435 results about "Radar imaging" patented technology

The invention provides a short-time rainfall prediction method based on an MSPTS-Net network model. The short-time rainfall prediction method comprises the following steps: acquiring a radar echo data set formed by radar echo images; a network model is constructed based on MSPTS-Net, a spatial-temporal feature extraction and fusion mechanism is embedded in a framework of the MSPTS-Net, and an initial short-term rainfall prediction model is generated; training the initial short-time rainfall prediction model by using the radar echo data set, and carrying out reverse adjustment and optimization on network parameters through a combined loss function to obtain a final short-time rainfall prediction model; and inputting a radar echo image to be measured into the final short-time rainfall prediction model to obtain a short-time rainfall prediction result. Through the MSPTS-Net model, spatio-temporal change information in radar echo data can be more comprehensively captured, multi-scale feature extraction and a self-attention fusion mechanism are utilized, data changes of different time scales and spatial scales are processed, a combined loss function is combined, the model training process is more efficient, convergence to an optimal solution can be faster, and the method is suitable for being applied to the field of radar imaging. And the precision of short-time rainfall prediction is improved.

The invention discloses a ship target tracking method based on radar imaging in a shielding environment, belongs to the technical field of target tracking, is used for ship target tracking, and comprises the following steps: obtaining multiple frames of echo images of a time sequence, obtaining a potential target on each frame by using a ship image feature detection method, and obtaining a time accumulation space diagram; initial tracks are extracted from the time accumulation space diagram, and a tracking ID is distributed to each initial track; a frame of echo image is read, the position of a potential target is detected, and the position of the potential target is associated and matched with the tracking ID; and updating the position of a potential target which is not normally associated and matched by using a ship target tracking method combining unscented Kalman filtering and LSTM. According to the method, more potential targets are extracted, and a local matching result is optimized; more and more accurate target information is provided for the VTS system, so that the VTS system can have more prior information of early warning, and safe passage of a main channel is ensured.

The invention relates to the technical field of satellite remote sensing monitoring, in particular to a method for monitoring a resident-free island by adopting satellite remote sensing, which comprises the following steps of: acquiring optical images and cloud layer data of a target island through optical and meteorological satellites, predicting a cloud shielding probability, and scheduling a radar satellite when the cloud shielding probability exceeds a threshold value; comparing the effective area of the optical image with a reference demand, triggering radar imaging when a difference value reaches a preset proportion, extracting a coastline feature point set from a historical image, calculating a spatial distribution entropy screening subset, constructing an optimized affine transformation matrix to compensate a radar image, and obtaining a new optical image when the cloud cover is low. Coastline feature point errors of the calibration radar image and the new optical image are calculated, secondary compensation is carried out, global time sequence monitoring data are generated, monitoring continuity and data consistency in a cloud shielding scene are improved, and the method is suitable for resident-free island dynamic monitoring.

The invention relates to a terahertz radar imaging system based on four-inch spinning, which belongs to the technical field of terahertz wave imaging and comprises a femtosecond laser light source, a beam splitter, a laser pumping terahertz transmitting module, a detection laser transmission module and a terahertz detection module which are arranged along the laser incidence direction. An efficient, large-size and ultra-wideband terahertz radiation source and a radar imaging device are fused, high-signal-to-noise-ratio and high-resolution terahertz imaging of a large-size target is achieved, and the system is low in cost, easy to construct, free of complex parabolic mirror combination, firm and reliable in performance and suitable for large-size target imaging. And the requirements of various engineering application scenes on synthetic aperture and nondestructive detection imaging can be practically met.

