Multi-mode fusion sea surface target identification system for high-dynamic unmanned platform

Abstract

The invention relates to the technical field of sea surface target multi-mode fusion recognition system design, in particular to a multi-mode fusion sea surface target recognition system for a high-dynamic unmanned platform. The system is characterized by comprising a platform sensor subsystem, a multi-mode characteristic processing and identification subsystem, an information transmission control subsystem and a far-end display control subsystem, the system is combined with a corresponding sea surface target image feature extraction technology, an active and passive radar signal feature extraction technology and a sea surface target identification technology of a multimode heterogeneous feature fusion multi-core classifier, so that an efficient fusion identification system of four modal data of an optical image, an SAR image, an active radar signal and a passive radar signal is realized; according to the method, the high-dynamic unmanned system can autonomously judge the type of the sea surface slow-moving target, the multi-mode feature fusion utilization efficiency is enhanced, the target recognition precision and robustness are improved, and the autonomy and intelligence level of the high-dynamic unmanned system is improved.

Description

Technical Field

[0001] The invention relates to the technical field of design of a multi-mode fusion recognition system for sea surface targets, and in particular to a multi-mode fusion sea surface target recognition system for a high-dynamic unmanned platform. Background Art

[0002] The sea surface targets that the present invention is concerned with are slow-moving targets on the sea surface, such as ships, boats, etc., which have a certain radar reflection cross-sectional area or are equipped with radar equipment and radiate electromagnetic signals outward, but other moving targets, such as cruise missiles flying over the sea, are not included in this list. Slow-moving targets on the sea surface are the focus of sea observation by various sensors. Compared with stationary targets such as sea surface buildings and islands, their echo signals in typical radar systems, such as airborne active radar systems, will fall into the main lobe clutter area. The targets are mixed with the main clutter, resulting in defocus or displacement in the radar image, thereby affecting the identification of the targets, making it difficult to detect and locate the targets. These targets are often targets of military interest, such as sea surface ships and boats.

[0003] The high-dynamic unmanned platform described in the present invention refers to an aircraft platform that can achieve rapid acceleration, deceleration or change direction in a short period of time, including missile-borne and airborne platforms such as missiles and high-speed drones with high-speed movement capabilities. Such platforms often have high mobility and flexibility, and are suitable for occasions that require rapid intelligent perception, rapid decision-making response, high mobility and real-time performance.

[0004] There are many ways to obtain information about sea surface targets, including: UAV-borne, missile-borne visible light, infrared, synthetic aperture radar (SAR), active radar, passive radar, etc.

[0005] Based on this, in the prior art, there are several methods for detecting and identifying sea surface targets:

[0006] 1. Sea surface target detection and recognition based on visible light images

[0007] Sea surface target detection based on satellite-borne and airborne visible light remote sensing images has always attracted much attention. It mainly includes three technical systems: target detection based on traditional methods, target detection based on visual saliency, and target detection and recognition based on deep learning.

[0008] Object detection and recognition based on traditional methods can usually be divided into four steps: ① Select candidate target regions from the input image; ② Extract features from the candidate regions; ③ Use a classifier for classification; ④ Use non-maximum suppression to delete intersection regions with low scores.

[0009] Target detection and recognition based on visual saliency mainly focuses on visual attention mechanism and digital image processing technology. Since there is a large texture difference between ships and the sea surface, the texture information of the image will be an important feature for detecting ships on the sea surface, while the classic Itti model only extracts the brightness, color and direction features of the image. Therefore, in order to further improve the target detection effect, based on the traditional Itti model, the color, direction and brightness features of the remote sensing image can be extracted, and the texture features of the image can be obtained by using the gray level co-occurrence matrix. The distribution range of the ship target is estimated according to the resolution of the remote sensing image and the shape features of the ship-like target, and finally the regional filtering is performed through threshold segmentation to obtain the ship target. This method has a high recognition accuracy for a single ship target, but the accurate segmentation effect for multiple ships at a close distance is poor.

