31 results about "Adaptive imaging" patented technology

The invention relates to a cable channel monitoring and risk grading method based on working condition adaptive enhancement and cross-modal unified modeling, and the method comprises the steps: obtaining video modal data and sensor modal data, and building a global unified time axis; performing enhancement processing on the video modal data through a working condition self-adaptive imaging enhancement mechanism, and performing weighted fusion to calculate an image quality score; enhancing the enhanced video modal data through an attention mechanism to obtain final video features, and constructing a graph structure according to a topological relation of sensor positions to obtain sensor features; constructing a cross-modal fusion mechanism, and performing deep semantic fusion on the final video features and the sensor features to obtain final fusion features; multi-dimensional detection results are calculated, the detection results meeting the reliability condition are screened, the detection results are converted into risk grades and disposal instructions, and cable channel monitoring and risk grading are achieved. Compared with the prior art, the method has the advantages of being suitable for complex working conditions, high in anomaly recognition accuracy, high in reliability, stable in long-term operation and the like.

The application discloses a hyperspectral imaging chip, device and system based on an adjustable reconfigurable metasurface, and the chip comprises a metasurface unit array, a metasurface reconfiguration module, a metasurface adjustment module, a CMOS image sensor and a spectrum reconstruction and identification module, the metasurface unit array comprises a plurality of metasurface units arranged on a silicon layer; the metasurface reconfiguration module is used for adjusting the number of metasurface units included in the reconfigurable metasurface unit array; the metasurface adjustment module is used for connecting the silicon layer of the metasurface unit array through wires, and is used for changing the spectral response of the metasurface unit by adjusting the voltage of the wires; the CMOS image sensor is used for detecting light signals from the reconfigurable metasurface unit array after modulation; and the spectrum reconstruction and identification module is used for performing hyperspectral reconstruction and imaging according to the light signals detected by the CMOS image sensor. The application has adjustable spectral response, ultrahigh spectral resolution, real-time dynamic monitoring capability and self-adaptive imaging capability.

The invention discloses an on-line detection method for surface defects of an EVA (Ethylene Vinyl Acetate) adhesive film based on machine vision, which belongs to the technical field of industrial automatic quality detection and comprises the following steps: acquiring multiband image data of the surface of the EVA adhesive film acquired by a preset multi-angle self-adaptive imaging system; performing background difference processing on the multiband image data to generate a difference image; performing adaptive contrast enhancement processing on the difference image to obtain an enhanced image; extracting texture feature data and morphological feature data from the enhanced image to generate a multi-feature data set; performing dynamic weighted fusion processing on the multi-feature data set to generate a defect probability graph; the defect probability graph is input into the layered detection network for processing, a defect detection result is generated, the technical scheme that multi-angle self-adaptive imaging, multi-feature dynamic fusion and the layered detection network are combined is adopted, the defect imaging quality and feature significance can be improved in a self-adaptive mode, and high-speed accurate detection and dynamic optimization of the surface defects of the EVA adhesive film are achieved.

This invention discloses an all-weather reconnaissance and alarm device, comprising a laser illumination component, an ICCD / ICMOS gating component, and an integrated processor. The laser illumination component uses high peak power pulsed laser light for illumination assistance, enhancing target brightness. The ICCD / ICMOS gating component is used to achieve high-gain photoelectric conversion of weak optical signals. Specifically, the ICCD / ICMOS gating component eliminates or reduces the influence of sunlight through "ultrafast adaptive imaging + light decay," enabling the component to operate in the linear region with the optimal signal-to-noise ratio for the target. Simultaneously, the ICCD / ICMOS gating component integrates a high-speed CCD / CMOS image sensor. Without changing the delay time, a higher image intensifier repetition frequency results in a smaller increase in delay time. Through ultra-high-speed image acquisition, it fully utilizes the energy of each laser pulse, enabling imaging of targets at different distances and high-speed moving targets. The integrated processing component focuses on controlling the ultrafast adaptive adjustment duration, the interval between auxiliary illumination and gating opening, and high-precision gain control.

The invention provides a multi-drug fluorescence spectrum adaptive imaging analysis method and device. Comprising the following steps: performing incident wavelength search on an original fluorescence image acquired by a fluorescence camera by applying a Bayesian optimization algorithm to obtain an optimal incident wavelength; and under the optimal incident wavelength, acquiring an original fluorescence image again by the fluorescence camera, acquiring an original visible light image by the visible light camera, performing deep learning filtering noise reduction on the original visible light image and the original fluorescence image, and inputting the images into an image fusion network at the same time to obtain an image fusion fluorescence image. The fluorescent drug excitation wavelength can be automatically matched, the fluorescent brightness can be automatically adjusted, the debugging time of a machine is shortened, and the utilization rate of equipment is improved.