The invention discloses a single-photon laser radar imaging system and method for underwater target detection, and belongs to the technical field of detection imaging. The imaging system comprises an optical system and a control system, and the optical system comprises a transmitting unit, a receiving unit, a single-photon detector and a scanning unit. An optical path of the optical system adopts a transmitting-receiving combined optical path structure, the transmitting unit transmits an optical signal, the scanning unit receives the transmitted optical signal and a control signal and reflects an echo optical signal, the echo optical signal generated by the optical signal transmitted by the transmitting unit is received by the receiving unit, the echo optical signal is converged to the single-photon detector, and the single-photon detector is used for detecting the single-photon. The single-photon detector performs photoelectric conversion; the control system comprises an FPGA control board, and the FPGA control board is connected with a communication module, a gating signal module and a scanning signal module. The system is compact in structure and reasonable in design, detection of weak optical signals can be realized by using a reasonable optical path structure and the single-photon detector, and the farthest detection distance is increased.

The invention provides a light intensity measurement method and device based on single-photon detection and electronic equipment, and relates to the technical field of single-photon detection and laser radar imaging, and the method comprises the steps: collecting the arrival time information of photons in a plurality of single-photon detection events for each detection position; extracting a time sequence characteristic quantity related to the light intensity estimation based on the arrival time information; constructing a model input parameter for estimating the target light intensity based on the time sequence characteristic quantity; inputting the model input parameters into a light intensity estimation model, and predicting a light intensity estimation value of the target light intensity; and generating a corresponding light intensity distribution image according to the light intensity estimation value. According to the method, high-precision light intensity estimation can be realized only by a few photons, the requirements on the repeated detection times and the signal-to-background ratio are greatly reduced, and the imaging robustness is enhanced.

The invention provides a vortex electromagnetic wave radar multipath virtual image suppression method and system, and relates to the technical field of radar imaging virtual image suppression, and the method comprises the steps: adjusting the circular ring radiuses corresponding to different modes in a uniform concentric circular ring array according to the prior information of a target pitch angle, and obtaining the radar emission configuration of main lobe alignment; performing time domain sampling on the multi-path vortex radar echo signal in each mode to form a two-dimensional radar echo matrix; analyzing the obtained two-dimensional radar echo matrix and the MISO system echo after phase compensation by using an azimuth dimension point spread function to obtain dual-system imaging features; and performing modal domain FFT and time domain pulse compression processing on the multipath echo features under the MISO and MIMO systems by using fast Fourier transform and pulse compression, and finally outputting a virtual image suppressed synthetic radar image. The multi-path virtual image suppression method has the beneficial effects that the suppression of the multi-path virtual image is realized by utilizing the characteristic that the real image positions of the imaging results are completely the same and the virtual image positions are different under the multi-transmitting and single-receiving systems.

The invention discloses a speed ambiguity resolution method suitable for 4D millimeter wave radar imaging. The method comprises the steps of 1, acquiring radar original target sampling data, and performing two-dimensional fast Fourier transform processing to obtain a target distance-Doppler matrix; carrying out constant false alarm rate detection on the target distance-Doppler matrix; 2, searching an optimal representative point as a speed representative point of the target; 3, adopting a hypothetical phase compensation speed ambiguity resolution algorithm for the speed representative point of the target to obtain a speed and a phase compensation value corresponding to the representative point; step 4, compensating all target points with phase compensation values calculated according to the representative points, and performing DOA estimation to obtain azimuth angle values; 5, coordinate transformation is carried out according to the obtained target point speed and azimuth angle value, the real position of the target under the three-dimensional coordinate system is obtained, and the representative point speed represents the real speed of the target. According to the invention, the detection capability of the MIMO radar imaging system on a high-speed target can be improved.

The invention discloses a preprocessing and compression method and system for single-photon laser radar data, and belongs to the technical field of single-photon laser radar data processing. According to the method, firstly, a target distance image is collected by means of a single-photon laser radar imaging system, and original data is provided for subsequent processing; thirdly, constructing a frequency-time histogram for each pixel point of the acquired image so as to visually present the distribution condition of photons in the time dimension; then, Gaussian fitting and peak value screening operation is carried out on the histogram of each pixel point, effective photon signal peak values are accurately identified, false signals caused by noise and interference are eliminated, and the data quality is improved. And finally, inputting the photon data corresponding to the screened peak values into a multi-attention three-dimensional single-photon compression neural network, and realizing high-efficiency compression of the single-photon laser radar data by using the strong feature extraction, high-efficiency compression mechanism and flexible output control capability of the network, thereby providing powerful support for the application of the single-photon laser radar technology in multiple fields.

