31 results about "Marine radar" patented technology

The invention belongs to the technical field of target detection, and particularly relates to a marine radar target detection method, system and equipment based on space-frequency feature fusion, and the method comprises the steps: constructing a marine target detection model based on space-frequency feature fusion, and extracting the multi-scale spatial domain features of an input image through a spatial domain feature sensing module in the model; frequency domain feature extraction and enhancement are carried out on the spatial domain features through a frequency domain feature sensing module to obtain frequency domain enhanced features, and the spatial domain features and the frequency domain enhanced features are fused through a space-frequency feature fusion module to obtain multi-scale enhanced feature representation; and performing layer-by-layer feature fusion on the multi-scale enhanced feature representation through a feature fusion network to obtain a final feature map, performing target detection on the final feature map through a detection head, and realizing radar target detection by using a trained marine target detection model. According to the method, a space-frequency double-domain fusion framework is constructed, and the method has high accuracy for radar image crowded scenes, multi-scale scenes and small target detection tasks.

The application provides an ice area navigation dynamic sea ice risk early warning method and system, the method first acquires the navigation information of a target ship, the first navigation radar image information of a first time point and the second navigation radar image information of a second time point, and calculates the time interval of the two time points, then respectively extracts the interested region of the sea ice echo through a mask matrix, fills the pixels of the interested region of the sea ice echo and the pixels of other regions except the interested region of the sea ice echo with different colors to obtain the binary image of the sea ice echo of the two time points, then calculates the distance, azimuth angle of each piece of sea ice and the target ship, and the latitude and longitude coordinates of the sea ice centroid of the two time points by using a specific calculation method, calculates the sea ice collision probability and the sea ice collision consequence, further calculates the sea ice collision risk index, and performs corresponding different level sea ice risk early warning on the sea ice according to the comparison result of the sea ice collision risk index and the preset risk threshold of different levels.

This application provides a sea clutter suppression method based on complex numbers and adaptive dynamic weighting, relating to the field of marine radar signal processing technology. The method includes performing a short-time Fourier transform on the raw radar echo data to generate a spectrum; standardizing preprocessing to obtain preprocessed in-phase and quadrature component signals; performing frequency domain pre-separation to obtain low-frequency sea clutter component signals; performing phase compensation and outputting the IMF corresponding to the low-frequency sea clutter components through improved complex EMD decomposition; performing multi-criteria adaptive selection to obtain the filtered IMF; calculating the high-frequency residual of the original signal; and performing dynamic weighted reconstruction and energy calibration based on the high-frequency residual and the filtered IMF to output the target signal after clutter suppression. This application improves the method's ability to capture signal phase information and suppresses mode aliasing by extending the time-domain EMD to the complex domain; and introduces an adaptive dynamic weighting mechanism to adjust the weight allocation in real time during the decomposition process, thereby improving sea clutter suppression performance.

The invention aims to provide a target detection method and system for suppressing marine radar image sequence clutters based on time-space domain Hough transform, and the method comprises the steps: firstly removing sea clutter spectrum energy through employing a frequency domain and wave number domain combined filter based on a dispersion relation, and then intercepting a spectrum center region; the method comprises the following steps: firstly, performing clutter suppression on an image sequence, acquiring target frequency spectrum parameters by using Hough detection to establish a clutter frequency spectrum filter so as to realize further clutter filtering, finally performing generalized extreme value distribution modeling on the image sequence subjected to clutter suppression to obtain mu, sigma and xi values conforming to data background clutters, and performing target point detection by using a rejection inclusion detector. The algorithm is verified by using the real data of the X-band marine radar, and the method can effectively suppress the strong clutter signal, thereby enhancing the signal-to-noise ratio of the image, and further improving the target detection probability under the strong sea clutter condition.