[0010] In recent years, with the rapid development of deep learning algorithms, object detection and recognition based on deep learning have been rapidly promoted and applied. For image recognition problems, feature extraction is an important part of the detection process, and the quality of features will directly affect the accuracy of detection. Traditional machine learning algorithms require manual design of feature sets for complex scenes, which is time-consuming and requires a high level of technical skills from researchers. The emergence of deep learning algorithms effectively solves the shortcomings of manually designed features, realizes automatic feature extraction, and can automatically combine simple features to form more complex features.

[0011] 2. Sea surface target detection and recognition based on infrared images

[0012] Sea surface target detection and recognition based on infrared images generally include three categories. The first is the infrared small target detection algorithm based on traditional methods, such as mathematical morphology methods, which can effectively eliminate the interference of high-frequency noise and high-frequency signals of background edges, and mainly detect targets of corresponding shapes in images through structural elements with certain forms and basic operations; the second is the method based on the human visual system. This type of algorithm uses the significant changes in local texture caused by the human eye to sea surface targets based on the perception characteristics of the human eye, such as using the brightness feature map, color feature map and direction feature map of the image, combining feature maps of different scales to obtain brightness, color and direction saliency maps, and fusion to obtain the final visual saliency map. The third is the infrared sea surface target detection algorithm based on neural networks. Deep learning technology provides a new perspective for sea surface target detection. Compared with traditional methods, neural networks have the advantages of fitting any complex function, strong fitting ability, and can complete more complex nonlinear mappings. In the case of large samples, better target detection and recognition results can be obtained.

[0013] 3. Sea surface target detection and recognition based on SAR images

[0014] The constant false alarm rate (CFAR) target detection algorithm is one of the most commonly used methods in radar image detection. It mainly detects targets in images through a predetermined background clutter distribution model and false alarm rate. The algorithm has the characteristics of low computational complexity and adaptive threshold to maintain a constant false alarm rate. However, complex sea conditions can lead to high non-uniformity of sea clutter in SAR images, thus affecting the effect of constant false alarm detection. Therefore, many scholars have improved and developed the CFAR detection algorithm.

[0015] In addition to the CFAR detection algorithm, there are also template-based detection algorithms. This type of algorithm not only considers the characteristics of the target, but also the characteristics of background clutter. Among them, the most famous is the SUMO detector; detection algorithms based on SAR image target ship features, such as the shape, texture and other features of the ship. A typical example is the high-resolution SAR image target ship detection algorithm based on feature analysis, which mainly uses the target ship's kernel density estimation, aspect ratio and other features to identify the ship. In addition, there is a method of detecting ships using target clustering. This method is based on a high-pass filter in nonlinear space. First, the filter is roughly adjusted to produce the maximum response to structures whose size is less than or equal to the expected object. After filtering, histogram and connected area processing are performed, and then the relevant theoretical knowledge of shape and cluster deployment mode is used to eliminate false alarms.

[0016] 4. Sea surface target detection and recognition based on active radar

[0017] Sea surface target detection based on active radar mainly utilizes the one-dimensional range image data formed by radar echo and uses target detection algorithm to detect targets. It is widely used on airborne and missile-borne platforms.

[0018] 5. Sea surface target detection and recognition based on passive radar

[0019] Passive radar detection is also called passive detection. It is generally used on ground fixed platforms to detect and track non-cooperative "low, slow and small" aerial targets. It can also be used on larger electronic reconnaissance aircraft, electronic reconnaissance ships or some high-dynamic platforms (such as missile-borne platforms) to perceive electromagnetic radiation signals of specific categories of targets. Since it is difficult to obtain passive radar signals of non-cooperative targets, there are few literatures on the processing of passive radar signals, and most of them use simulation to obtain signal samples.