A radar system comprises a physical radar array including a plurality of physical transmit antennas, each configured to transmit a respective signal having a wavelength λ. The physical radar array further comprises a plurality of physical receive antennas, each configured to receive each of the respective transmitted signals. At least one of the plurality of physical receive antennas is positioned at a different elevation than the other receive antennas. In some embodiments at least one of the plurality of physical transmit antennas is positioned at a different elevation than the other transmit antennas. In some embodiments the radar system further comprises a neural network arranged to receive data from the physical radar array and to generate or update data for one or more missing virtual antennas, generate a new fraction of the virtual antennas, generate the full set of virtual antennas, or directly generate the processed data.

The invention discloses a scanning radar super-resolution imaging method based on data-driven adaptive parameter learning, which is applied to the technical field of radar imaging and aims to overcome the defects that a traditional super-resolution method is high in parameter sensitivity and an existing network expansion method has a redundant structure and poor adaptability. The method comprises the following steps: firstly, converting a non-differentiable L1 regularization problem into a differentiable L2 problem based on a maximum-minimum principle; secondly, expanding an iteration process into a multi-stage cascade neural network, and performing adaptive learning on regularization parameters in a data driving mode; and finally, layered parameter optimization and super-resolution reconstruction are realized through an end-to-end network architecture. By adopting the method of the invention, the super-resolution imaging performance of the radar is effectively improved.

The invention discloses an I SAR fusion imaging method, system and device based on centralized multi-station networking, and relates to the technical field of radar imaging, and the method comprises the steps: obtaining I SAR frequency domain echo data of each subsystem; performing frequency domain space matching on the frequency domain echo data based on the observation angle difference of each radar; performing frequency domain alignment processing on the matched frequency domain data; and fusion and imaging are carried out. According to the I SAR fusion imaging method, system and device based on the centralized multi-station networking, the imaging resolution is remarkably improved, multi-station frequency domain data are mapped to a unified local frequency space through a coordinate transformation matrix, non-uniform data are subjected to uniform reconstruction in combination with a self-adaptive interpolation algorithm, and the imaging resolution is remarkably improved. The frequency domain integration interval is effectively expanded, compared with a single-station I SAR which only covers a local area of the total rotation angle of the target, the range resolution is further optimized through multi-station bandwidth synthesis in the range direction, and high-fidelity reconstruction of the target in the complex shape is achieved.

The invention discloses an infrared dual-band single-photon laser radar imaging system, and belongs to the field of laser radar detection. The system comprises an infrared dual-band light source, a beam splitter, a first delay line, a light splitting module, a transceiver module, a frequency up-conversion module, a photoelectric detector, a single-photon detector, a signal acquisition module and a signal processing module. The infrared dual-band light source generates near-infrared and intermediate-infrared pulse laser; near-infrared laser is used for near-infrared detection, frequency up-conversion pumping and initial signals after passing through a beam splitter, intermediate-infrared laser is used for intermediate-infrared detection, intermediate-infrared detection signal echoes are subjected to time domain gating and frequency up-conversion by near-infrared pumping signals and then injected into a single-photon detector together with near-infrared detection signal echoes through time division multiplexing, and the intermediate-infrared detection signal echoes and the near-infrared detection signal echoes are subjected to frequency up-conversion. And detection imaging is realized through signal acquisition and processing. According to the invention, the weak target detection imaging capability of the single-photon laser radar in environments of gas, strong background noise and the like can be improved, and the multi-band sensing capability of the radar on target characteristics is improved.

