In this paper, We design a dual-channel linearly swept laser based on an electro-optic phase modulator and micro-ring filters, and constructing a long-distance coherent detection system to achieve a long range 3D imaging. This swept laser utilizes two fiber micro-ring filters to generate two independent linear scanning frequency bands, improving the system’s detection range, resolution, and anti-interference capability through dual-channel differential detection. Experimental tests indicate that the swept-source laser designed in this study has a linewidth of less than 20 kHz and a coherence length greater than 1 km; the coherent detection system based on this light source, at a fiber length of 1150 m, achieved a signal intensity of 168 dB, a suppression ratio of 48 dB, and a lateral resolution better than 0.37 m. Furthermore, through a two-dimensional scanning imaging experiment, the three-dimensional contour of a building target at a distance of 378.9 m is successfully reconstructed, with clear visibility of corner points, proving the system’s long-range three-dimensional imaging performance. The results of this paper provide new technical means and application scenarios for long-distance high-resolution laser coherent imaging.
The inverse synthetic aperture LiDAR (ISAL) system demonstrates excellent performance in various application scenarios, including long-range target imaging and recognition. However, due to the micrometer scale of the ISAL system carrier, compensating for the motion error of high-speed complex motion targets becomes challenging. Linear frequency modulation (LFM) signals are not enough to describe these targets accurately, as they can be better represented by high-order polynomial phase signals. Nevertheless, the presence of second-order and higher phase components can greatly affect the coherent synthesis between pulses. To tackle this issue, we propose a non-searching fast cubic phase signal estimation algorithm based on the fourth-order instantaneous autocorrelation function. This algorithm aims to estimate the second-order and third-order coefficients of the rotational error signals using the non-uniform Fourier transform. By accounting for advection compensation, we model the phase of the signal of the complex motion target as the third-order form of the slow time. The Fourier transform of the fourth-order instantaneous autocorrelation function presents challenges, such as the nonlinear coupling between the slow time and delay time, as well as the nonuniform sampling of the delay time. We suggest utilizing the nonuniform Fourier transform to complete the two-dimensional transformation and estimate the obtained second-order and third-order coefficients. In comparison to the traditional range doppler (RD) algorithm, our proposed algorithm achieves better image point focusing and enhances the energy aggregation of scattering points by approximately four times. Simulation results validate the effectiveness of this algorithm.
In the adaptive optics system of large-aperture ground-based telescopes, the wavefront sensor plays a crucial role. Pyramid wavefront sensors are increasingly favored by an expanding number of world-class telescopes. However, traditional wavefront reconstruction algorithms with pyramid wavefront sensors have limited ability to fit nonlinearity, resulting in restricted improvement in reconstruction accuracy. The kernel of deep learning lies in the ability of artificial neural networks to approximate nonlinear functions with arbitrary precision, which is well-suited for solving the nonlinear wavefront reconstruction problem of pyramid wavefront sensors and achieving more accurate wavefront sensing. This paper introduces the application of deep learning in pyramid wavefront sensors and Shack-Hartmann wavefront sensors, conducts a comparative analysis between them, and discusses potential future research directions.
Motion error compensation is a crucial aspect of processing inverse synthetic aperture light detection and ranging data. Motion phase error occurs mainly due to the relative motion between the target and the system, as well as vibration of either the system or the target, which significantly affects the image quality of optical synthetic aperture radar. Since spatial targets usually have a non-cooperative motion state with a high degree of motion parameter uncertainty, accurate estimation of cross-range phase error becomes challenging due to the presence of envelope tilt effect. We propose an adaptive compensation method that handles motion errors of maneuvering targets by estimating and compensating various types of errors introduced by the target motion process. Using the geometric and signal models to analyze error components, a compensation model is established that uses envelope contrast and image entropy as fitness functions. The bat algorithm is employed to solve this error model. Simulation and outdoor experimental results demonstrate that the proposed algorithm offers higher accuracy and better stability compared to traditional optimization algorithms.
In this paper, an indoor segmented mirror co-focus and co-phasing experiment system is constructed in order to successfully identify and correct co-phasing errors in the segmented telescope. The Golay-7 segmented mirror's piston error and tilt & tip error are detected by this experimental system's pyramid wavefront sensor. The control system is used to drive the closed-loop correction of the active segmented mirror. The results of the experiments demonstrate that this method is capable of achieving fine co-focus and co-phasing of the Golay-7 segmented mirror. A segmented mirror system can approach imaging towards the diffraction limit.
Infrared camera arrays have previously demonstrated the potential for long-range detection due to their distributed aperture structures, but their performance is affected by the calibration procedure. The widely used calibration method based on image registration estimates the transformation matrix between different cameras by extracting matching features, and its accuracy is limited by low-resolution infrared images. To solve this problem, a new calibration method based on infinite scene registration is developed, which has high registration accuracy and is easy to implement. The results of the laboratory experiment demonstrate that the signal-to-noise ratio of the point target is improved by 1.86 with the proposed method, which proves the effectiveness and competitiveness of the proposed method. Finally, the measured data of long-range aircraft further verifies the feasibility of the proposed method.
KEYWORDS: Cameras, Long wavelength infrared, Signal to noise ratio, Prototyping, Super resolution, Modulation transfer functions, Infrared cameras, Image processing, Imaging systems, Infrared imaging
To address the complex structure and bulky volume problem of long-wave infrared detection system, we design a front-and-back-splitting distributed long-wave infrared array camera based on fiber image bundle with a simple and high-flexibility structure. The proposed system achieves higher signal-to-noise ratio and improved resolution using image algorithm processing. Benefited by the fiber bundle, the front and back lens groups are separated, which is conductive to scanning imaging and conformal design. A principal prototype of 2-way synthesis is built to verify the imaging performance of the array camera. The prototype is used to obtain two-aperture time series images of small target. One is used as a benchmark and the other is registered and synthesized. The signal-to-noise ratio of composite image increased by 25.15% by eliminating time noise. Besides, this paper uses the redundant information among multiple micro-displacement images obtained by pixel-level displacement of fiber bundle to achieve super-resolution image reconstruction. The image resolution at least doubles in one-dimensional direction and the reconstruction algorithm can be extended to two dimensions. Experimental results of the prototype show that the distributed array camera we designed can improve the signal-to-noise ratio and realize image super-resolution reconstruction with structure simplified and volume reduced.
Based on photonic integrated circuit (PIC) technology and synthetic aperture technology, the advanced technology of segmented planar imaging, which can be used to realize ultrathin and high-resolution imaging, is proposed. We develop a sampling lens array structure that combines coherent detection and traditional imaging; it can improve the sampling of low- and medium-frequency information. Based on the proposed lens array structure, a full-chain simulation model of the segmented planar imaging system is established. Under the premise of similar imaging quality, compared with the existing sampling lens structures, the proposed lens array has a simpler structure, lower processing difficulty, and lower cost. The simulation analyses of the duty ratio of the lens array, the light splitting number of PIC arrayed waveguide gratings, and the radius of the single lens are carried out. The simulation results provide a certain reference value for the optimal design of the segmented planar optical imaging system and further prove the advantages of the proposed structure.
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