Height inversion method and device for marine micro-motion target based on one-dimensional range image

Abstract

The application discloses a sea micro-motion target height inversion method and device based on one-dimensional range profile, and belongs to the technical field of radar target feature extraction. The method provided by the application only depends on target HRRP, utilizes the swing difference of different height scattering points of a large sea micro-motion target under high sea conditions, combines a deep learning algorithm, obtains the moving range of the same scattering point in a distance unit on the radar line of sight, and then inverts the height information of each scattering point. The method comprises micro-motion target modeling, HRRP acquisition, scattering point clustering and collection, and height inversion. The application utilizes time-sequentially continuous multiple frames of HRRP to obtain the height information of target scattering points, expands and enriches the feature dimension of the target, lays a foundation for more accurate target recognition and classification, and has important research value.

Description

Technical Field

[0001] This invention belongs to the field of radar target feature extraction technology, specifically relating to a method and apparatus for elevation inversion of marine micro-moving targets based on one-dimensional range profiles. Background Technology

[0002] With the continuous advancement of broadband radar technology, radar resolution has been significantly improved. For example... Figure 1 As shown, for large maritime targets such as ships, whose size far exceeds radar resolution, according to electromagnetic field theory, these targets are decomposed into multiple equivalent scattering centers, rather than single point targets. Each scattering center generates a sub-echo, and by vector superimposing these sub-echoes, a high-resolution one-dimensional range profile (HRRP) of the target can be generated. The HRRP reflects the vector sum of the projections of the target's scattering points onto the radar line of sight, and its envelope varies with the target's structure, containing rich information about the target's structure. For example, the planes, vertices, dihedrals, trihedrals, and antennas of a ship all appear as strong scattering points, revealing the target's structural features.

[0003] In complex sea conditions, targets such as ships undergo complex motions, including oscillations in the x, y, and z axes (such as roll, pitch, and yaw), as well as micro-motion phenomena such as the hull's own sway and the rotation of antenna components. It is worth noting that scattering points at different altitudes will produce different amplitudes at the same oscillation angle, resulting in different range cell movement ranges in HRRP.

[0004] Currently, target feature extraction based on HRRP mainly focuses on scattering point distribution, target size, and envelope shape, and uses classifiers or deep learning methods for target classification and recognition. However, the important feature of target elevation information has not been fully studied. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method and apparatus for elevation inversion of marine micro-moving targets based on one-dimensional range profiles. By analyzing the positional changes of the same scattering point in continuous temporal HRRP, the elevation information of the target can be inverted, thus providing a novel feature dimension for target identification. Therefore, this invention utilizes multiple frames of temporally continuous HRRP to extract the elevation information of the target scattering point, expanding and enriching the feature dimension of the target, thereby laying the foundation for more accurate target identification and classification, and has significant research value.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] A method for elevation inversion of sub-motion targets at sea based on one-dimensional range images includes:

[0008] Step 1: Construct a scattering model for complex ship targets. Combined with sea state characteristics, form a model of the coordinate changes of scattering points at different heights under the condition of oscillation in six directions for complex ship targets.

[0009] Step 2: Obtain multiple temporally consecutive single-frame HRRPs of the target and perform preprocessing; HRRPs are high-resolution one-dimensional range profiles.

[0010] Step 3: Extract the peak points of multiple single-frame HRRPs that are sequentially continuous after preprocessing, associate the same scattering point at different times with the deep learning algorithm, and combine the scattering point position coordinate change model to obtain the maximum number of distance units that each scattering point traverses on the radar line of sight.

[0011] Step 4: Based on the maximum number of distance cells that the scattering point spans in the radar line of sight, derive the elevation inversion formula using geometric relationships, and invert the elevation information of each scattering point.

[0012] On the other hand, the present invention provides a device for inverting the elevation of a small marine target based on a one-dimensional range image, comprising:

[0013] The micro-motion target modeling module is used to construct a scattering model of complex ship targets. Combined with sea state characteristics, it forms a model of the position coordinate changes of scattering points at different heights under the condition of oscillation in six directions of complex ship targets.

[0014] The HRRP acquisition module is used to acquire multiple temporally consecutive single-frame HRRPs of the target and perform preprocessing; the HRRP is a high-resolution one-dimensional range profile.

[0015] The scattering point clustering and aggregation module is used to extract the peak points of multiple single-frame HRRPs that are sequentially continuous after preprocessing. It uses a deep learning algorithm to associate the same scattering point at different times and combines the scattering point position coordinate change model to obtain the maximum number of distance units that each scattering point can cross on the radar line of sight.