The invention relates to a task-driven adaptive imaging method for power line anomaly detection, and the method comprises the steps: constructing a power line anomaly detection model, an adaptive camera control model and an image simulation model, and carrying out the combined optimization of the dynamic imaging parameters of a camera and a downstream anomaly detection task based on the three models. A dynamic mapping relation among ambient light, the moving speed of inspection equipment, dynamic imaging parameters and detection performance is established, and conversion from general imaging to task optimal imaging is realized. And the problem of poor performance of a downstream anomaly detection model caused by a general imaging means under complex illumination and motion in the existing routing inspection technology is solved. According to the invention, the image simulation model is utilized to obtain the data set for training the model based on the data set of the existing anomaly detection task, and the data set construction process is simplified.

The invention relates to the technical field of weld visual inspection, in particular to a weld visual inspection method and system. The method comprises the following steps: acquiring navigation data; evaluating a quality score of the navigation data, and when the quality score is lower than a first preset threshold value, generating an adaptive imaging mode instruction; according to the self-adaptive imaging mode instruction, imaging parameters of the main vision industrial camera are adjusted, and the imaging parameters comprise the focal length and the aperture; collecting an expanded view field image according to the adjusted imaging parameters; the expanded view field image is processed, the actual position of the welding seam is recognized, and the center line of the welding seam is extracted; and determining a core detection area of the welding seam by taking the center line as a reference, and performing defect detection on the core detection area of the welding seam. The problems that in a specific welding process, laser scanning navigation data are inaccurate due to uneven welding slag covering the surface of a welding seam, and then the welding seam detection precision and reliability are affected are solved.

The invention discloses a satellite-borne camera real-time adaptive imaging method based on target detection, and belongs to the technical field of space-based information on-satellite processing. From the perspective of satellite detection imaging engineering, imaging parameters of corresponding detection modules are adjusted based on the change condition of the exposure information amount of a frame anchor image where an on-satellite target detection result is located according to the characteristic of large change of the radiation intensity of a detection dynamic target; wherein in consideration of the fact that the gradient value of a target edge pixel area is large and the gradient value of a pixel area in a target contour is small, in order to balance the importance of a weak gradient and a strong gradient, a logarithmic function is used for mapping the relation between the gradient amplitude and the gradient information amount, and therefore the accuracy amount of the exposure information amount of the frame anchor image is achieved; based on this, the method can adapt to the change of the radiation intensity of the target in real time, thereby not only guaranteeing the effective detection of the dark and weak target, but also avoiding the detection saturation, and achieving the imaging of complete target characteristics.

The invention discloses a multi-mode single-photon radar system and an environment adaptive imaging method based on the same. The multi-mode single-photon radar system comprises a laser scanning module and a photon detection module. The environment detection module and the photon detection module are electrically connected with the control module, and the control module is electrically connected with the laser scanning module, the photon detection module and the environment detection module; the image reconstruction module is electrically connected with the environment detection module and the control module; and the upper computer interaction module is electrically connected with the image reconstruction module. Therefore, the multi-mode single-photon radar system can dynamically adapt to and perform high-resolution imaging in complex environments such as haze, strong background light, rain and snow and the like, and the multi-mode single-photon radar system is modularized in structure, has good expansibility and engineering realizability and is suitable for the fields of automatic driving, complex scene perception, target surveying and mapping and the like.

The invention discloses an ecological intelligent capturing method and system based on sound and infrared multi-mode cooperation, and belongs to the technical field of ecological monitoring and intelligent sensing, and the method comprises the steps: collecting an original environment sound signal, carrying out the preprocessing of the original environment sound signal, carrying out the semantic recognition of the preprocessed original environment sound signal, and obtaining a semantic recognition result; semantic tags and sound source direction information are obtained; according to the sound source direction information, infrared scanning is carried out on the corresponding direction area, and infrared features of a heat source in the scanning area are extracted; performing feature matching on the infrared features and semantic tags, outputting a target type, and generating adaptive imaging parameters based on the target type; shooting is carried out according to the self-adaptive imaging parameters, event capturing is completed, and the low-power-consumption standby state is returned. According to the method, the problems of high false triggering rate, low effective data proportion, short endurance time and poor scene adaptability in the prior art are solved, the transmission efficiency, the positioning accuracy and the adaptability are improved, and the false alarm rate is reduced.

