This brief proposes a joint error information theoretic criterion (JET) assisted blind calibration method for multichannel compressive sampling systems under complete blindness. Addressing gain-phase errors and mutual coupling, we first estimate these errors, then leverage an information theoretic criterion (ITC) to estimate signal sparsity, enabling blind calibration and signal reconstruction. An optimal ITC penalty term is derived through joint error model analysis. Simulation and hardware experiments validate the method's effectiveness. Simulation experiments and hardware experiments confirm that our method achieves over 99.9% accuracy in sparsity estimation within the range [0-0.5] of gain-phase errors and mutual coupling errors.

As a sparse-based direction of arrival (DOA) estimation algorithm, the L1-singular value decomposition (SVD) algorithm is widely used to measure the orientation of targets. In real measurements, the coherent environment that often arises due to multipath propagation leads to the deterioration of the noise immunity and estimation accuracy of the L1-SVD algorithm. Although the decoherence of L1-SVD can be enhanced by introducing spatial smoothing after SVD, which is called SS-L1-SVD, the algorithm does not fully utilize the available information in the observed data. In this article, we propose a new method called L1-enhanced spatial smoothing decomposition (ESSD). ESSD combines spatial smoothing with matrix decomposition by utilizing the relationship among the covariance matrix and the left singular matrix and the singular value matrix. ESSD not only improves the decoherence ability of the algorithm but also makes full use of the information in the observed data and reduces the computational complexity, which makes the algorithm more practical than the traditional algorithms in real measurements. In order to further verify the performance of the new algorithm, we not only performed simulation experiments but also designed a physical experimental platform that can be used for DOA estimation and constructed a real coherent environment caused by multipath propagation and performed physical experiments. The results of simulation and physical experiments show that the L1-ESSD algorithm reduces the error by about 1 degrees and the computation time by about 8 s compared with the conventional L1-SVD algorithm.

Sparse direction-of-arrival (DOA) estimation methods can be formulated as a group-sparse optimization problem. Meanwhile, sparse recovery methods based on nonconvex penalty terms have been a hot topic in recent years due to their several appealing properties. Herein, this paper studies a new nonconvex regularized approach called the trimmed lasso for DOA estimation. We define the penalty term of the trimmed lasso in the multiple measurement vector model by ℓ2,1-norm. First, we use the smooth approximation function to change the nonconvex objective function to the convex weighted problem. Next, we derive sparse recovery guarantees based on the extended Restricted Isometry Property and regularization parameter for the trimmed lasso in the multiple measurement vector problem. Our proposed method can control the desired level of sparsity of estimators exactly and give a more precise solution to the DOA estimation problem. Numerical simulations show that our proposed method overperforms traditional approaches, which is more close to the Cramer-Rao bound. © 2024 Elsevier Inc.

With the rapid increase in signal frequency and bandwidth faced by communication and radar systems, compressive sampling systems have received attention. However, these systems face many nonideal situations. The calibration of gain errors is critical and more difficult in the presence of mutual coupling. Therefore, in this article, we focus on the joint blind calibration of modulated wideband converter (MWC) systems with the unknown gain and mutual coupling. Differing from previous studies on array blind calibration, a novel joint blind calibration method for compressed sampling systems that does not depend on Vandermonde matrix properties of array manifolds is proposed. We model this calibration process as a multilinear inverse problem. By transforming the multilinear inverse problem into a linear inverse problem, a general joint blind calibration algorithm for compressed sampling systems is proposed. For the MWC system, we propose an optimized algorithm to meet the identifiability of joint blind calibration and improve the algorithm performance by using the sparse multiband signal characteristics. Simulation experiments and hardware prototype experiments verify our approach.

This paper presents rank-awareness algorithms to solve sparse blind deconvolution using modulated input. We consider sparse blind deconvolution as a rank-one column sparse matrix recovery problem, so the proposed algorithms can use both the rank-one property and the sparsity of the unknowns. Unknown input s is first multiplied by a random sign sequence r and then convolved with an arbitrary filter h to obtain the measurements y. The unknown signal s is assumed to have a sparse representation. Sparse blind deconvolution using modulated input has unique applications, such as the blind calibration of the random demodulation system. When the number of measurements has satisfied certain conditions, blind deconvolution can be solved without considering signal sparsity. This paper mainly studies how to use signal sparsity to reduce the number of measurements required for sparse blind deconvolution. We propose two methods to solve this problem. The first method uses the l(1)-norm regularization to promote the unknown signal to iterate in the direction of sparsity. The second method transforms the sparse blind deconvolution problem into a rank-one constrained block-sparse signal recovery problem, and we propose the rank-awareness sparse blind demodulation algorithm to solve it. Our proposed methods could effectively reduce the number of measurements required for sparse blind deconvolution. Under certain conditions, our proposed sparse blind deconvolution algorithms required 320 and 160 measurements, while 400 measurements were required when signal sparsity was not considered. The simulation results verify the effectiveness of the proposed algorithms.

