1151 results about "Radar signals" patented technology

The invention discloses a hydropower station water level detection method and system. The method comprises the steps that an original water flow characteristic parameter set is obtained through multiple sets of Doppler effect radar flow measuring devices; performing segmentation extraction to obtain a water flow feature sub-data set; inputting a Transform network coding layer to generate a water flow feature coding vector; outputting a preliminary water level predicted value sequence through a decoding layer; a radar flow measurement model is called for correction to obtain a correction sequence; time sequence integration is carried out to obtain a water level change curve. The system comprises a multi-source Doppler radar signal acquisition unit, a spatial-temporal feature segmentation extraction unit, a Transform coding processing unit, a water level preliminary prediction unit, a radar model correction unit and a time sequence integration output unit. According to the method and system, multi-source parameters are deeply fused, the environmental adaptability is improved, the continuity and reliability of a detection result are enhanced, the water level of the hydropower station can be accurately monitored, and support is provided for scheduling decision and safety prevention and control.

The invention relates to the technical field of intelligent household appliances and man-machine interaction, and discloses an electric cooker anti-noise voice interaction system based on multi-mode perception and a control method, and the system comprises a multi-mode perception and collection unit which synchronously collects millimeter wave radar echo signals and acoustic signals; the signal preprocessing and feature extraction unit is used for extracting a user physiological vibration signal and a three-dimensional space position vector from the radar signal and extracting an acoustic energy envelope and a sound source direction vector from the acoustic signal; the time-space consistency verification unit and the voice gating and recognition unit are used for carrying out time synchronization verification by calculating the correlation between the physiological vibration signal and the acoustic energy envelope, and distinguishing real human voice from an environment false trigger source; meanwhile, space consistency verification is carried out by comparing the user direction of radar positioning with the sound source direction of acoustic positioning, so that non-target human voice interference is eliminated. According to the invention, the anti-interference capability and reliability of voice interaction in a real home environment are improved through double verification on a physical level.

The invention discloses a semi-supervised LPI radar signal modulation identification system and method based on entropy perception pseudo labels, and relates to the technical field of radar signal processing and mode identification. The system comprises a preprocessing module, a multi-scale reconstruction enhancer, a classification backbone network and a semi-supervised training module. The multi-scale reconstruction intensifier is used for reconstructing dual-channel separation through high-frequency detail enhancement and a low-frequency structure and enhancing discriminative characteristics in a noise environment; the classification backbone network introduces an adaptive contraction unit to realize channel-level noise suppression; and the semi-supervised training module dynamically evaluates the uncertainty of the unlabeled samples by adopting an entropy sensing mechanism, and generates weighted pseudo labels to carry out consistency regularization training. The method realizes signal modulation identification based on the system. According to the method, the problem of feature shielding under the condition of low signal-to-noise ratio is solved, the dependence of the model on labeled data is reduced through a reliable pseudo label generation mechanism, and stable and efficient modulation identification can still be realized in a severe channel environment with scarce labeled data.

A non-contact real-time monitoring system of physiological signs based on millimeter-wave radar includes a millimeter-wave radar and multiple modules for processing radar signals. The millimeter-wave radar is configured to continuously transmit electromagnetic wave signals and simultaneously receive echo signals, perform frequency mixing processing on the echo signals to obtain an intermediate frequency signal, and process the intermediate frequency signal to obtain a radar four-dimensional data matrix. Human body physiological signs are monitored by analyzing body thoracic cavity micro-motion information in signals through the modules; a target echo is processed by adopting a constant false alarm rate detection algorithm, and invalid signals are filtered. A self-adaptive range cell selection algorithm based on short-time stability of respiratory signals is adopted to capture radar echoes reflecting physiological movement. Mixed human body physiological sign signals are processed by using a VMD algorithm, and key parameters in VMD are optimized by using a GWO algorithm.

The invention relates to the technical field of target detection and identification, and discloses a low-altitude target identification system and method combined with multi-band signal processing. Comprising the steps of establishing a cross-frequency implicit vector model and constraining a geometric scale, a scattering center and rotor harmonic waves through synchronous acquisition of an L-band radar, a Ku-band radar and a millimeter-wave radar, and realizing unified characterization of multi-frequency signals; receiving a global satellite navigation system reflection signal, communication channel state information or a very high frequency / ultrahigh frequency passive radar signal to form prior discrimination, and triggering high-frequency radar short-time imaging; carrying out propagation correction on millimeter wave, terahertz and infrared signals in combination with a meteorological profile and generating an environment label; determining a sampling window by using the region of interest and rotor phase prediction, and optimizing a residence strategy; harmonic waves are extracted from radar and acoustic signals, robust pairing is carried out, and a cross-domain consistency score output category is calculated. The method realizes multi-frequency multi-source cooperative processing, has environment self-adaption and closed-loop feedback capabilities, and can improve the precision and stability of low-altitude target identification.