[0020] 6. Sea surface target detection and recognition based on multimodal fusion

[0021] Due to the limitations of platform payload and computing device performance, in the application of multi-modal information fusion, multi-modal target detection and recognition are mostly used in large manned electronic reconnaissance aircraft, integrated reconnaissance and strike drones, reconnaissance ships, reconnaissance vehicles, high-orbit satellites and other systems. With the gradual reduction in the size and power consumption of electronic devices and the rapid improvement of AI computing performance, multi-modal fusion recognition has also begun to be promoted and applied in some smaller drones and missile-borne platforms. At present, in the field of sea surface target detection and recognition, various modal applications based on optical imaging such as visible light and infrared, high-resolution two-dimensional imaging based on SAR, and one-dimensional range images based on active and passive radar signals have been widely used, but in the multi-modal fusion of optical images, radar images and radar signals, especially in some high-dynamic small unmanned platform applications, there is no relatively complete practical framework. In addition, since it is difficult to obtain samples of typical sea surface target detection by missile-borne or small unmanned aerial vehicle platforms, how to apply the current popular data-driven target detection and recognition algorithm in the case of small samples remains to be explored in depth. Summary of the invention

[0022] The purpose of the present invention is to avoid the shortcomings of the prior art and provide a multi-mode fusion sea surface target recognition system for a high-dynamic unmanned platform. It designs a sea surface target recognition system based on a multi-mode heterogeneous feature fusion multi-core classifier to solve the problems raised in the above background technology.

[0023] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows: the multi-mode fusion sea surface target recognition system for a high-dynamic unmanned platform is characterized by comprising a platform sensor subsystem, a multi-mode feature processing and recognition subsystem, an information transmission control subsystem and a remote display and control subsystem;

[0024] The platform sensor subsystem includes an optical imaging sensor module, a SAR sensor module, an active radar sensor module and a passive radar sensor module. The system is a front-end receiving device for obtaining optical images, SAR images, active radar signals and passive radar signals of sea surface targets. The signals obtained by the optical imaging sensor module, the SAR sensor module, the active radar sensor module and the passive radar sensor module provide test samples for the target recognition system; at the same time, the azimuth information of the target relative to itself is obtained, and after coordinate transformation, the absolute position information of the target is obtained, including the latitude and longitude coordinates and distance information of the target.

[0025] The multi-mode feature processing and identification subsystem includes a pre-processing module, a feature extraction module and a sea surface target feature fusion classification module;

[0026] The preprocessing module includes an image filtering processing module corresponding to the optical imaging sensor module, an image filtering processing module corresponding to the SAR sensor module, a signal sampling processing module corresponding to the active radar sensor module, and a signal sampling processing module corresponding to the passive radar sensor module; the image and signal data receiving and preprocessing operation functions of the preprocessing module are realized by field programmable gate array hardware, and the processing results are stored in the onboard memory.

[0027] The image filtering processing module corresponding to the optical imaging sensor module realizes the noise filtering, distortion correction and target segmentation preprocessing functions of the optical image; the image filtering processing module corresponding to the SAR sensor module realizes the image coherent speckle noise suppression and CFAR target detection functions; the signal sampling processing module corresponding to the active radar sensor module completes the discrete sampling of the one-dimensional range image signal of the active radar, collects the one-dimensional discrete signal of the same length according to the input requirement of the classifier model, and is convenient for feature extraction operation processing; the signal sampling processing module corresponding to the passive radar sensor module completes the discrete sampling of the one-dimensional range image signal of the passive radar, collects the one-dimensional discrete signal of the same length according to the input requirement of the classifier model, and is convenient for feature extraction operation processing.

[0028] The feature extraction module includes an image feature extraction module corresponding to the optical imaging sensor module and the SAR sensor module, which mainly completes the multi-resolution robust feature extraction and selection functions of the image target. The feature extraction is implemented by a fast sampling filtering method, and the feature selection adopts a PCA algorithm, and finally the image sample is converted into a feature vector; the feature extraction module also includes a signal feature extraction module corresponding to the active radar sensor module and the passive radar sensor module, which mainly completes the calculation of 10 feature quantities including enhanced average absolute value, average amplitude change, wavelength, maximum fractal length, root mean square, zero crossing, slope sign change, time moment, Teager-Kaiser energy operator, and skewness, and then combines them into a 10-dimensional feature vector; the feature extraction module is implemented by field programmable gate array hardware, and the feature vector is stored in an onboard flash memory.