The invention provides a multi-modal fusion-driven ship navigation target detection method and device, and the method comprises the steps: extracting the feature vectors of ship vision, marine radar, AIS and ship navigation data, taking the vector of the marine radar as a dominant vector, taking the other feature vectors as supplementary fusion, and achieving the modal complementation. And mapping the fusion feature vector to a bird's-eye view semantic space to obtain bird's-eye view features, decoding the bird's-eye view features, and inputting the decoded bird's-eye view features into a detection head for target detection to obtain a target detection result. In this way, through multi-modal fusion complementation and semantic space mapping, the problem that existing multi-modal data fusion only stays at a data layer for direct splicing or shallow feature level fusion can be avoided, and the space-time isomerism and semantic difference between multi-modal data are overcome. The consistency and robustness of target detection in the ship navigation process are improved under the complex sea condition, and the reliability and accuracy of the detection result are ensured.

This invention provides a method for identifying and suppressing false echoes from marine radar under calm sea conditions. The specific process is as follows: For a single-frame radar image, extended target information is obtained. The recorded extended target information is converted into point coordinate information, i.e., the wave crest position of the echo is retained, and the azimuth and range information of the wave crest position are extracted. Simultaneously, the echo intensity information of the target is added and stored as an initial target information matrix. Azimuth feature information is judged, and target information that conforms to the azimuth feature of the false echo is saved. Range feature information is judged, and target information that conforms to the range feature of the false echo is saved. The false echo occurrence scenario is judged to determine whether the conditions for false echo occurrence are met. Echo intensity feature information is judged, and target information that does not conform to the echo intensity feature of the false echo is deleted. Simultaneously, the initial target information matrix is ​​updated to a false echo target information matrix. Based on the information in the false echo target information matrix, a suppression threshold for the digital domain of the radar echo data is set to suppress false echoes in the single-frame target information.

The invention aims to provide a marine radar image target detection method and system based on airspace sea clutter suppression, and the method comprises the steps: firstly obtaining sea clutter frequency spectrum information in a three-dimensional frequency wave number domain in a three-dimensional space-time joint domain processing channel, and the sea clutter frequency spectrum information comprises a wave number, an angular frequency and a wave direction corresponding to each frequency spectrum point; the method comprises the following steps: firstly, acquiring information of a sea clutter spectrum, then converting the information into a two-dimensional spatial domain as priori knowledge to establish a filter model, filtering an image spectrum after two-dimensional Fourier transform to realize rapid suppression of sea clutter spectrum energy, and finally, carrying out Burr distribution modeling on an image sequence after clutter suppression to obtain c, k and alpha values conforming to data background clutters. And target point detection is carried out by using a WHOS detector. The algorithm is verified by using the real data of the X-band marine radar, and the method can effectively model a background clutter distribution model under the background of sea clutter suppression, thereby improving the target detection probability of the image after the sea clutter suppression of the marine radar.

The invention relates to the technical field of marine environment monitoring, and discloses a marine radar oil spill detection method and system based on a neural network and GMM clustering, and the method comprises the steps: obtaining a marine radar image, and dividing the marine radar image into a plurality of image blocks; extracting an eight-dimensional feature vector from each image block and performing dimension reduction processing to obtain a four-dimensional feature vector; clustering the four-dimensional feature vectors to generate a region of interest; performing feature extraction and anomaly detection on non-background pixels in the region of interest based on a neural network, and marking abnormal points; after abnormal points are removed, clustering is carried out on residual non-background pixels based on a Gaussian mixture model, and an oil film segmentation result is obtained; and fusing an oil film segmentation result with the preprocessed image, converting a fusion result from a Cartesian coordinate system into a polar coordinate system, and outputting a final detection result. According to the scheme, the detection precision and robustness can be remarkably improved, stable and reliable oil spill identification is realized under complex sea conditions, and support is provided for marine environment monitoring.