The invention relates to the technical field of data processing, and discloses a low-altitude radar information processing system based on big data analysis. The system comprises a data fusion module, a false alarm suppression module, a measurement target identification module and a grading early warning mechanism establishment module. Firstly, low-altitude radar signals are collected, space-time reference synchronization is carried out, original low-altitude radar data are obtained, and then data association matching is carried out by using a clustering fusion algorithm; secondly, optimizing the threshold value by using a multi-strategy fusion particle swarm algorithm, and performing false alarm suppression; establishing a three-path feature network model based on a multi-modal neural network architecture, carrying out target recognition, and outputting a low-altitude radar measurement target recognition result; and finally, according to a low-altitude radar measurement target identification result, carrying out environment constraint integration, generating a radar target motion track, and establishing a grading early warning mechanism. According to the method, low-altitude radar data are processed and modeled, the purpose of low-altitude radar information processing is achieved, and the method is accurate and objective.

The invention provides a low signal-to-noise ratio-oriented deep generative adversarial radar signal enhancement method and system. The method comprises the following steps of: extracting target scattering statistical characteristics from a preset historical database; generating an emission polarization state configuration instruction set according to the target scattering statistical characteristics; according to the emission polarization state configuration instruction set, obtaining an emission signal with a set polarization state parameter corresponding to the optimal emission polarization state, and emitting the emission signal in a low signal-to-noise ratio environment; receiving an echo signal generated after the signal is transmitted, and generating a noise polarization state parameter; obtaining a polarization filtering signal based on the noise polarization state parameter and a set polarization state parameter; based on target scattering statistical characteristics, obtaining a reconstructed polarization filtering signal, and obtaining an enhanced radar signal; according to the technical scheme provided by the invention, deep generative adversarial network reconstruction fusing optimal polarization emission, adaptive polarization filtering and target scattering characteristics is realized, and the radar echo signal quality and detection performance are improved in a low signal-to-noise ratio environment.

The invention belongs to the technical field of target detection, and particularly relates to a radar image restoration and small target detection method based on adaptive sparse modeling, and the method comprises the steps: collecting original radar signal data, obtaining an initial low-quality radar image through preprocessing, and constructing a signal feature template; carrying out adaptive step size division, sparse decomposition and dynamic threshold judgment to obtain a characteristic coefficient matrix; a double-norm dynamic weighting optimization model and a local noise sensing mechanism are utilized, and alternate iterative optimization is combined, so that a high-quality restored image is obtained; obtaining a signal point enhanced image through a three-scale Gaussian kernel collaborative detection and information entropy quantization weighted fusion strategy; outputting a signal point correlation structure chart and a structured feature matrix; and outputting a small target category probability through training and parallel reasoning by adopting a fusion network of an image path and a graph structure path. The method has the capabilities of small target high-precision detection, cross-equipment efficient adaptation and real-time processing in a complex scene.

The invention belongs to the technical field of human body posture reconstruction, and particularly provides an MIMO millimeter wave radar and infrared camera fused human body posture reconstruction method which is used for solving the problems that an existing human body posture reconstruction method is low in resolution, poor in stability, poor in reconstruction effect and the like. According to the method, a two-dimensional spatial spectrogram of a human body is extracted through a radar signal preprocessing algorithm, and two human body posture feature representations are obtained in combination with synchronously collected infrared images; then a fusion human body posture reconstruction model based on the MIMO millimeter wave radar and the infrared camera is constructed, effective extraction and fusion of two kinds of human body posture feature data are achieved through the fusion human body posture reconstruction model, high-precision and stable human body posture reconstruction is completed, and finally stable perception of the human body posture is achieved.