[0016] The elevation inversion module is used to derive the elevation inversion formula based on the maximum number of distance cells that a scattering point spans on the radar line of sight, combined with geometric relationships, and to invert the elevation information of each scattering point.

[0017] Thirdly, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for elevation inversion of marine micro-moving targets based on one-dimensional range images.

[0018] Fourthly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned method for elevation inversion of marine micro-moving targets based on a one-dimensional range image.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention extracts target elevation information using only multiple consecutive frames of HRRP (High-Resolution Mapping) in complex sea environments and scenarios involving complex combinations of maritime target movements. By analyzing the amplitude differences of scattering points at different altitudes under high sea states and combining this with deep learning algorithms, the invention accurately obtains the movement range of the scattering points within the radar line of sight, thereby retrieving their elevation information. This innovation provides a new dimension for maritime target feature extraction and fills a gap in existing technologies for target elevation information extraction.

[0021] This invention integrates sample points, prior environmental information, and prior target characteristic information into the network. By weighted processing of environmental and target characteristic information, it achieves accurate clustering of sample points in the joint space. This technique improves the accuracy and efficiency of scattering point aggregation, providing data support for elevation information inversion. Furthermore, using the extracted elevation information as target features enriches the dimensions of target features, thus providing a more comprehensive and accurate basis for subsequent target identification and classification, improving the accuracy and reliability of target identification. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the principle of a high-resolution one-dimensional range image of a ship in the radar line of sight in the existing technology.

[0023] Figure 2 The flowchart shows the elevation inversion method for marine micro-moving targets based on one-dimensional range images according to the present invention.

[0024] Figure 3 The diagram shows three consecutive HRRP frames under the same sea state, where (a) is the moment when the ship does not sway, (b) is the moment when the ship sways downward, and (c) is the moment when the ship sways upward.

[0025] Figure 4 A schematic diagram of three consecutive HRRP observations during the ship's pitching motion.

[0026] Figure 5 The flowchart shows the scattering point aggregation algorithm based on deep learning.

[0027] Figure 6 This is a diagram illustrating the principle of elevation inversion.

[0028] Figure 7 This is a system block diagram of the single-pulse detection and tracking radar system involved in the present invention;

[0029] Figure 8 This is a schematic diagram of the device for inverting the elevation of marine micro-moving targets based on a one-dimensional range image according to the present invention. Detailed Implementation

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0031] This invention focuses on the varying amplitudes of scattering points from complex, multi-scattering targets such as large maritime vessels at different altitudes under varying sea conditions, influenced by ocean waves and winds. Based on a temporally continuous multi-frame HRRP (Heat Reflection Mapping) of the target, the amplitudes of scattering points at different altitudes under different sea state conditions are analyzed and extracted. Using a deep learning algorithm, the same scattering point at each moment described in the temporally continuous multi-frame HRRP is grouped into the same category. Based on the movement range of the same scattering point within the range cell on the radar line of sight, the elevation information of each scattering point is retrieved.

[0032] like Figure 2 As shown, the elevation inversion method for marine micro-motion targets based on one-dimensional range images of the present invention includes the following steps:

[0033] Step 1, Micro-motion target modeling: Construct a scattering model of complex ship targets, and combine it with the characteristics of the sea conditions to form a model of the position coordinate changes of the scattering points of complex ship targets at different heights under the condition of swinging in six directions, so as to provide target scattering point position information for the acquisition of each single frame HRRP;

[0034] Under the micro-motion conditions caused by sea winds and waves, the strong scattering points of large and complex ship targets at different heights will have inconsistent motion states on the sea surface. The micro-motion state of the ship on the sea surface is modeled.

[0035] For large and complex ship targets, to simplify the modeling process, the ship targets are selected to be distributed at different heights, such as the deck, island, and electronic mast. scattering points ( In a three-dimensional coordinate system, set the three-dimensional position coordinates of each scattering point. ,in, Indicates the first One scattering point, , This indicates the time corresponding to the frame.

[0036] Based on meteorological and ship-related intelligence data such as sea conditions and wind force, a complex ship target scattering model is used to analyze the ship's oscillation state during the observation period. The coordinates of each scattering point of the target at different times under different sea state levels are determined, forming a model of the coordinate changes of scattering points at different heights under six-directional oscillation conditions of a complex ship target. Calculations are then performed. The position coordinates of each scattering point of the target after the swing. The observation time includes That moment, namely .

[0037] Step 2, HRRP Acquisition: Acquire the high-resolution one-dimensional range image (HRRP) of the target and perform velocity compensation and envelope alignment preprocessing;

[0038] The radar system transmits a broadband radar signal to image the target in the range direction, obtaining the vector sum of the projections of the target's scattered point echoes onto the radar's line of sight, i.e., HRRP. Each peak point in the HRRP corresponds to a strong scattering point of the target.