This invention discloses a method and system for quality inspection of liquid coating products based on image recognition, belonging to the field of image processing technology. The method includes acquiring a geometric topological model and extracting the interface curve; dynamically configuring the light source and adjusting the shooting direction according to the material reflectivity difference to acquire composite feature images; executing parallel analysis paths for the substrate, silicone parts, and interface bonding; the interface bonding path projects the interface curve onto the image and calculates the grayscale gradient modulus to quantify the bonding degree; inputting the indicators of each path into a multivariate nonlinear comprehensive quality evaluation function, and outputting the judgment result. This invention achieves simultaneous integrated detection of the substrate, silicone parts, and bonding interface for the first time. Adaptive imaging effectively eliminates interference from reflectivity differences, and the three-path parallel architecture meets online cycle time requirements. Combined with a graded evaluation of delamination degree and an adaptive threshold update mechanism, it possesses high detection rate, strong robustness, and good industrial adaptability.

Various embodiments of the present disclosure provide an adaptive imaging process that includes receiving a measured fill level for a container based on a distance reading measurement, selecting a focus level from a plurality of calibrated focus levels based on the measured fill level, providing, to an imaging device, one or more imaging instructions to trigger a validation image at the focus level, receiving, from the imaging device, an imaging response to the one or more imaging instructions that comprises an image classification for the validation image, generating a verification event based on the image classification, and storing the verification event in association with the container.

The application provides an intelligent control method and system based on camera automatic focusing and exposure, and relates to the technical field of camera intelligent control, comprising: collecting multi-sensor data, wherein the multi-sensor data comprises temperature data, acceleration data, illumination data and humidity data; performing fusion processing on the multi-sensor data to construct a working state data set; extracting acceleration sensor data from the working state data set, identifying vibration mode and intensity through vibration spectrum analysis, and dynamically adjusting anti-shake parameters; performing real-time analysis on the collected image, extracting image quality characteristics, constructing a multi-dimensional feature vector in combination with the working state data set, and generating a focusing state score. The application can maintain image clarity and stability in a complex and changeable environment, improve the adaptability of the camera to different scenes, realize intelligent adaptive imaging control, and significantly improve image quality and system reliability.

This invention relates to a cable channel monitoring and risk classification method based on condition-adaptive enhancement and cross-modal unified modeling, comprising: acquiring video modal data and sensor modal data, and establishing a globally unified time axis; enhancing the video modal data through a condition-adaptive imaging enhancement mechanism, and calculating the image quality score through weighted fusion; enhancing the enhanced video modal data through an attention mechanism to obtain final video features, and constructing a graph structure based on the topological relationship of sensor locations to obtain sensor features; constructing a cross-modal fusion mechanism to perform deep semantic fusion of the final video features and sensor features to obtain the final fused features; calculating multi-dimensional detection results, screening detection results that meet reliability conditions, and converting the detection results into risk levels and handling instructions to achieve cable channel monitoring and risk classification. Compared with existing technologies, this invention has advantages such as adaptability to complex working conditions, high accuracy in anomaly identification, high reliability, and long-term stable operation.

The present application relates to a kind of wind snow particle adaptive imaging measurement system and method based on double view field fusion, system includes: optical illumination unit, double view field imaging unit, field of view switching unit, data acquisition control unit, data processing unit;Optical illumination unit, for illuminating sampling space;Double view field imaging unit, for wind snow particle double view field imaging;Field of view switching unit, for controlling double view field imaging unit field of view switching and adjustment;Data acquisition control unit, for controlling optical illumination unit and double view field imaging unit, wind snow particle image data acquisition;Data processing unit, for wind snow particle image data processing and display.The present application is based on the development of large field of view and small field of view array camera, can provide important data support for atmospheric science research, snow field observation, weather modification etc..