The invention discloses a side slope risk monitoring method and system based on three-dimensional laser and two-dimensional phase sweeping radars, and the method comprises the steps: S1, obtaining a digital elevation model of a side slope through a three-dimensional laser radar, and obtaining a radar imaging result of the side slope through the two-dimensional phase sweeping radars; s2, performing coordinate system unification and projection conversion on the digital elevation model and the radar imaging result to generate a conversion relation lookup table; s3, obtaining a real deformation value of the target based on the digital elevation model, the radar imaging result and the conversion relation lookup table; s4, performing time sequence analysis on the real deformation value of the target, judging the state of the slope, and calculating a tangent angle to determine an early warning level when accelerated deformation is recognized; s5, automatically switching the working mode of the two-dimensional phase sweeping radar according to the early warning level; and S6, in the moving target monitoring mode, tracking and predicting the moving target, and performing collision judgment and target re-identification. The method has the advantages of high precision, intelligence and the like.

The invention belongs to the technical field of slope monitoring and early warning, and provides a slope progressive failure precursor identification method based on deep learning. On the basis of 4D point cloud imaging radar data, surface patches are divided according to slope sections, rock stratum boundaries and the like, attributes are marked, a directional kernel is constructed in the dominant direction and the normal direction, and density, flatness, roughness, edge feeling, pore feeling and other characteristics and state labels are extracted through independent coding of all moments; constructing a normal learning window and a usual trajectory based on environmental conditions, training a conditional variation auto-encoder which only learns a normal form, setting two interpretable dimensions of pore evolution and edge enhancement, and applying direction consistency and state constraint; according to the reconstruction deviation degree and the consistency of adjacent surface patches, the abnormal trend that the density is reduced, the roughness / edge is increased and the pores are increased in the dominant direction is recognized online, and encryption observation is triggered for side slope precursor recognition and early warning; the method does not depend on cross-time displacement regression, and the influence of environmental disturbance can be inhibited.

The invention discloses an efficient pipeline double-DDR-group SAR imaging data storage and transposition system based on an FPGA, and belongs to the technical field of radar imaging signal processing. An efficient DDR group state machine is configured in the system, each DDR has seven different states, two frames of radar data can be processed in total after running for one time, one DDR reads data, then the data is processed and transmitted to the other DDR for data writing, one DDR performs writing operation while reading the other DDR, and the data writing efficiency is greatly improved. High-speed pipeline multi-path parallel data processing is realized, so that time is saved to the greatest extent, and high real-time performance of the whole radar imaging system is ensured.

The invention discloses a long-distance wide-area high-resolution single-photon radar imaging method. The method comprises the following steps: S1, constructing a system; s2, the distance is detected, and the long-distance wide-area high-resolution problem is solved; s3, receiving the data, and solving the problems of data processing and storage; s4, detecting an imaging signal, and solving the problems of signal attenuation and interference; s5, after the problem is solved, testing work is carried out through single-photon radar imaging; s6, after the test work is completed, detecting and patching problems again; and S7, setting a problem triggering and vacancy supplementing system, and timely supplementing the problem when the problem occurs in the single-photon radar imaging. The method has the beneficial effects that clutter interference can be effectively reduced by arranging and removing clutter devices generated in the single-photon radar, and software and hardware configuration in an internal system is optimized, so that the reliability of the single-photon radar imaging is improved, and the reliability of the single-photon radar imaging is improved. Cooperative operation among all components in the system is ensured, so that the problem of long distance and wide area during single photon radar imaging is solved.

The invention discloses a non-orthogonal waveform MIMO radar imaging method based on deep learning, and the method comprises the following steps: constructing an MIMO radar echo signal model, and obtaining a target echo signal in a whole imaging scene; separating signals of different waveforms in the target echo signal under the condition that the transmitted waveform satisfies an ideal orthogonal condition to obtain waveform vectors corresponding to the different waveforms, and performing azimuth compression to obtain an ideal target image of the MIMO radar; taking frequency domain echo signals at different receiving array elements in an MIMO radar echo signal model and the ideal target image as a training set, and training the constructed deep network model to obtain a trained deep network model; performing target imaging on the actual target echo signal by using the trained deep network model; according to the invention, the echo signal is processed through the trained deep network echo, the coupling interference between waveforms can be effectively suppressed, and the target high-resolution image can be efficiently acquired.