[0039] For ease of analysis, it is assumed that the ship only experiences pitch oscillation and not oscillation in any other direction. Based on geometric analysis, it can be determined that each strong scattering point on the ship will move within a certain number of range units in the radar line of sight.

[0040] Assumption At any given time, the ship's hull was in its original state, such as Figure 3 As shown in (a), the hull oscillates continuously with the movement of waves and wind, similar to periodic oscillation. This invention selects only three specific moments. , , Let's take an example to illustrate, where, At any moment the hull is in Figure 3 The state shown in (b) is the moment when the bow of the ship swings downward to its maximum angle; The ship's hull was once again in a state of... Figure 3 The state shown in (a) represents the moment when the hull reaches its original state. At any moment the hull is in Figure 3 The state shown in (c) is the moment when the bow swings upward to its maximum angle.

[0041] During the observation period, the radar system continuously transmits broadband linear frequency modulated signals to continuously perform one-dimensional imaging operations on the target, thereby acquiring multiple frames of target HRRP in a temporally continuous sequence. This invention defines a single frame HRRP signal as... The result of accumulating pulses. The number of pulse accumulations is represented by the HRRP value per frame. The target state at any given moment.

[0042] After pulse compression and pulse accumulation, a single-frame HRRP of the target is obtained, and preprocessing operations such as velocity compensation, envelope alignment, and clutter and noise removal are performed on the single-frame HRRP. Under the same sea state, N consecutive single-frame HRRP imaging results are jointly observed. Figure 4 It demonstrates temporal continuity ( time, time, A schematic diagram of the three-frame HRRP joint observation at time (time).

[0043] Step 3, Scattering Point Clustering and Grouping: Extract the peak points of preprocessed temporally continuous HRRP from multiple frames to form a scatter plot. Use a deep learning algorithm to associate the same scattering point at different times and obtain the maximum number of distance units traversed by each scattering point. The deep embedding clustering method includes inputting sample points, environmental prior information, and target characteristic prior information into the network, optimizing the cluster center through weighted joint space, iteratively updating the category of scattering points, and outputting the maximum number of distance units for each scattering point for elevation inversion.

[0044] A sequentially consecutive multi-frame HRRP can reflect the target's swaying behavior. Figure 3 It is known that during the oscillation process, the same scattering point of the target moves across the range cells of each frame of HRRP, occupying adjacent range cells at different times. Because the oscillation amplitude is inconsistent, the number of range cells moved by scattering points at different heights varies across multiple temporally consecutive HRRP frames. This causes changes in the relative positions of scattering points at different heights, resulting in overlap, intersection, or submersion of HRRP peak point regions corresponding to adjacent scattering points. Consequently, it becomes impossible to distinguish which scattering point of the target corresponds to the peak point of a single frame of HRRP, and thus, it is impossible to determine the range of range cell movement of the same scattering point at different times. Therefore, it is necessary to use deep learning algorithms to cluster the same scattering points at different times.

[0045] This step clusters the same scattering point at each time point to obtain the complete set of the same scattering point in N consecutive HRRP frames, and then obtains the maximum number of distance units traversed by each scattering point.

[0046] like Figure 5 As shown, firstly, peak points are extracted. Using N consecutive frames of HRRP as input data, peak points from each frame of HRRP are extracted to form a scatter plot, which serves as the set of sample points to be classified.

[0047] Then, deep learning algorithms are used to cluster the sample points, automatically selecting sample points corresponding to the same scattering point at each time point in the scatter plot and identifying them as grouped into the same scattering point. Assuming the target has m scattering points, and each scattering point is denoted as a category, this step requires classifying all sample points into m categories. Due to the complexity of the marine environment, this step uses Deep Embedded Cluster (DEC) to cluster the sample points. The DEC algorithm jointly optimizes the deep embedding feature representation and clustering assignment results, and iterates through a soft assignment method, outputting the optimal clustering results in terms of both accuracy and speed. Sample points, prior environmental information (sea conditions, wind force, etc.), and prior target characteristic information (target RCS scintillation characteristics, target scattering point oscillation amplitude, etc.) are input into the network to cluster the scatter points in the joint space. For this application scenario, high-precision clustering of sample points is achieved by weighting the environmental information and target characteristic information. The algorithm network outputs the category to which each scatter point belongs (e.g., Figure 5 (The labels 1-7 shown here are used as an example to represent 7 categories). Combined with the target scattering point position information provided by the scattering point position coordinate change model, the maximum number of range cells occupied by each scattering point in the radar line of sight is obtained. .