[0029] The sea surface target feature fusion classification module mainly completes model storage, multi-feature kernel matrix operation, fusion of multi-mode heterogeneous features, recognition test, and recognition result storage functions. The sea surface target feature fusion classification module is composed of Huawei Ascend high-performance AI embedded acceleration computing unit, embedded multimedia storage card, high-speed interface device and target recognition algorithm, including the storage of trained sea surface target recognition algorithm model, feature call reading of input samples, calculation fusion of multi-mode feature kernel matrix, test classification of input target samples, and finally completes the recognition of sea surface targets, that is, the determination of target categories.

[0030] The information transmission control subsystem includes an intelligent control module and an information transmission module connected to the sea surface target feature fusion classification module of the multi-mode feature processing and identification subsystem. The information transmission control subsystem mainly completes the system internal and external transmission of target identification results and the coordinated control function; the information transmission module mainly completes the wired and wireless transmission functions of target identification data, target coordinate data and other information; wired transmission is mainly responsible for data transmission within the system; wireless transmission is mainly responsible for data transmission between the system and the ground control station; according to the information transmission distance, wireless digital data link sending and receiving equipment is used, or data link and communication relay equipment are used to achieve the corresponding functions; the intelligent control module mainly controls the normal and orderly operation of each sensor module, sea surface target feature fusion classification module, information transmission module and other modules through the transmission of control signals in the entire sea surface target identification system. The function of this module is directly completed by the onboard control device of the Ascend AI accelerator.

[0031] The remote display and control subsystem is a ground display control module. The remote display and control subsystem is set on the ground control station host, which is convenient for the training, integration and debugging of the target recognition algorithm, and also convenient for the visualization of the target recognition function; the remote display and control subsystem realizes the visualization output and positioning identification of the target in the digital map of the remote ground station through the target coordinates and target recognition information obtained wirelessly, and completes the real-time tracking and character overlay functions of the target; the subsystem is mainly composed of a general-purpose computer host, a display device, and a geographic information system hardware and software device.

[0032] The beneficial effect of the present invention is as follows: the present invention provides a multi-mode fusion sea surface target recognition system for a high-dynamic unmanned platform, which combines corresponding sea surface target image feature extraction technology, active and passive radar signal feature extraction technology, and multi-mode heterogeneous feature fusion multi-core classifier sea surface target recognition technology to realize an efficient fusion recognition system of four modal data: optical image, SAR image, active radar signal, and passive radar signal, realizes the autonomous judgment of the type of slow-moving sea surface targets by the high-dynamic unmanned system, enhances the utilization efficiency of multi-mode feature fusion, improves the target recognition accuracy and robustness, and enhances the autonomy and intelligence level of the high-dynamic unmanned system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the principle of the present invention. DETAILED DESCRIPTION

[0034] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0035] In order to achieve the above object, the present invention provides the following specific implementation methods: Figure 1As shown, the multi-mode fusion sea surface target recognition system for a high-dynamic unmanned platform is characterized by including a platform sensor subsystem, a multi-mode feature processing and recognition subsystem, an information transmission control subsystem and a remote display and control subsystem;

[0036] The platform sensor subsystem includes an optical imaging sensor module, a SAR sensor module, an active radar sensor module and a passive radar sensor module. The system is a front-end receiving device for obtaining optical images, SAR images, active radar signals and passive radar signals of sea surface targets. The signals obtained by the optical imaging sensor module, the SAR sensor module, the active radar sensor module and the passive radar sensor module provide test samples for the target recognition system; at the same time, the azimuth information of the target relative to itself is obtained, and after coordinate transformation, the absolute position information of the target is obtained, including the latitude and longitude coordinates and distance information of the target.

[0037] The multi-mode feature processing and identification subsystem includes a pre-processing module, a feature extraction module and a sea surface target feature fusion classification module;

[0038] The preprocessing module includes an image filtering processing module corresponding to the optical imaging sensor module, an image filtering processing module corresponding to the SAR sensor module, a signal sampling processing module corresponding to the active radar sensor module, and a signal sampling processing module corresponding to the passive radar sensor module; the image and signal data receiving and preprocessing operation functions of the preprocessing module are realized by field programmable gate array hardware, and the processing results are stored in the onboard memory.