The invention discloses an unmanned aerial vehicle intelligent identification device based on FAST site dangerous rock inspection. The unmanned aerial vehicle intelligent identification device comprises an unmanned aerial vehicle, a laser radar, a surveying and mapping camera and a D-RTK mobile station, after the unmanned aerial vehicle takes off, inertial navigation calibration is firstly carried out at the high altitude, the unmanned aerial vehicle flies upwards in a bow-shaped circulation mode from the foot of a mountain body at the station site 1H, dangerous rock point cloud data of the high and steep slope around the station site are rapidly obtained through a laser radar and a surveying and mapping camera carried by the unmanned aerial vehicle, the three-dimensional geometrical characteristics of the dangerous rock mass are extracted, and the isolated dangerous rock mass of the high and steep slope is intelligently and efficiently recognized. Through laser radar imaging equipment carried by the unmanned aerial vehicle, high-density point cloud data of the high and steep slope around the station site is rapidly obtained, three-dimensional geometric features of the dangerous rock mass are extracted, and the isolated dangerous rock mass of the high and steep slope can be accurately, intelligently and efficiently identified. The method is of great significance to stable and reliable operation and scientific achievement output of a 500-meter-caliber spherical radio telescope (FAST) which is a national important scientific and technological infrastructure.

The invention belongs to the technical field of radar signal processing and imaging, and particularly relates to a distributed MIMO radar imaging method based on multi-technology fusion, which comprises the following steps: S1, constructing a distributed MIMO radar array: constructing a distributed array consisting of L identical MIMO radar units, each unit comprising M transmitting antennas and N receiving antennas; the multi-radar synchronous imaging method is characterized in that each radar unit adopts an offset frequency orthogonalization design, signals of each radar unit and different transmitting antennas in the same unit are mutually orthogonal in a time domain and a frequency domain by elaborately setting the offset frequency of each transmitting antenna, a virtual array is equivalent to a uniform linear array, and multi-radar synchronous imaging is realized by utilizing a high-precision synchronous control mechanism. According to the method, the equivalent aperture is greatly expanded through cooperative work of the distributed MIMO radar arrays, so that the angular resolution is remarkably improved, the accuracy and reliability of echo signals are guaranteed by adopting a unique orthogonal signal design, and the imaging stability is improved.

The present invention discloses a sparse aperture ISAR maneuvering target imaging and calibration method, comprising the following steps: establishing a maneuvering target ISAR sparse aperture signal model, characterizing the maneuverability of the target by constructing a variable scale non-uniform Fourier two-dimensional dictionary containing MTRC; constructing a parameterized sparse imaging model by maximum a posteriori estimation, expressed as a cost function of joint target parameter estimation and sparse imaging; finding the target rotation parameter by the minimum entropy method, and implementing a rough estimation of the maneuvering target rotation parameter by performing parameter iteration by the coordinate descent method; and implementing high-quality imaging with MTRC correction and accurate parameter estimation by combining sparse ISAR imaging and target rotation parameter estimation, decomposing into sparse imaging and dictionary learning, and iterating and solving in sequence. The present invention realizes the use of sparse aperture echo data to solve the problem of high-resolution imaging of ISAR maneuvering targets, greatly increasing the implementation scope and application scenarios of radar imaging.

The present invention discloses a high-resolution imaging method for a scanning radar under low signal-to-noise ratio conditions, comprising: Step 1: Modeling the echo signal of the scanning radar and initializing the scattering point power matrix; Step 2: Calculating the covariance matrix according to the scattering point power matrix; Step 3: Modeling the radar imaging problem as a least-squares problem by using the covariance matrix and calculating the least-squares solution of the target scattering intensity; Step 4: Calculating the scattering point power matrix according to the least-squares solution of the target scattering intensity; Step 5: Removing the smaller values in the scattering point power matrix to update it; Step 6: Repeating the operations of Step 2 to Step 5 until the maximum number of iterations is reached. The present invention applies the iterative adaptive method based on the least-squares method to scanning radar imaging, and removes the smaller values in the scattering point power matrix during the iteration process, making the target signal more prominent, improving the azimuth resolution under low signal-to-noise ratio conditions, and enhancing the imaging effect.