The invention relates to the technical field of smart home control, and particularly discloses a smart home central control system and method based on multi-mode perception, and the system comprises the steps: synchronously collecting a voice audio signal, a gesture image signal, an infrared thermal imaging signal and a millimeter wave radar signal through a plurality of groups of sensors; performing blind source separation processing on the voice and gesture signals, extracting a voice command component and a gesture action component which are independent in statistics, and performing space-time alignment and Kalman filtering fusion on the infrared and radar signals to generate a dynamic environment sensing graph; voice intention features, gesture track features and environment anomaly features are extracted through Mel frequency cepstrum coefficient analysis, skeleton key point tracking and multi-level convolution processing; constructing a three-dimensional decision matrix based on the features, performing weighted evaluation through a fuzzy logic rule base to generate a control instruction priority sequence, and dynamically adjusting an equipment operation mode according to the priority; according to the invention, the problems of control conflict and response delay caused by multi-mode signal coupling are solved.

The invention discloses a self-adaptive phased array radar and method for severe convection weather detection, and relates to the technical field of meteorological remote sensing detection. The adaptive phased array radar comprises an active electronic scanning array front-end unit which is used for receiving a radar scanning instruction, generating and transmitting an electromagnetic beam based on the radar scanning instruction, receiving a meteorological target scattering echo and generating echo data; the radar signal and data processing unit is used for processing the echo data based on the signal processing parameter instruction so as to generate a standardized three-dimensional meteorological data set; and the cognitive control and resource management unit is used for identifying a region of interest from the standardized three-dimensional meteorological data set and updating a radar scanning instruction and a signal processing parameter instruction based on the region of interest. According to the invention, active, intelligent and adaptive monitoring of strong weather is realized, and the accuracy and reliability of convective weather detection are improved.

The invention relates to the technical field of unmanned vehicles, and discloses an unmanned vehicle auxiliary lane changing decision-making method based on multi-modal fusion, and the method comprises the steps: synthesizing a visual image of a densely overlapped vehicle contour through a generative adversarial network, shielding missing laser point cloud, radar signals of clutter interference, and other analog data; samples are expanded in combination with oversampling and transfer learning, and the problem of insufficient sample size is solved. A dynamic attention mechanism is utilized, correlation modes of multi-modal features such as vision, laser and radar in the scene are mainly learned, the scene is quickly recognized through a long-tail scene adaptation module of a meta-learning framework, and decision parameters are dynamically adjusted. In training, a mixed loss function is adopted to reinforce learning, sudden plug triggering is re-evaluated during real-time decision making, and meanwhile, the model is continuously optimized by relying on a feedback iteration mechanism. Finally, the model can accurately identify a plugging scene, the safety space is judged by fusing multi-modal features, dynamic changes are dealt with, and the traffic accident risk caused by plugging is reduced.

The invention discloses a shipborne radar signal source with zero-discontinuity parameter switching capability, which relates to the technical field of shipborne radars and comprises a transmitting end control unit and a radio frequency output unit. The transmitting end control unit is sequentially provided with a parameter management and pre-installation layer, a waveform synthesis layer, a deterministic delay and switching control layer, a time base consistency and channel calibration layer and a recording and diagnosis layer from the top layer to the bottom layer to form complete link output; the radio frequency output unit is provided with a digital-to-analog conversion and intermediate frequency up-conversion link and an external up-conversion and power amplifier link. A double DDS ping-pong and pre-installed preheating switching mechanism, a phase state copying and aligning mechanism, a delay calibration and on-line compensation mechanism and a multi-channel time base consistency and channel calibration mechanism are provided, the problems of interruption, amplitude phase jump, moment jitter, poor multi-channel consistency and the like existing in the parameter switching process of an existing simulation source are solved, and meanwhile the simulation source has the event recording and diagnosing functions. And the reliability and the accuracy of the electric detection equipment on the sea test are ensured.

The invention relates to the technical field of data processing, and discloses an integrated reconnaissance interference method and system based on a transmit-receive cancellation technology, and the method comprises the steps: receiving a mixed signal containing a target radar signal and a leakage interference signal through a receiving channel, and obtaining a first digital signal after down-conversion and analog-to-digital conversion, the leakage interference signal is a signal leaked from the transmitting channel to the receiving channel; acquiring a second digital signal corresponding to an interference signal generated in the transmitting channel, and transmitting the interference signal through an antenna after digital-to-analog conversion and up-conversion; taking the second digital signal as a reference signal, performing adaptive cancellation processing on the first digital signal through an adaptive filter to suppress a leakage interference signal component in the first digital signal, and generating a cancelled signal; wherein in the adaptive cancellation processing process, the digital processing unit adaptively updates the cancellation filtering weight of the adaptive filter according to the reference signal and the cancelled signal.