This paper considers the orthogonal observation matrix design of deterministic compressive sampling (CS). An observation matrix called the weighted truncated Hadamard-modulated wideband converter (WITH-MWC) is deterministically designed based on the truncated Hadamard matrix. This matrix can meet the restricted isometry property (RIP) condition with overwhelming probability by randomly or strategically extracting from a standard Hadamard matrix. Most compressed sampling systems are highly sensitive to noise. To reduce the adverse effects of noise interference, partial specific matrix elements are weighted according to the sparse characteristics of multiband signals, and the recovery probability is provably better than that of the original system and other deterministic observation matrices, especially in the low signal-to-noise ratio scenario. Compared to the random Gaussian and random Bernoulli matrices, WITH-MWC is much easier to implement in hardware. The simulations verify the above analysis.

Modulated Wideband Converter (MWC) is an attractive system implementing analog to information conversion (AIC) of multiband signals at sub-Nyquist rate, which is based on the emerging theory of Compressed Sensing (CS). Frequency mixing with periodic sequence is a pivotal step. However, random waveforms constructing the mixing sequences make the MWC has high complexity. To reduce the complexity, in this letter, we present a novel Diagonal Remainder Matrix based AIC (DRM-AIC). DRM-AIC has only one non-zero element in each sequence, which is similar to the sample-and-hold sampling and each sequence is produced by the delay of a base sequence. We obtain the non-uniform delay between sequences by a simple remainder function plus uniform sampling interval. This special structure makes the observation matrix of DRM-AIC consists of diagonal matrix and a submatrix of a Vandermonde matrix. The rationality and superiority of the observation matrix are verified by theoretical analysis. Simulation results show that DRM-AIC has better reconstruction performance than MWC.

This paper presents a high-speed data transmission plan based on VPX bus. The aim is to establish a high-speed data transmission structure which could be used for dealing with massive echo data from the PAR (Phased array radar) in the future. The frequency is up to gigahertz so that it is hard to design the PCB which is used for high-speed serial data transmission between the boards. Stable and reliable interconnection between payload boards is a main problem. This paper uses channel simulation method to design the hardware. A channel simulation model is established between high-speed backplane and payload modules. This design extracts key information from channel tub curve and eye diagram, then design the hardware based on channel capacity and bit error rate. This paper designs a dual-channel high-speed serial RapidIO annular communication between payload modules based on VPX bus. Single channel theory transmission rate is 3.125Gbps. And the result shows that the actual speed of dual-channel is up to 540.701MB/s. It's nearly the theory speed of the RapidIO communication protocol.

Missions, both near Earth and deep space, are under consideration that will require data recorder capacities doubled at a rate of approximately every three years. This challenge for ever-increasing mass storage also exists in other applications, such as unmanned aerial vehicle (UAV) and echo recording for phased array radar (PAR). All these scenarios call for storage devices with larger capacity, higher I/O bandwidth, lower latency and smaller size. In this paper, we combine Field Programmable Gate Array (FPGA)-based efficient cores of the emerging Non-Volatile Memory express (NVMe) protocol with Flash storage to improve the I/O bandwidth and latency from the operating system (OS) storage I/O software stack. We provide an alternating operation scheme to guarantee consistency of I/O bandwidth. The device has two independent optical fiber channels to ensure the reliability of interconnections and four NVMe flash storage recording data respectively at the same time, which increase its integration and scalability. The prototype has a capacity of 8TB and a volume of only 990 cubic centimeter, weighing only 2.2 pounds. Experimental results demonstrate that the continuous I/O bandwidth of each channel is above 1GBps with variance no more than 7% for its total capacity, and NVMe host logic core achieves up to 88% lower latency against the OS-based system. © 2013 IEEE.