The invention discloses a radar foresight super-resolution imaging method based on echo energy correction, belongs to the technical field of radar imaging, and aims to solve the problem of imaging errors caused by speed errors of a traditional imaging method on a high-speed motion platform. The speed of the motion platform is accurately estimated based on an echo energy correction criterion, so that the influence of the motion error of the platform on the super-resolution imaging performance is effectively reduced, and the dependence of a traditional method on high-precision external speed measurement equipment is overcome. The method comprises the following steps: firstly, constructing a reconvolution model containing Doppler phase information; secondly, estimating the speed of the platform based on an echo energy correction criterion so as to obtain accurate speed information; then, updating a Doppler phase matrix by using the corrected speed, and correcting a reconvolution echo model; and finally, proposing a sparse super-resolution algorithm based on power weighting, and optimizing the target power and the noise power through alternate iteration to realize robust super-resolution imaging under the motion platform, thereby remarkably improving the target recognition and situation awareness capabilities of the motion platform.

The invention discloses an SAR intelligent refocusing method based on deep learning. The method mainly solves the problems that in traditional SAR image refocusing, phase error modeling is complex, the image resolution is low, and details are lost. According to the implementation scheme, the method comprises the following steps: acquiring a data set of an SAR pure image and a corresponding defocused image, and dividing the data set into a training set, a verification set and a test set; constructing a deep learning network comprising an encoder and a decoder; using the training set to carry out forward and back propagation iteration to train the network, and evaluating and updating model parameters on the verification set to obtain a trained network model; and inputting a defocused image in a test set into the trained network model, extracting image features through an encoder, and reconstructing a high-resolution image through a decoder to obtain a restored image result. According to the method, the deep features of the SAR image can be effectively extracted, gradient disappearance is relieved, features of different levels are effectively fused, the reconstruction quality and resolution of the SAR image are improved, the detail performance of the restored image is enhanced, and the method can be used for radar imaging and evaluation.

The invention relates to the technical field of campus safety protection, in particular to a method and system for preventing campus bullying by using a laser radar imaging technology, and the method comprises the following steps: obtaining 3D point cloud data of an environment in a monitoring region preset in a campus, and collecting sound data in the monitoring region; processing the 3D point cloud data, generating a three-dimensional environment image and extracting a first behavior feature; processing the sound data, and extracting a second behavior feature; performing multi-modal fusion on the first behavior feature and the second behavior feature to generate a unified behavior feature vector; based on the behavior feature vector, identifying whether a bullying behavior exists in the current scene, and calculating a corresponding behavior risk score; according to the method and the device, whether the preset alarm threshold value is reached or not is judged, and if yes, corresponding alarm and early warning operation is triggered, so that non-intrusive active monitoring of non-appearance privacy leakage of the campus secret corner is realized, and the problems of privacy limitation and blind areas of traditional video monitoring are solved.

The invention discloses a correction method for slope radar monitoring data. The correction method comprises the following steps: fitting to obtain a fitting function calculation model between the coordinate offset of a slope radar and the offset monitoring duration; counting slope monitoring time points corresponding to monitoring of the slope radar on the to-be-monitored area of the surface mine each time, and forming a slope monitoring time axis and slope monitoring data; counting the time point of each time of displacement monitoring of the side slope radar by the GNSS displacement detector, forming a monitoring time axis of the mine displacement, and calculating the coordinate offset of the side slope radar obtained by the displacement monitoring of the GNSS displacement detector; determining the displacement starting time of the slope radar in combination with the coordinate offset monitored by the GNSS displacement detector and a fitting function calculation model, calculating the radar displacement duration, calculating a coordinate offset coefficient corresponding to a slope monitoring time point in the radar displacement duration, and performing coordinate correction on the monitoring point; according to the invention, the correction accuracy of the radar image and the slope deformation identification accuracy are improved.