The invention provides a marine radar performance monitor system based on frequency sweep, which comprises a radar antenna, a magnetron pulse transmitting module, an echo receiving module, a display module, a PM antenna, a receiving and detecting module and a regular image transmitting module, and is characterized in that the output end of the magnetron pulse transmitting module is connected with the input end of the radar antenna; the output end of the radar antenna is connected with the input end of the echo receiving module, the output end of the echo receiving module is connected with the input end of the displayer, the output end of the PM antenna is connected with the output end of the receiving and detecting module, and the output end of the receiving and detecting module is connected with the input end of the regular image transmitting module. The output end of the regular image transmitting module is connected with the input end of the PM antenna, the regular calibration pulse width can be broadened and can be stably kept within the response range of the echo receiving module, and the problem that the transmitting width of a pulse radar of a traditional marine radar performance monitor system is narrow is solved.

1. The name of the design product: solid-state marine radar (X-band). 2. The use of the design product: the appearance of X-band solid-state marine radar. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The invention provides a marine radar oil spill detection method and system based on BiLSTM and SOM, and relates to the technical field of target detection. The method comprises the following steps: preprocessing original radar image data; extracting multi-modal texture features of the preprocessed image, and generating a pseudo tag of the image by using a K-means clustering algorithm; using a BiLSTM algorithm to extract time sequence features of the preprocessed image; inputting the time sequence features into a trained self-organizing mapping network, and finding out a winning neuron for each feature vector; allocating a pseudo label of a pixel corresponding to each feature vector to a corresponding winning neuron so as to obtain a classification result of each pixel in the preprocessed image; and on the basis of the categories of other pixels in the neighborhood window of each pixel in the classification result, carrying out iterative updating on the category of each pixel to determine a final classification result of each pixel, and then carrying out morphological processing to obtain an oil spill image. According to the invention, a new solution is provided for accurate detection of radar oil spill in a complex scene.

This invention belongs to the field of intelligent collision avoidance and early warning technology for ships, specifically relating to a method and system for collision avoidance and early warning in open sea areas. The method includes the following steps in sequence: multi-source navigation data acquisition and analysis, intelligent filtering of open water conditions, real-time calculation of collision risk parameters, collision risk early warning triggering, automatic recording of close-range navigation events, and ship-shore synchronization of early warning and recorded information. The main hardware used in the system includes an AIS signal controller, marine ARPA radar, GPS equipment, gyrocompass, serial port server, switch, collision avoidance tablet computer, captain's cabin computer, ship-side communication host, and shore-side platform. This invention aims to solve the problems of "alarm fatigue" and safety risks caused by numerous invalid alarms and poor scene adaptability in traditional collision avoidance early warning systems during navigation in open sea areas by integrating intelligent scene filtering, accurate risk calculation, and ship-shore collaborative management into a system and method, thereby achieving efficient and reliable automatic collision early warning.

This invention discloses a virtual simulation method and system for marine radar, comprising the following steps: constructing a virtual test environment consistent with the real marine environment, including an unmanned vessel, marine radar, and maritime targets; using a quaternion calculation method to transform measurements in the local coordinate system of the virtual radar to the world coordinate system; determining the radar field of view through horizontal and vertical scanning coverage; realizing simulated ray tracing and collision detection of the marine radar beam; calculating echo distance and angle, calculating echo intensity, and generating standardized radar echo data records; dynamically adjusting the echo intensity through adjustable gain control and range compensation mechanisms, and mapping the echo data to a color space; persistently storing and time-attenuating the echo data to simulate radar afterglow effects; and drawing range rings, azimuth markers, and dynamic scan lines on the radar display interface to construct a complete radar visualization system.

The invention aims to provide a radar image sea clutter suppression method and system based on a spatial domain filtering technology, and the method comprises the steps: firstly obtaining sea clutter frequency spectrum information in a three-dimensional frequency wave number domain in a three-dimensional space-time joint domain processing channel, and the sea clutter frequency spectrum information comprises a wave number, an angular frequency and a wave direction corresponding to each frequency spectrum point; then the information is converted into a two-dimensional spatial domain to serve as priori knowledge to establish a filter model, filtering is carried out on an image spectrum after two-dimensional Fourier transform, sea clutter spectrum energy is rapidly suppressed, and finally target point detection is carried out on the image after sea clutter suppression. The algorithm is verified by using the real data of the X-band marine radar, and the method can effectively suppress the strong sea clutter signal, thereby enhancing the signal-to-noise ratio of the image, and further improving the target detection probability under the strong sea clutter condition.