This invention discloses an intelligent fish fry sorting method based on state-space modeling and adaptive imaging closed-loop, relating to the field of aquaculture technology. At the acquisition end, an imaging closed loop of "controllable illumination—frequency domain calibration—exposure and gain self-calibration" is constructed. At the computation end, a lightweight front-end of "multi-scale template matching ROI rapid localization + small-range four-sided approximation tracking" is used. At the detection end, an end-to-end detection network is constructed, with a visual state-space module introduced in the backbone stage. In the feature fusion stage, HSFPN is used, and multi-scale feature selection and complementary fusion are completed through channel attention. At the control end, the detection results are mapped to the coordinate system of the sorting actuator, and combined with trajectory correlation, activity heatmap, abnormal fish fry identification, dead fish fry identification, and closed-loop strategy, the actuator is driven to achieve non-contact hierarchical sorting and counting. This invention, using the above method, improves the detection rate of dense small targets and robustness under complex water imaging conditions while ensuring real-time performance.

This application discloses a method and system for inspecting the surface quality of electroplated finished products. The method includes acquiring information about the electroplated finished product to be tested, including product attribute information and actual electroplating process parameters; determining image acquisition parameters based on the product attribute information and actual electroplating process parameters through a preset parameter mapping table; acquiring an image of the electroplated finished product based on the image acquisition parameters; extracting multi-dimensional optical features from the electroplated finished product surface image; fusing the actual electroplating process parameters with the multi-dimensional optical features to generate process-aware fusion features; and obtaining the surface quality inspection result of the electroplated finished product based on the process-aware fusion features through a preset defect detection model. This application effectively improves the accuracy of electroplated surface quality inspection by using adaptive imaging and fusing electroplating process information.

The invention provides a self-adaptive imaging system based on a CMOS detector. The self-adaptive imaging system comprises a CMOS focal plane circuit, a data processing circuit, an interface circuit and a camera integrated electron. The interface circuit receives a power supply provided by the power supply module, and provides the power supply to the CMOS focal plane circuit and the data processing circuit after filtering conversion; each control signal of the camera integrated electronics is received and sent to the data processing circuit, and telemetering information of the data processing circuit is forwarded to the camera integrated electronics; the CMOS focal plane circuit comprises a CMOS detector; the CMOS detector receives a time sequence driving signal generated by the data processing circuit and drives the CMOS detector to work normally, and the CMOS detector outputs multichannel original image data; the data processing circuit carries out image data automatic alignment on original image data output by the CMOS detector, carries out real-time monitoring in the imaging process, carries out arrangement and preprocessing on the data, carries out target detection and image interception in real time, recognizes a target area image, carries out automatic exposure adjustment on the recognized target area image, and carries out automatic exposure adjustment on the recognized target area image. And outputting the target valid data.

The invention relates to the technical field of infrared imaging, in particular to an anti-stray-light automatic imaging control method and system for infrared imaging. The method comprises the following steps: acquiring image data and stray light data corresponding to the image data; according to the equipment temperature time sequence data and the light intensity time sequence data, the stray light imaging influence degree is determined; correcting the stray light imaging influence degree according to the dust particle concentration data to obtain a strong stray light tendency coefficient; the strong stray light tendency coefficient is used for representing the comprehensive probability of strong stray light interference of the infrared image; based on the texture difference degree of the image data and the radiation stability value corresponding to the infrared image, determining the comprehensive anti-stray light stability; determining stray light inhibition urgency according to the comprehensive stray light resistance stability and the strong stray light tendency coefficient; and according to the stray light suppression urgent degree and a preset threshold value, determining a target imaging control mode, so that the infrared imager performs adaptive imaging control according to the target imaging control mode. According to the invention, the anti-stray light imaging control precision in the infrared imaging process can be improved.

The invention relates to a self-adaptive imaging method and system suitable for circuit board defect detection, and is applied to the technical field of circuit board defect detection, and the method comprises the steps: obtaining a current collection image of a circuit board under a current optical condition and a current collection parameter; generating a current difference total vector according to the current acquired image based on a preset target image imaging requirement; and based on the current difference total vector, the current optical condition and the current acquisition parameter, adopting the trained neural network model to generate an optical correction vector and an acquisition correction vector so as to re-acquire a next acquisition image of the circuit board according to the corrected acquisition parameter under the corrected optical condition. The circuit board defect detection method has the technical effect that the accuracy and efficiency of circuit board defect detection are improved through adaptive imaging.