The invention provides a multilayer medium efficient imaging method suitable for frequency modulation continuous wave ice radar data, and belongs to the field of frequency modulation continuous wave ice radar imaging. Comprising the steps of multi-layer medium signal model construction, improved distance-Doppler algorithm implementation, distance migration accurate correction and azimuth focusing processing. According to the method, a multi-layer medium signal model containing refractive index and propagation velocity changes is established for the problem of target distance distortion caused by propagation velocity differences of electromagnetic waves in different media such as ice and snow; based on the model, a traditional distance-Doppler algorithm process is improved, distance migration caused by multiple layers of media is accurately corrected by introducing a medium-dependent distance migration factor, and efficient azimuth focusing is achieved. And finally, a high-resolution ice layer internal structure image can be generated, and the method is suitable for polar region large-range ice cover detection and rapid processing of a large amount of ice radar data.

The invention belongs to the technical field of laser radar scanning control, and discloses an MEMS galvanometer scanning method and a laser radar system.The method comprises the steps that firstly, an electrostatic drive MEMS galvanometer dynamic model with external disturbance is established, and a self-adaptive nonsingular terminal sliding mode controller based on a nonlinear extended state observer is constructed; therefore, trajectory tracking control of the MEMS galvanometer is realized. The laser radar system comprises a laser, an MEMS galvanometer, a driving circuit, a voltage amplifier, a controller based on an FPGA, a position sensor and the like, and realizes accurate trajectory tracking control of a laser beam based on an MEMS galvanometer scanning method. According to the MEMS galvanometer scanning method, the system state can be converged to the sliding mode surface within the limited time through the self-adaptive nonsingular terminal sliding mode controller, the problem of singular values is avoided, the stability and precision of the MEMS galvanometer during tracking of the scanning track are guaranteed, and the laser radar imaging quality can be improved.

The invention belongs to the technical field of radar imaging, and particularly relates to a radar sparse three-dimensional imaging and resource scheduling method based on ring array nesting, which comprises the following steps of: establishing a sparse imaging model under an OFDM (Orthogonal Frequency Division Multiplexing) framework; providing a sparse two-dimensional imaging method; proposing a pitch angle reconstruction method based on phase interference; calculating imaging parameters required by resource allocation in the multi-target imaging scene; and establishing an adaptive resource scheduling optimization model and providing a solving method. According to the radar sparse three-dimensional imaging method based on circular ring array nesting, three-dimensional high-resolution imaging is achieved under the condition that a small number of radar resources are consumed. And a self-adaptive resource scheduling algorithm is provided, so that a multi-target imaging self-adaptive resource scheduling model is established by taking time saving, improvement of array element and frequency band utilization rates and balance of radiation intensity of a mode selected by each target as optimization targets, and reasonable allocation of limited radar resources is realized.

The invention relates to an SAR image fusion method, device and equipment in a terahertz frequency band bunching mode, and the method comprises the steps: carrying out the preprocessing of a terahertz bunching mode video, and enabling a reference image and a to-be-fused image to be projected to a ground plane through combining the linearity and interpolation of a radar imaging digital elevation model, thereby obtaining a corresponding projection image; constructing an anisotropic diffusion scale space image for the two types of projection images, generating a Harris function scale space image according to the anisotropic diffusion scale space image, extracting extreme points, constructing SIFT feature descriptors of the two types of images based on the extreme points, obtaining an initial feature matching point pair set through similarity comparison, obtaining an optimized point pair set after multiple times of screening, and obtaining an anisotropic diffusion image through the optimized point pair set. And calculating an image conversion affine matrix by least square matching, fusing the to-be-fused image and the reference image according to the matrix to obtain a fused image, updating the image as the reference image, and repeatedly fusing the reference image with the next to-be-fused image until all frames are fused to obtain a final fused image. By adopting the method, the quality of the fused image can be effectively improved.