This embodiment discloses an AIS-assisted method for identifying sidelobe echoes in marine radar. It utilizes AIS data to establish a pre-selected area, removes the main lobe, determines the sidelobe, and then selectively zeros and restores the main lobe to achieve accurate identification and effective suppression of sidelobe echoes. This method effectively identifies and eliminates interfering sidelobe echoes, ensuring that the true echo (main lobe) of the target ship is not mistakenly detected, thus preserving the core information of radar detection. After suppressing the sidelobes, the radar range-azimuth image becomes clearer, reducing interference from false targets. This helps the navigator more accurately assess the surrounding navigation situation, reducing the risk of navigational decision errors and improving the readability and safety of radar images. Furthermore, the introduction of AIS reported target data as a high-precision reference makes the locking of the main lobe echo more stable, avoiding potential biases that might arise from relying solely on the radar image's own features for clustering, thus providing technical support for suppressing or eliminating sidelobe echoes.

This invention relates to a method for inverting marine radar echoes based on satellite data, belonging to the field of meteorological data processing technology. The invention acquires and processes multi-channel satellite observation data and combined radar reflectivity observation data within the same spatiotemporal range of a target area; then, it constructs a deep convolutional neural network model based on the U-Net architecture and trains the model using the preprocessed data; finally, it inputs multi-channel satellite observation data of the area to be inverted into the trained model, outputting the combined radar reflectivity inversion data for that area. This invention fills the gap in marine radar observation through artificial intelligence technology, achieving high inversion accuracy, strong model generalization ability, and significant operational application value.

The invention relates to a vessel collision monitoring system that automatically and continuously compares radar and AIS observations over time. The movement of all (or many) vessels (12, 14) in the area (5) around an offshore installation (1) can be monitored and this data can be recorded and used to produce trends and reports indicating where there is a discrepancy between AIS “targets” and radar observations. In this way, potential blind spots (10) in the radar system can be identified and appropriate action taken, e.g. maintenance of the radar system (2) or moving or adding radar transmitters or receivers to remove a blind spot.

This invention relates to a method for tracking maritime targets based on multiple maritime radars. The method includes the following steps: Step 1: Using a clustering algorithm to identify targets from point cloud data acquired by the radar, obtaining the position and size information of the identified observations; Step 2: Matching the current observation with existing historical tracking trajectories based on a joint probabilistic data interconnection method, obtaining the state information of the matched trajectories; Step 3: Estimating the state of the track being tracked based on the state information of the matched trajectories, obtaining the state estimation results, including the target's state information and the corresponding estimated variance value; Step 4: Correcting the parameters of the clustering algorithm based on the state estimation results; Step 5: Fusion of the tracking results from multiple radars, that is, fusing the state estimation results from each radar individually. Compared with existing technologies, this invention has advantages such as alleviating the difficulty of setting target identification algorithm parameters and increasing system robustness.

The application discloses a sea surface wind speed inversion method based on a marine radar image and belongs to the technical field of ocean remote sensing. The application solves the problem of low wind speed inversion precision of the existing method. The technical scheme of the application is as follows: step 1, original radar image sets are obtained by reading radar files offline, each original radar image is processed to obtain the full distance integral area of the counter-wind direction corresponding to each original radar image, and then a function relationship between the wind speed and the full distance integral area of the counter-wind direction is established according to the wind speed when each original radar image is collected; step 2, the full distance integral area of the counter-wind direction of the radar image to be measured is calculated; and step 3, the full distance integral area of the counter-wind direction calculated in step 2 is substituted into the function of the wind speed and the full distance integral area of the counter-wind direction established in step 1 to obtain the sea surface wind speed when the radar image read in step 2 is collected. The method can be applied to sea surface wind speed inversion.