This invention discloses a fast deconvolution sound source localization method based on array sparsity and broadband integrated processing, comprising the following steps: selecting microphones to form a sparse array, transforming the regular focusing plane into an irregular grid coordinate; calculating the cross-spectrum matrix of the sparse array based on the coordinates, and obtaining the function beamforming output through eigenvalue decomposition and power exponentiation; constructing a frequency domain deconvolution model based on its convolution relationship with the sound source distribution and the power-law spread function, and solving the sound source intensity distribution matrix using fast Fourier transform and iterative optimization; estimating the noise floor and transforming it into a signal-to-noise ratio matrix, traversing frequency points to obtain the maximum signal-to-noise ratio and fusing it into a broadband imaging matrix; introducing a time smoothing factor to weight and fuse the inter-frame imaging matrix to complete the localization; this method reduces the load through array sparsity and fast deconvolution, maintains accuracy through function beamforming and Gaussian filtering, achieves multi-frequency point fusion adaptive imaging, and suppresses flicker through inter-frame smoothing, adapting to complex scenes and realizing low-power, high-precision, and robust sound source localization.

This invention relates to the field of microscopy imaging technology, and more particularly to a method for acquiring microscope images. By inputting sample type data and environmental parameters of the environmental monitoring sample into a pre-trained adaptive imaging model, the model synchronously generates adaptive focusing and illumination parameters that match the current imaging environment. The microscope's focusing position, focusing step size, and focusing range are adjusted according to the adaptive focusing parameters, and the brightness, wavelength, and illumination angle of the illumination source are adjusted according to the adaptive illumination parameters. The environmental monitoring sample is then imaged under these adjusted focusing and illumination conditions to obtain a raw microscope image. Based on the environmental parameters, image restoration processing is performed on the raw microscope image to generate a target microscope image. This method solves the problem of balancing environmental adaptability and image quality in complex environmental monitoring scenarios with existing general-purpose microscopes, improving both target image quality and image adaptability.

The application relates to the technical field of overhead transmission line remote sensing monitoring, in particular to a method for positioning a center point of a transmission conductor scattering spot and determining an adaptive imaging heading angle, wherein the method for positioning the center point of the transmission conductor scattering spot comprises the following steps: acquiring a positioning deviation of a synthetic aperture radar image to correct geometric positioning errors of transmission conductor imaging; calculating a transmission conductor radar imaging simulation result according to a three-dimensional geographic coordinate of the transmission conductor and a range-doppler positioning model; and obtaining a center point positioning result of the transmission conductor scattering spot according to the transmission conductor radar imaging simulation result. Therefore, the problem of accurately simulating and positioning the center point of the transmission conductor scattering spot in a radar remote sensing image is solved. In addition, the method can be used to simulate and calculate the accurate imaging position of the transmission conductor scattering spot under different SAR sensor heading angle conditions, realize the calculation of optimal and limit heading angles, and be used for unmanned aerial vehicle SAR cruise route planning and SAR satellite orbit design.

Refractive index modulation (RIM) underpins desirable photonic functions including phase and intensity control, beam steering, dispersion tuning, and quantum signal routing. Conventional mechanisms face inherent trade-offs: large Δn but slow (thermal, liquid crystal), or fast but limited Δn (electro-optic, acousto-optic). We present a new RIM platform using nanoporous piezoelectric III-nitrides (GaN, AlN, InN, GaAlScN) with sub-wavelength pores. Dynamic pore refilling driven electrically, acoustically, or optically achieves Δneff=0.001-0.3 with sub-nanosecond response, sub-volt drive, and femtofarad capacitance. This enables compact, broadband, integrable devices for ultrafast communications, LiDAR, AR / VR, spectroscopy, adaptive imaging, and quantum photonics, unifying speed, efficiency, and multifunctionality.

Adaptive imaging methods and systems for generating enhanced low light video of an object for medical visualization are disclosed and include acquiring, with an image acquisition assembly, a sequence of reference frames and / or a sequence of low light video frames depicting the object, assessing relative movement between the image acquisition assembly and the object based on at least a portion of the acquired sequence of reference video frames or the acquired sequence of low light video frames, adjusting a level of image processing of the low light video frames based at least in part on the relative movement between the image acquisition assembly and the object, and generating a characteristic low light video output from a quantity of the low light video frames, wherein the quantity of the low light video frames is based on the adjusted level of image processing of the low light video frames.