The invention discloses a radar system clutter background modeling and adaptive suppression method and system, and relates to the technical field of radar signal target detection. The radar system clutter background modeling and adaptive suppression method comprises the following steps of covariance matrix construction, clutter model construction, clutter adaptive suppression and adaptive suppression processing optimization. According to the method, covariance matrix stability evaluation is carried out through the constructed covariance matrix, whether covariance matrix correction is carried out is judged, then the initial clutter model is established according to the covariance matrix and the preset probability distribution model, then clutter model adaptability evaluation is carried out, whether clutter model optimization is carried out is judged, and clutter model optimization is carried out. According to the method, the radar signal is subjected to self-adaptive suppression processing, and finally the judgment result of self-adaptive suppression processing optimization is obtained based on the signal-to-noise ratio of the radar signal after self-adaptive suppression processing, so that the clutter suppression effect in the radar signal is improved, and the problem of poor clutter suppression effect in the radar signal in the prior art is solved.

The invention discloses a radar-based communication method, a transmitting end radar, a receiving end radar, a radar-based communication system, a vehicle, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring to-be-sent target communication data; a first radar signal corresponding to the target communication data is sent according to the target communication data, under the condition that the sending end radar sends the first radar signal, the first radar signal received by the receiving end radar is at least interfered with the intensity corresponding to the first radar signal, and the receiving end radar sends the target communication data according to the received first radar signal. And determining the interference intensity of the first radar signal, and determining target communication data corresponding to the interference intensity according to the interference intensity. Therefore, in a rain and fog scene, the rear vehicle and the front vehicle can communicate through the radar to share information, so that the sensing range is expanded, and the steady driving of the vehicle and the driving safety of a user are guaranteed.

The invention discloses a liquid level sensor device of a pesticide preparation tank, and relates to the technical field of liquid level measurement. The device comprises a first acquisition module for acquiring internal component parameters of a pesticide preparation tank and working condition parameters of a preparation process, and an initial mounting position determination module for determining an initial mounting position of a radar liquid level sensor according to related parameters; after the second acquisition module acquires liquid level data and an original radar signal, the change amplitude calculation module calculates the change amplitude of the stirring speed, and if the change amplitude exceeds a threshold value, the dynamic adjustment module dynamically adjusts the position of the sensor; and the liquid level compensation value generation module generates a compensation value by using a trained liquid level dynamic compensation model of correlation mapping of the stirring speed and the measurement error in combination with the liquid level data, the original radar signal and the working condition parameters, and finally, the dynamic fusion module fuses the initial liquid level data and the compensation value to obtain an accurate liquid level value. According to the method, the accuracy of liquid level measurement in the pesticide preparation process can be effectively improved by dynamically adjusting the position of the sensor and based on multi-parameter dynamic compensation.

The invention relates to the technical field of ground penetrating radar data processing, in particular to a hidden disease-oriented ground penetrating radar space-time registration and detection method. Comprising the following steps: S1, establishing a continuous mapping relation between a radar signal and a geographic space; s2, constructing a local trajectory indexing mechanism based on anisotropy measurement; s3, constructing a space-time joint correction space based on error prior distribution; s4, performing parameter inversion based on maximum posterior probability estimation constraint; s5, establishing a registration quality evaluation and signal feature decoupling mechanism; and S6, constructing a dielectric priori driven dual physical attribute index and adaptive discrimination model. According to the method, the influence of GPS positioning errors and track swing is overcome, centimeter-level physical space alignment is realized, and false alarms caused by registration deviation are remarkably reduced; by introducing the physical constraint of the frequency dimension, the signal-to-clutter ratio under the complex city background is improved, and the false alarm rate is remarkably reduced.

The invention provides a tunnel hidden defect simulation detection system, and relates to the technical field of tunnel detection. The method comprises a lining structure, a hidden defect simulation assembly, a radar assembly and a signal processing assembly. The lining structure comprises reinforcing meshes with different mesh sizes and internal defect simulation components; the reinforcing meshes with different mesh sizes are used for simulating the reinforcing meshes under different conditions, and the internal defect simulation component is used for simulating a lining structure under different internal defects; the hidden defect simulation assembly comprises a frame, a hidden defect simulation component and surrounding rock; the radar assembly is used for transmitting and receiving electromagnetic waves in a preset frequency range. The signal processing assembly is used for processing radar echo signals received by the radar, determining radar signal characteristics under the conditions of internal defects of different lining structures and tunnel hidden defects according to the radar echo signals, and constructing a hidden defect simulation detection database. According to the invention, tunnel simulation detection under different working conditions can be realized.