The application belongs to the technical field of marine navigation radar, and specifically discloses a marine radar navigation blind-filling method based on maximum value fusion and clutter suppression rules. According to the application, pixel-level registration is performed on the current radar image, and the initial fusion value of each pixel point is determined according to the gray value of each marine radar at each pixel point position; each pixel point is fused according to the initial fusion value; the true target and the clutter are distinguished and the clutter is suppressed by applying a multi-level clutter suppression rule to the initial fusion image, and the navigation of the marine radar is filled blind according to the true target. In the above manner, the maximum gray value is selected as the initial fusion value of the pixel point for fusion, ensuring that as long as the target is detected by the marine radar, the target can be displayed in the initial fusion image. The multi-level clutter suppression rule is used to distinguish the true target and the clutter, thereby effectively improving the accuracy of the blind-filling marine radar navigation and further improving the safety of ship navigation.

The invention relates to a vessel collision monitoring system that automatically and continuously compares radar and AIS observations over time. The movement of all (or many) vessels (12, 14) in the area (5) around an offshore installation (1) can be monitored and this data can be recorded and used to produce trends and reports indicating where there is a discrepancy between AIS "targets" and radar observations. In this way, potential blind spots (10) in the radar system can be identified and appropriate action taken, e.g. maintenance of the radar system (2) or moving or adding radar transmitters or receivers to remove a blind spot.

This invention discloses a small target detection method based on a two-parameter KNN in a marine environment. Addressing the issue of the traditional KNN algorithm's inability to control the false alarm rate, this invention achieves this goal by adjusting the k value and the scaling parameter η. It represents an extension of pattern recognition technology or theory into the field of slow-moving small target detection in marine radar. It can delve deeper into the small target detection problem, exhibits excellent generalization capabilities, and is a novel and promising method for small target detection.

The application relates to the technical field of marine environment monitoring, and discloses a marine radar oil spill detection method and system based on a neural network and GMM clustering. The method comprises the following steps: acquiring a marine radar image and dividing the image into multiple image blocks; extracting an eight-dimensional feature vector from each image block and performing dimension reduction processing to obtain a four-dimensional feature vector; clustering the four-dimensional feature vectors to generate a region of interest; performing feature extraction and anomaly detection on non-background pixels in the region of interest based on a neural network, and marking abnormal points; after removing the abnormal points, clustering the remaining non-background pixels based on a Gaussian mixture model to obtain an oil film segmentation result; fusing the oil film segmentation result and the preprocessed image, converting the fusion result from a Cartesian coordinate system into a polar coordinate system, and outputting a final detection result. The scheme can significantly improve the detection accuracy and robustness, realizes stable and reliable oil spill identification under complex sea conditions, and provides support for marine environment monitoring.

The application provides a maritime radar oil film segmentation method and system based on improved saliency features and SOM, and relates to the technical field of oil film image segmentation.The technical points of the application include: preprocessing the obtained original maritime radar image;extracting the saliency features of the preprocessed image to obtain an image of an area of interest;using an improved self-organizing mapping network to segment the oil film in the image of the area of interest to obtain a segmented image.By introducing the saliency features, the texture roughness and boundary irregularity of the oil film under different scales are effectively described, and the ability to distinguish the oil film from the seawater background under complex sea conditions is improved; meanwhile, in combination with the improved self-organizing mapping network, the problems that the traditional clustering method is prone to falling into local optimization and the classification boundary is fuzzy when processing high-dimensional and nonlinear oil film features are effectively solved, and the precision and stability of the oil film region segmentation are further improved.The application improves the intelligent level of oil film monitoring under complex environments.

The invention provides a marine radar oil film segmentation method and system based on improved saliency features and SOM, and relates to the technical field of oil film image segmentation. The method is characterized by comprising the following steps: preprocessing an obtained original marine radar image; extracting saliency features of the preprocessed image, and obtaining an interest region image; and performing oil film segmentation on the image of the region of interest by adopting an improved self-organizing mapping network to obtain a segmented image. Wherein the texture roughness and the boundary irregularity of the oil film under different scales are effectively depicted by introducing significant features, and the capability of distinguishing the oil film from the seawater background under the complex sea condition is improved; meanwhile, the improved self-organizing mapping network is combined, the problems that a traditional clustering method is prone to falling into local optimum and fuzzy classification boundaries when high-dimensional and nonlinear oil film features are processed are effectively solved, and the precision and stability of oil film region segmentation are further improved. The intelligent level of oil film monitoring in a complex environment is improved.