The invention provides a real-time bidirectional matching method for video data and geographic information of an unmanned aerial vehicle. The real-time bidirectional matching method comprises the following steps: collecting and preprocessing multi-dimensional data including basic imaging parameters and scene environment parameters in real time; constructing a dynamic scene adaptive imaging projection matrix F based on the dynamic scene adaptive imaging projection matrix F; in the matching from the image to the map, after layering and distortion removal are carried out on the video image, inverse projection is carried out, coordinates are corrected in combination with an industry elevation datum, finally, GIS attributes are associated, and mapping from image pixels to geographic information with attributes is completed; in the matching from the map to the image, orthographic projection is carried out on the GIS coordinates through the matrix F, distortion is restored in a self-adaptive mode according to industry characteristics, and pixel coordinates are obtained. According to the method, the adaptive matrix is constructed by fusing multi-dimensional parameters, and industrial elevation constraint and feature adaptive processing are combined, so that the problems of single parameter, fixed model, rough correction and elevation universalization of a traditional method are solved, and high-precision real-time bidirectional matching in a complex multi-industry scene is realized.

This invention relates to the technical field of infrared imaging, specifically to an automatic imaging control method and system for infrared imaging to combat stray light. The method includes: acquiring image data and corresponding stray light data; determining the stray light imaging influence degree based on device temperature time-series data and light intensity time-series data; correcting the stray light imaging influence degree based on dust particle concentration data to obtain a strong stray light tendency coefficient; the strong stray light tendency coefficient is used to characterize the comprehensive probability of strong stray light interference in the infrared image; determining the comprehensive stray light resistance stability based on the texture difference of the image data and the radiometric stability value corresponding to the infrared image; determining the stray light suppression urgency based on the comprehensive stray light resistance stability and the strong stray light tendency coefficient; and determining the target imaging control mode based on the stray light suppression urgency and a preset threshold, so that the infrared imager performs adaptive imaging control according to the target imaging control mode. This invention can improve the accuracy of stray light imaging control during infrared imaging.

The application provides a self-adaptive imaging dual-wavelength reflection type Fourier superposition microscopic system, which comprises the following modules: a flexible light source module for bright field and dark field cooperation, which is used for improving imaging quality, reducing image acquisition time and improving resolution of reconstructed images; a high-speed self-adaptive optical module, which is used for real-time detection of image aberration and real-time compensation of the image aberration through a deformable mirror; a microscopic imaging module, which is used for realizing super-resolution image reconstruction and three-dimensional reconstruction of depth through images of dual-wavelength channels; an operation control platform, which is used for configuring a plurality of high-performance general-purpose GPUs and controlling software operation of other modules; and an image reconstruction algorithm module, which is used for realizing efficient and general multi-mode image 2D and 3D reconstruction. The application combines a deep learning-based FPM network which fuses an imaging physical model, significantly reduces data acquisition amount and acquisition time, optimizes reconstruction structure and reconstruction stability, improves the versatility of FPM for different imaging targets and realizes efficient and general multi-mode image reconstruction.

The invention discloses a visual detection method for a motor shaft. The visual detection method comprises the steps of collecting relevant parameters of the motor shaft in real time, constructing a CPS data model of the motor shaft, associating a physical detection unit with a digital twin model in real time, and performing digital mapping on a manufacturing process state and a data conversion time sequence. Adaptive imaging parameter regulation and control and multi-station cooperative imaging planning based on deep reinforcement learning are performed on the basis of a real-time state vector output by fusion of multi-source data and a digital twinborn model; image preprocessing is carried out based on the real-time state vector and the imaging parameters, and dynamic deformation is compensated by using a distortion correction algorithm based on a physical model; detecting tiny defects of the motor shaft by using a multi-scale feature fusion algorithm in combination with a multi-station image; a hierarchical attention classification network is constructed, defect hierarchical classification is carried out, a multi-modal fusion graph neural network is synchronously established to carry out diagnosis output on defect causes, and the self-adaptability and precision of motor shaft fast detection imaging are improved.

The invention relates to the technical field of airborne synthetic aperture radar systems, and discloses a modular miniature high-performance airborne synthetic aperture radar system. According to the system, under the assistance of Beidou navigation, an original echo signal is acquired and preprocessed through a radar data acquisition unit; the navigation data interface unit receives Beidou positioning information and time sequence signals and converts the Beidou positioning information and the time sequence signals into available reference of radar; the signal processing unit generates a preliminary radar image; the fusion evaluation unit aligns the image with a coordinate reference, calculates a reference scattering characteristic through a radar model, and compares the reference scattering characteristic with a measured value to obtain a deviation index; and the control execution unit dynamically adjusts the radar transmitting power, the scanning mode and the resource allocation according to the index. According to the system, real-time perception of imaging quality and automatic optimization of front-end parameters are realized, the problem of uneven imaging quality caused by fixed parameters of a traditional radar system is solved, and the adaptive imaging capability in a complex scene is improved.