. In this brief, a multi-channel FRI sampling system is presented to sample distinct bands of Fourier coefficients. This is achieved through modulating the desired spectrum band to baseband and then filtering with a low-pass filter. However, the modulation process will lead to the spectrum aliasing and unavailability. A modulation frequency selection strategy is proposed to solve this problem, which allows obtaining reconfigurable Fourier coefficients from the aliasing spectrum. Combining with multi-channel structure, we present a simple and efficient way to sample distinct bands of the pulse streams' spectrum. Finally, a design and implementation of the hardware prototype is presented. Simulation and hardware experiment results demonstrate the effectiveness and robustness of our system.