[0039] The image filtering processing module corresponding to the optical imaging sensor module realizes the noise filtering, distortion correction and target segmentation preprocessing functions of the optical image; the image filtering processing module corresponding to the SAR sensor module realizes the image coherent speckle noise suppression and CFAR target detection functions; the signal sampling processing module corresponding to the active radar sensor module completes the discrete sampling of the one-dimensional range image signal of the active radar, and collects the one-dimensional discrete signal of the same length according to the input requirements of the classifier model, which can be 1000 sampling points, which is convenient for feature extraction operation processing; the signal sampling processing module corresponding to the passive radar sensor module completes the discrete sampling of the one-dimensional range image signal of the passive radar, and collects the one-dimensional discrete signal of the same length according to the input requirements of the classifier model, such as 1000 sampling points, which is convenient for feature extraction operation processing.

[0040] The feature extraction module includes an image feature extraction module corresponding to the optical imaging sensor module and the SAR sensor module, which mainly completes the multi-resolution robust feature extraction and selection functions of the image target. The feature extraction is implemented by a fast sampling filtering method, and the feature selection adopts a principal component analysis (PCA) algorithm, and finally the image sample is converted into a feature vector; the feature extraction module also includes a signal feature extraction module corresponding to the active radar sensor module and the passive radar sensor module, which mainly completes the calculation of 10 feature quantities of enhanced average absolute value, average amplitude change, wavelength, maximum fractal length, root mean square, zero crossing, slope sign change, time moment, Teager-Kaiser energy operator, and skewness, and then combines them into a 10-dimensional feature vector; the feature extraction module is implemented by field programmable gate array hardware, and the feature vector is stored in an onboard flash memory.

[0041] The sea surface target feature fusion classification module mainly completes model storage, multi-feature kernel matrix operation, fusion of multi-mode heterogeneous features, recognition test, and recognition result storage functions. The sea surface target feature fusion classification module is composed of Huawei Ascend high-performance AI embedded acceleration computing unit, embedded multimedia storage card, high-speed interface device and target recognition algorithm, including the storage of trained sea surface target recognition algorithm model, feature call reading of input samples, calculation fusion of multi-mode feature kernel matrix, test classification of input target samples, and finally completes the recognition of sea surface targets, that is, the determination of target categories.

[0042] The information transmission control subsystem includes an intelligent control module and an information transmission module connected to the sea surface target feature fusion classification module of the multi-mode feature processing and identification subsystem. The information transmission control subsystem mainly completes the system internal and external transmission of target identification results and the coordinated control function; the information transmission module mainly completes the wired and wireless transmission functions of target identification data, target coordinate data and other information; wired transmission is mainly responsible for data transmission within the system; wireless transmission is mainly responsible for data transmission between the system and the ground control station; according to the information transmission distance, wireless digital data link sending and receiving equipment is used, or data link and communication relay equipment are used to achieve the corresponding functions; the intelligent control module mainly controls the normal and orderly operation of each sensor module, sea surface target feature fusion classification module, information transmission module and other modules through the transmission of control signals in the entire sea surface target identification system. The function of this module is directly completed by the onboard control device of the Ascend AI accelerator.

[0043] The remote display and control subsystem is a ground display control module. The remote display and control subsystem is set on the ground control station host, which is convenient for the training, integration and debugging of the target recognition algorithm, and also convenient for the visualization of the target recognition function; the remote display and control subsystem realizes the visualization output and positioning identification of the target in the digital map of the remote ground station through the target coordinates and target recognition information obtained wirelessly, and completes the real-time tracking and character overlay functions of the target; the subsystem is mainly composed of a general-purpose computer host, a display device, and a Geographic Information System (GIS) hardware and software device.

