Search

Phaseless measurement is widely used in various fields, and phase retrieval is a key step in signal reconstruction of phaseless measurement. The occurrence of outliers will cause the optimal solution of the traditional phase retrieval objective function to deviate from the original signal, thereby reducing the reconstruction accuracy. This article modifies the objective function by introducing a weight vector so that its optimal solution will still approximate the original signal when outliers appear and proposes a specific implementation strategy for this idea under the background of Fourier phase retrieval. For the weight vector, we design a two-channel measurement structure using a discrete Fourier transform frequency-domain cyclic shift theorem and mask technique to obtain a weight vector. For the improved objective function, we use the majorization–minimization framework to derive an iterative algorithm to get an optimal solution. Extensive simulation and hardware experiments show that the proposed method can effectively suppress the adverse effects of outliers. [ABSTRACT FROM AUTHOR]

Measurement of multiple sinusoidal signals (MSSs) 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 article, we propose a multichannel time-staggered sampling system to measure MSSs 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 image frequency aliasing, N' ≥ 2 sampling channels and N ≥ [K + K/N']samples for each channel are enough. In the situation when image frequency aliasing exists, N' ≥ 2K and 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. [ABSTRACT FROM AUTHOR]

Measurement of multiple sinusoidal signals (MSSs) 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 article, we propose a multichannel time-staggered sampling system to measure MSSs 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 image frequency aliasing, $N' \geq 2$ sampling channels and $N\geq \lceil K+K/N' \rceil $ samples for each channel are enough. In the situation when image frequency aliasing exists, $N' \geq 2K$ and $N\geq 2K$ 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. [ABSTRACT FROM AUTHOR]

Measurement of parameters of $M$ -ary phase shift keying (MPSK) signals is significant for radar and communication systems. Traditional parameter measurement methods require very high sampling rates and heavy processing to estimate the key parameters of amplitude, carrier frequency, phases, and discontinuity locations. We propose a multichannel cooperative sampling system using the finite rate of innovation sampling theorem to address this problem. In our two-channel system, the main channel estimates the MPSK amplitude and carrier frequency using a nonlinear process and time-staggered sampling structure, while the cooperative channel determines phases and discontinuity locations with an annihilating filter using the main channel’s estimated carrier frequency. Our system enables recovery of these parameters from as few as $2K+6$ samples from MPSK signals with $K$ segments. We also present a hardware platform implementing the structure. The simulation and experimental results demonstrate the effectiveness and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) shows that, when a signal is sparse or compressible with respect to some basis, a small number of compressive measurements of the original signal can be sufficient for exact (or approximate) recovery. Distributed CS (DCS) takes advantage of both intra- and intersignal correlation structures to reduce the number of measurements required for multisignal recovery. In most cases of audio signal processing, only mixtures of the original sources are available for observation under the DCS framework, without prior information on both the source signals and the mixing process. To recover the original sources, estimating the mixing process is a key step. The underlying method for mixing matrix estimation reconstructs the mixtures by a DCS approach first and then estimates the mixing matrix from the recovered mixtures. The reconstruction step takes considerable time and also introduces errors into the estimation step. The novelty of this paper lies in verifying the independence and non-Gaussian property for the compressive measurements of audio signals, based on which it proposes a novel method that estimates the mixing matrix directly from the compressive observations without reconstructing the mixtures. Numerical simulations show that the proposed method outperforms the underlying method with better estimation speed and accuracy in both noisy and noiseless cases. [ABSTRACT FROM PUBLISHER]