The invention discloses a distributed radar time-frequency synchronization and transmit-receive coherent method and system, and belongs to the technical field of radars. The method comprises the following steps: connecting each transmitting station and each receiving station of the distributed radar with a synchronization device, wherein the synchronization device comprises timing system equipment, an atomic clock and wireless communication equipment; the distributed radar receives working parameters issued by the wireless communication center controller to the distributed radar by using the wireless communication equipment; according to the working parameters, the transmitting station starts to transmit a radar signal after receiving a trigger synchronization signal of the timing system equipment, and the receiving station starts to collect an echo signal based on a local oscillator signal after receiving the trigger synchronization signal of the timing system equipment; the radar signal and the local oscillator signal are both generated based on the connected atomic clock as a reference clock. According to the invention, time-frequency synchronization and transmit-receive coherence of the distributed radar can be realized by using the synchronization device, limitation of space wired connection is avoided, and the construction cost of the distributed radar is reduced.

A method includes transmitting radar signals toward a target area via a phased array antenna of a synthetic aperture radar (SAR) antenna system, receiving reflected radar signals from the target area, performing, via a signal processor, one or more SAR algorithms to convert the reflected radar signals into one or more images, video, and / or measurements of the target area, receiving one or more of ground-based visual data, aerial data, and / or historical or architectural data, integrating the ground-based visual data, the aerial data, and / or the historical or architectural data with the one or more images, video, and / or measurements produced by the SAR antenna system to produce a cohesive visualization, and sending the visualization to a user device for display and interaction therewith.

The invention is suitable for the technical field of information processing, and provides an information fusion method, device and system, and the method comprises the steps: obtaining an original image and a radar signal, which are obtained through the collection of a vehicle or a road side unit; extracting a visual feature sequence of the original image, and extracting a motion and geometric feature sequence of the radar signal; generating multi-modal features based on the visual feature sequence and the motion and geometric feature sequence; obtaining a physical index of V2X wireless communication, and obtaining a channel prompt based on the physical index; performing channel modulation on the multi-modal features based on channel prompts to obtain channel sensing features; and obtaining a dense prediction map and coordinate information of the target object based on the channel prompt and the channel perception characteristics. According to the method, the multi-modal features including the radar information are modulated through the physical indexes, pollution of inaccurate external information to the fusion result is inhibited, robustness is improved, and the false detection rate is reduced.

The invention provides a forest biomass dynamic monitoring method, system and device based on satellite remote sensing and a storage medium. The method comprises the following steps: acquiring an optical image and a radar signal of a forest to be monitored through a satellite platform, acquiring a multi-source remote sensing data set, and preprocessing the multi-source remote sensing data set; based on the preprocessed multi-source remote sensing data set, using a convolutional neural network to extract vegetation texture and height features to obtain an initial biomass distribution map; on the basis of the optical image area cloud coverage rate in the initial biomass distribution diagram, supplementing missing parts through synthetic aperture radar data to obtain a complete biomass distribution diagram; acquiring spectral band information of the optical image and the radar data based on the complete biomass distribution diagram, and acquiring a fused biomass estimated value by combining a random forest algorithm; and based on the fused biomass estimated value, calculating a differential image in a continuous time sequence to obtain a biomass variation trend, and realizing dynamic monitoring of the biomass of the forest to be monitored. According to the invention, high-precision and real-time forest biomass dynamic analysis is realized.

In various examples, methods and systems are provided for sampling and transmitting the most useful information from a radar signal representing a scene while staying within the computational and storage confines of a standard automotive radar sensor and the bandwidth constraints of a standard communication link between a radar sensor and processing unit. Disclosed approaches may select a patch of frequency bins that correspond to radar signals based at least on proximities of the frequency bins to one or more frequency bins corresponding to at least one peak and / or detection point in the radar signals. Data representing samples corresponding to the patch of frequency bins may be transmitted to the processing unit and applied to one or more machine learning models in order to accurately classify, identify, and / or track objects.

The embodiment of the invention discloses an indoor equipment intelligent control method based on human body induction and human body induction equipment. A specific embodiment of the method comprises the following steps: performing gait feature recognition on a target vibration signal; starting a heat source sensing assembly to collect a heat source image sequence aiming at the target area, and performing heat source identification on the heat source image sequence; starting a heat source positioning assembly to collect a millimeter-wave radar signal for the target area, and performing radar signal feature extraction on the millimeter-wave radar signal; generating object description information according to the gait features, the heat source features and the radar signal features; and controlling the intelligent indoor equipment corresponding to the target linkage information in the target area through the wireless communication component. According to the embodiment of the invention, accurate personnel identification and intention identification are realized, and the effectiveness and timeliness of intelligent indoor equipment control are further ensured.

