In this paper we apply the encoding in mathematics to the optical principle and propose a new optical matrix multiplication system with negative binary encoding by using four-wave mixing of photorefractive crystal. Its principle is introduced and its operating performance is analyzed. The research shows that the matrix encoding of the system is easy and it can realize the matrix multiplication cosmically. And it is useful for the research on optical calculation.
Based on the low imaging resolution of bistatic inverse synthetic aperture radar (Bi-ISAR) and the failure of pulse correlation under the condition of sparse aperture cause that of the traditional self-focusing algorithm, a Bi-ISAR sparse aperture self-focusing algorithm with the combined constraint of image quality optimization and sparsity is proposed. First, the proposed algorithm establishes the Bi-ISAR sparse aperture self-focusing signal model, reconstructs images through fast sparse Bayesian learning (FSBL), uses the minimum Tsallis entropy and constraints the reconstruction process, iteratively updates the phase error, and performs self-focusing to realize the initial phase correction of Bi-ISAR images. Simulation results show that the proposed algorithm has a fast convergence speed, strong robustness to noise, and high accuracy in reconstructing images.
Drifting with oceanic current forces is an unique mobility pattern for the underwater sensor networks. The different current velocities at the different depth levels and the periodic velocities can impact the three dimensional network deployment greatly over time. One outcome of such impact is the disruption to the network connectivity. In this paper, a routing protocol that utilizes the bridging nodes is introduced to tackle the connectivity problem. The protocol bears features that explore the unique mobility pattern and geological structure to suppress transmission overhead and improve energy efficiency.
The range resolution of inverse synthetic aperture radar (ISAR) imaging can be improved by directly increasing the bandwidth of the transmitted signal. However, it complicates the design of radar system hardware and increases the manufacturing cost. Aiming at solving the abovementioned problems, a multi-band ISAR fusion imaging method based on the multiple measurement vectors (MMV) model is proposed to improve the range resolution. First, a multi-band ISAR fusion imaging model based on the compressed sensing theory is established. Second, to improve the computational efficiency, a MMV accelerated improved linearized Bregman algorithm is proposed to solve the model. Nesterov’s acceleration gradient method and the condition number optimization of the sensing matrix are combined to further improve the iterative convergence speed. Finally, experimental results based on the simulation data and measured data verify the effectiveness and superiority of the proposed algorithm, which can achieve multi-band ISAR fusion imaging with higher imaging efficiency and better image quality.
Aiming at the multi-sensor multi-target assignment (MSMTA) problem under complex air defense combat environment, a new MSMTA model is proposed with the compositive combat efficiency of the identification, tracking and positioning stage.And then, the quantum particle swarm optimization (QPSQ) algorithm is imported in order to solve the MSMTA problem.Finally, the experiments show that the new MSMTA model is effective and compare the performances between the QPSO and particle swarm optimization (PSO) algorithms.
This paper introduces an effort to overcome the zero steady-state errors resulting from the direct use of the conventional PI regulators to AC signal. A novel type AC PI (P+Resonant) regulator based on the theory of carrier-servo control systems that DC regulators are converted into AC regulators is applied to the inverter. Theory analyses and simulations verified the validity of the new regulator and show a better transient performance. The regulators also proved to be applicable to both single-phase and three-phase constant voltage constant frequency (CVCF) inverters.
Inverse synthetic aperture radar (ISAR) has been widely used in remote sensing because of its high-resolution imaging ability for moving targets in space, ground and sea. At present, the imaging technology for moving targets with uniform velocity has been mature and can get clearer images, but the imaging technology for maneuvering targets still needs to be improved. Firstly, this paper reviews the classical methods of ISAR full aperture maneuvering target imaging, and summarizes the algorithm innovation in recent years; Then, the development of ISAR sparse aperture maneuvering target imaging using compressed sensing technology is systematically introduced. Finally, the future development trend is prospected.
Multi-band inverse synthetic aperture radar (ISAR) fusion imaging technology can effectively improve the range resolution without incurring high hardware cost. The coherent phase between sub-bands is a prerequisite to achieve multi-band ISAR fusion imaging. Here, a joint approach of coherent compensation and high-resolution imaging is proposed to compensate the incoherent phase and obtain high-resolution ISAR fusion images. First, an incoherent phase estimation model based on sparse representation is established, and the phase estimation accuracy is improved by a modified coherent dictionary in case of off-grid. Then, a multi-band ISAR fusion imaging model based on sparse representation is established. The complex Gaussian scale mixture priors and the complex Gaussian priors are imposed on the scatterers and noise, respectively. The solution is derived in the complex domain based on the variational Bayesian expectation maximization framework. The proposed method can not only achieve better incoherent phase compensation in the case of off-grid, but also obtain high-quality ISAR fusion images under low signal-to-noise ratio and low bandwidth sampling ratio. Experimental results verify the effectiveness and robustness of the proposed method based on both numerical simulations and real data.
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