[0044] The multi-mode fusion sea surface target recognition system for a high-dynamic unmanned platform, when implemented, the system first receives the image data (respectively from the optical imaging sensor module and the SAR sensor module) and the active and passive radar signal data (respectively from the active radar sensor module and the passive radar sensor module) acquired by the high-dynamic platform sensor subsystem. After the data is sent to the multi-mode feature processing and recognition subsystem, image filtering processing and signal sampling processing are respectively performed, wherein the image filtering processing module for optical images mainly realizes noise filtering, distortion correction and target segmentation preprocessing; the image filtering processing module for SAR images mainly realizes image coherent speckle noise suppression and CFAR target detection; the signal sampling processing module for active radar signals mainly realizes discrete sampling of active radar one-dimensional range image signals to obtain equal-length one-dimensional discrete signals; the signal sampling processing module for passive radar signals mainly realizes discrete sampling of passive radar one-dimensional range image signals to obtain equal-length one-dimensional discrete signals.

[0045] The preprocessed multi-mode data are subjected to feature extraction respectively, where the feature extraction of image data is realized by the image feature extraction modules corresponding to the optical imaging sensor module and the SAR sensor module respectively, mainly using the fast sampling filtering method, and the PCA algorithm is used for feature selection, and finally the image samples are converted into feature vectors; the feature extraction of active and passive radar signals is respectively carried out by the signal feature extraction modules corresponding to the active radar sensor module and the passive radar sensor module, mainly completing the calculation of 10 feature quantities including enhanced mean absolute value, mean amplitude change, wavelength, maximum fractal length, root mean square, zero crossing, slope sign change, time moment, Teager–Kaiser energy operator, and skewness, and then combining them into a 10-dimensional feature vector.

[0046] After acquiring the four modal features of the sea surface target, namely, optical image, SAR image, active radar signal and passive radar signal, the sea surface target feature fusion and classification module realizes target recognition, that is, the determination of the target category, and mainly completes the functions of model storage, multi-feature kernel matrix operation, fusion of multi-mode heterogeneous features, recognition test and recognition result storage.

[0047] The target recognition result is transmitted to the remote display and control subsystem through the information transmission module of the information transmission control subsystem under the control and coordination of the intelligent control module, such as the target recognition data and the target coordinate data. The core of the remote display and control subsystem is the ground display control module, which mainly realizes the visual output and positioning identification of the target in the digital map of the remote ground station, and completes the real-time tracking and character overlay of the target.

[0048] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-mode fusion target recognition system for a highly dynamic unmanned platform, characterized in that: It includes platform sensor subsystem, multi-mode feature processing and recognition subsystem, information transmission control subsystem and remote display and control subsystem; The platform sensor subsystem includes an optical imaging sensor module, a SAR sensor module, an active radar sensor module and a passive radar sensor module; The multi-mode feature processing and identification subsystem includes a pre-processing module, a feature extraction module and a sea surface target feature fusion classification module; The information transmission control subsystem comprises an intelligent control module and an information transmission module which are connected to the sea surface target feature fusion classification module of the multi-mode feature processing and recognition subsystem.

2. A multi-mode fusion target recognition system for a highly dynamic unmanned platform as claimed in claim 1, characterized in that: The platform sensor subsystem is a front-end receiving device for acquiring optical images, SAR images, active radar signals and passive radar signals of sea surface targets. The signals acquired by the optical imaging sensor module, SAR sensor module, active radar sensor module and passive radar sensor module provide test samples for the target recognition system; at the same time, the orientation information of the target relative to itself is acquired, and after coordinate transformation, the absolute position information of the target is acquired, including the latitude and longitude coordinates and distance information of the target.

3. A multi-mode fusion target recognition system for a highly dynamic unmanned platform as claimed in claim 1, characterized in that: The preprocessing module of the multi-mode feature processing and recognition subsystem includes an image filtering processing module corresponding to the optical imaging sensor module, an image filtering processing module corresponding to the SAR sensor module, a signal sampling processing module corresponding to the active radar sensor module, and a signal sampling processing module corresponding to the passive radar sensor module; The image and signal data receiving and preprocessing operation functions of the preprocessing module are implemented by field programmable gate array hardware, and the processing results are stored in the onboard memory.