The invention discloses a radar signal classification method and system based on a quantum classical hybrid convolutional neural network, and belongs to the technical field of quantum calculation and signal processing. According to the scheme, the classic radar signals are sequentially subjected to preprocessing, quantum phase encoding, quantum convolution feature extraction, quantum measurement and classic convolution neural network classification, and high-precision classification of the radar signals is achieved. According to the method, the parallel advantage of quantum calculation on high-dimensional feature extraction and the robustness of a classic neural network on classification decision are fully utilized, the classification accuracy and the anti-noise capability are remarkably improved, and the method is suitable for radar signal processing tasks in a complex environment.

The invention discloses an intelligent control system for rearview mirror adjustment. The intelligent control system comprises a vehicle-road collaborative sensing data acquisition module, a multi-mode road condition feature fusion module, a space-time fusion segmentation processing module, a scene parameter analysis module, a reinforcement learning decision calculation module and a rearview mirror driving control module. The vehicle-road collaborative sensing data acquisition module captures roadside radar signals, camera images and vehicle-end bus data, the multi-mode road condition feature fusion module and the space-time fusion segmentation processing module cooperatively complete data space-time alignment and semantic segmentation, and the scene parameter analysis module extracts parameters such as obstacle distance and lane line curvature from the data. The reinforcement learning decision calculation module constructs a scene self-adaptive decision system based on parameters, and the rearview mirror driving control module receives an instruction to regulate and control the rotating speed and the steering angle of a motor. All the modules form a'sensing-fusion-decision-execution 'closed loop, accurate adaptation of the view of the rearview mirror under different road conditions is achieved, and driving safety and adjustment timeliness are improved.

The invention provides a monitoring method for a clearance value of a fan blade, a fan control method and a medium, and the monitoring method comprises the steps: obtaining an actual strain parameter, meteorological data and SCADA data when a radar signal fails; inputting the meteorological data and the SCADA data into a preset digital twin system to obtain a predicted strain parameter; when the difference value between the predicted strain parameter and the actual strain parameter is greater than an error threshold value, adjusting the system parameter of the digital twin system until the error value is less than or equal to the error threshold value; and obtaining the clearance value based on the adjusted digital twin system.

The invention relates to the technical field of echo signal analysis, and discloses a radar radiation source signal identification method based on multi-task learning under physical feature constraint, which comprises the following steps of: 1, establishing a data set by a two-dimensional tensor and a physical feature tag; 2, the identification model comprises a feature extraction network and a multi-task learning network, and the multi-task learning network comprises a frame prediction branch, a classification branch, a parameter regression branch and a frequency spectrum prediction branch; 3, training the recognition model to obtain a trained recognition model; and step 4, inputting a two-dimensional tensor to be recognized into the trained recognition model, and outputting predicted physical characteristics, probability distribution of signal categories, predicted values of parameters and a remodeled two-dimensional STFT spectrogram. Physical priori knowledge and multi-task cooperative constraint are introduced, the physical nature of radar signals can be accurately captured, the interpretability and anti-noise capability of the model are improved, the expression capability of the model for key features is enhanced, and the method is suitable for scenes with sparse feature distribution or weak target signals.

The invention discloses a shipborne unattended radar alignment calibration system, which relates to the technical field of shipborne alignment calibration and is characterized in that an alignment calibration task list containing conditions such as time / position / waveform parameters is configured and issued through an upper computer terminal, and a shipborne data recording and control unit is used as a center; based on navigation data and a unified time reference provided by a satellite navigation time service and positioning module, the three-dimensional compass outputs attitude data of a ship, task triggering conditions are automatically judged, antenna pointing is adjusted through attitude compensation calculation, or area scanning is executed, so that a task is completed. A radar signal source is controlled to generate a programmable radar analog signal in an active calibration mode and transmit the programmable radar analog signal through an antenna, a target true value track is generated in a passive calibration mode, and meanwhile full-process data with timestamps are recorded in a concentrated mode. According to the invention, the problems of manual dependence and inaccurate time-space control of traditional shipborne calibration in research and development of radar receivers and electronic investigation equipment are solved, and unattended operation and accurate shipborne calibration are truly realized.