The present application relates to the technical field of visual detection and method, and particularly relates to a marine radar image target tracking method based on deep learning, comprising the following steps: collecting and analyzing echo data, rendering echo information into marine radar images and manually labeling to build a labeled training dataset; optimizing a basic detection model and establishing a target detection model; performing data enhancement processing on the marine radar images, training the target detection model and outputting a weight file; outputting a multi-scale feature map, obtaining a target detection frame based on threshold filtering and non-maximum suppression; inputting the target detection frame into a DeepSORT tracker, extracting appearance features and motion information of the target, and continuously tracking and maintaining identity consistency of multiple targets in the image sequence. The present application realizes accurate, stable and real-time detection and tracking of ship targets in marine radar images by optimizing the structure of the detection model and the tracking algorithm, and the model is lightweight and convenient for actual deployment.

This invention proposes a multi-target tracking method for marine radar based on an evaluation function. The method includes: after mapping radar echo data to obtain radar images, using a density clustering algorithm for target identification, dividing the target and background on the point cloud image; for the identified targets, proposing an evaluation function for target morphological feature information, further classifying and filtering the targets; further using a Kalman filter algorithm for target tracking after classification, and using the JPDA algorithm for data association, outputting the multi-target tracking results. This invention addresses the problems of numerous noisy targets in marine radar image target identification results and the high computational complexity and low efficiency of the JPDA algorithm in scenarios with multiple targets. By filtering out noisy targets, the number of feasible events input to the JPDA algorithm is reduced, improving the computational efficiency of the JPDA algorithm in multi-target tracking scenarios and enhancing the algorithm's applicability.

The invention discloses a non-uniform evaporation waveguide height inversion method based on bidirectional X-waveband signals, which comprises the following steps of: synchronously acquiring and recording channel level data from opposite-end signals by relying on channel monitoring equipment deployed by two communication parties, calculating bidirectional actually measured path loss by combining system parameters, and calculating a non-uniform evaporation waveguide height. And calculating bidirectional simulation path loss by using an evaporation waveguide section model and a radio wave propagation model. Then, an objective function is constructed based on the bidirectional actual measurement path loss and the simulation path loss, and an optimization algorithm is used for searching and solving a group of optimal non-uniform evaporation waveguide heights; according to the method, the inversion precision and reliability of the height of the non-uniform evaporation waveguide are effectively improved, and key technical support is provided for design and application of electromagnetic systems such as marine radar and communication.

The present invention provides a digital racon, and method and system for display on a marine radar screen. Upon receiving a main lobe trigger signal from a radar, by means of wireless communication, a racon replies with a digital signal identification code containing the identity of the racon; and upon receiving the digital signal identification code from the racon, the radar decodes the digital signal identification code, and, on the basis of the distance and orientation of the racon acquired by the radar, generates and displays a corresponding Morse code pattern in a radially outward direction at the screen position where the racon is located.

The invention discloses a marine radar target information acquisition method based on geometric correction and an improved YOLOv11 model, systematic innovation is carried out in the aspects of preprocessing and geometric correction, a model structure and a loss function, data enhancement, coordinate conversion and the like, and particularly, linear scale conversion and polar coordinate conversion parameters are pre-established in the preprocessing stage; geometric correction is advanced before target detection, so that the model training efficiency can be improved, and the coordinate conversion operation in the reasoning stage can be simplified; and meanwhile, a coordinate attention mechanism and a decoupling detection head are introduced, so that the perception capability of the model on long-strip-shaped and small targets is remarkably improved. Comprehensive test results show that the method of the invention significantly reduces the missed alarm rate and improves the detection precision, is suitable for being deployed in a marine radar system with limited resources, and is used for real-time target identification and navigation assistance.