4. A multi-mode fusion target recognition system for a highly dynamic unmanned platform as claimed in claim 3, characterized in that: The image filtering processing module corresponding to the optical imaging sensor module realizes the noise filtering, distortion correction and target segmentation preprocessing functions of the optical image; the image filtering processing module corresponding to the SAR sensor module realizes the image coherent speckle noise suppression and CFAR target detection functions; the signal sampling processing module corresponding to the active radar sensor module completes the discrete sampling of the one-dimensional range image signal of the active radar, collects the one-dimensional discrete signal of the same length according to the input requirement of the classifier model, and is convenient for feature extraction operation processing; the signal sampling processing module corresponding to the passive radar sensor module completes the discrete sampling of the one-dimensional range image signal of the passive radar, collects the one-dimensional discrete signal of the same length according to the input requirement of the classifier model, and is convenient for feature extraction operation processing.

5. The multi-mode fusion target recognition system for a highly dynamic unmanned platform as claimed in claim 1, characterized in that: The feature extraction module of the multi-mode feature processing and recognition subsystem includes an image feature extraction module corresponding to the optical imaging sensor module and the SAR sensor module, which mainly completes the multi-resolution robust feature extraction and selection functions of the image target. The feature extraction is implemented by a fast sampling filtering method, and the feature selection adopts a PCA algorithm, and finally the image sample is converted into a feature vector; the feature extraction module also includes a signal feature extraction module corresponding to the active radar sensor module and the passive radar sensor module, which mainly completes the calculation of 10 feature quantities including enhanced average absolute value, average amplitude change, wavelength, maximum fractal length, root mean square, zero crossing, slope sign change, time moment, Teager-Kaiser energy operator, and skewness, and then combines them into a 10-dimensional feature vector; the feature extraction module is implemented by field programmable gate array hardware, and the feature vector is stored in an onboard flash memory.

6. The multi-mode fusion target recognition system for a highly dynamic unmanned platform as claimed in claim 1, characterized in that: The sea surface target feature fusion classification module of the multi-mode feature processing and recognition subsystem mainly completes the functions of model storage, multi-feature kernel matrix operation, fusion of multi-mode heterogeneous features, recognition test, and recognition result storage. The sea surface target feature fusion classification module is composed of Huawei Ascend high-performance AI embedded acceleration computing unit, embedded multimedia storage card, high-speed interface device and target recognition algorithm, including the storage of trained sea surface target recognition algorithm model, feature call reading of input samples, calculation fusion of multi-mode feature kernel matrix, test classification of input target samples, and finally completes the recognition of sea surface targets, that is, the determination of target category.

7. The multi-mode fusion target recognition system for a highly dynamic unmanned platform as claimed in claim 1, characterized in that: The information transmission module of the information transmission control subsystem mainly completes the wired and wireless transmission functions of target identification data, target coordinate data and other information; wired transmission is mainly responsible for data transmission within the system; wireless transmission is mainly responsible for data transmission between the system and the ground control station; according to the information transmission distance, wireless digital data link sending and receiving equipment is used, or data link and communication relay equipment are used to achieve corresponding functions; the intelligent control module of the information transmission control subsystem mainly controls the normal and orderly operation of each sensor module, sea surface target feature fusion classification module, information transmission module and other modules through the transmission of control signals in the entire sea surface target identification system. The function of this module is directly completed by the onboard control device of the Ascend AI acceleration operator.

8. The multi-mode fusion target recognition system for a highly dynamic unmanned platform as claimed in claim 1, characterized in that: The remote display and control subsystem is a ground display control module. The remote display and control subsystem is set on the ground control station host, which is convenient for the training, integration and debugging of the target recognition algorithm, and also convenient for the visualization of the target recognition function; the remote display and control subsystem realizes the visualization output and positioning identification of the target in the digital map of the remote ground station through the target coordinates and target recognition information obtained wirelessly, and completes the real-time tracking and character overlay functions of the target; the subsystem is mainly composed of a general-purpose computer host, a display device, and a geographic information system hardware and software device.

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