[0048] Step 4, Elevation Inversion: Based on the maximum number of range cells spanned by the scattering point on the radar line of sight. By combining geometric relationships, the elevation inversion formula is derived to invert the elevation information of each scattering point.

[0049] Establish a three-dimensional Cartesian coordinate system with the ship's center of mass as the origin O, with the bow located on the positive x-axis and the stern located on the negative x-axis. Figure 6 This demonstrates the position of the ship's hull in the coordinate system when viewed from the positive y-axis direction towards the negative y-axis direction, i.e., the xoz plane. This invention uses the micro-motion characteristics of a strong scattering point P at an arbitrary height on a ship model as an example. Let the initial state be... At that time, the ship was in a horizontal position, and the coordinates of point P were... , Let h be the height of point P. The angle between line segment OP and the x-axis is... According to geometric relationships, and , The following relationship exists:

[0050] (1)

[0051] When a ship's hull is affected by environmental factors such as waves and winds, it will periodically oscillate around its y-axis. At that time, assume the ship swings to its maximum angle. At this point, point P moves to the point of maximum swing P', and the coordinates of P' are... , These are unknown parameters. Assume the radar radiates electromagnetic waves directly towards the bow of the ship along the positive x-axis, with coordinates as follows: , Time and By acquiring two frames of target HRRP at any given time, the distance to point P on the radar line of sight can be obtained. After passing through a deep learning network, the number of distance units between points P and P' is obtained. s is the maximum number of distance units occupied by point P during the swing, where, , The basic principle of elevation inversion is derived as follows:

[0052] (2)

[0053] Where OP represents the distance from the origin O to point P.

[0054] Comparing both sides of the above two equations, we get:

[0055] (3)

[0056] (4)

[0057] Substituting into equation (1), we get:

[0058] (5)

[0059] (6)

[0060] The height of point P can be inverted using equation (6). Equation (6) is the basic principle formula for elevation inversion.

[0061] like Figure 7 As shown, the present invention relates to a single-pulse detection and tracking radar system, which comprises four parts: a phased array antenna unit, an integrated radio frequency unit, an integrated digital unit, and a power supply unit.

[0062] A phased array antenna unit includes the phased array antenna array surface, T / R assembly, beam control assembly, and power supply assembly. The function of the phased array antenna unit is to achieve spatially directional radiation of high-resolution radio frequency signals and receive target echoes under the control of the integrated digital unit.

[0063] The integrated radio frequency unit includes a frequency synthesizer, a radar transmitter, and a receiver. It is mainly responsible for generating linear frequency modulated pulse signals, amplifying and demodulating target echo signals, and providing clock signals for the integrated digital unit.

[0064] The integrated digital unit includes a high-speed AD / DA converter and a control and signal processing module. It is mainly responsible for generating radar transmitted signals, sampling echo signals, processing and analyzing radar signals, and providing control signals to the integrated radio frequency unit.

[0065] The power supply unit is responsible for supplying power to the above-mentioned units.

[0066] like Figure 8 As shown, the present invention also provides a device for elevation inversion of marine micro-moving targets based on one-dimensional range images. Its various modules are capable of implementing the various steps of the aforementioned method. Each processing step is performed in the integrated digital unit of the aforementioned monopulse detection and tracking radar system, including:

[0067] The micro-motion target modeling module is used to construct a scattering model of complex ship targets. Combined with sea state characteristics, it forms a model of the position coordinate changes of scattering points at different heights under the condition of oscillation in six directions of complex ship targets.

[0068] The HRRP acquisition module is used to acquire multiple temporally consecutive single-frame high-resolution one-dimensional range profiles (HRRPs) of the target and perform preprocessing.

[0069] The scattering point clustering and aggregation module is used to extract the peak points of multiple single-frame HRRPs that are sequentially continuous after preprocessing. It uses a deep learning algorithm to associate the same scattering point at different times and combines the scattering point position coordinate change model to obtain the maximum number of distance units that each scattering point can cross on the radar line of sight.

[0070] The elevation inversion module is used to derive the elevation inversion formula based on the maximum number of distance cells that a scattering point spans on the radar line of sight, combined with geometric relationships, and to invert the elevation information of each scattering point.

[0071] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0072] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0073] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0075] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0076] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for elevation inversion of marine micro-moving targets based on one-dimensional range images, characterized in that, The method includes: Step 1: Construct a scattering model for complex ship targets. Combined with sea state characteristics, form a model of the coordinate changes of scattering points at different heights under the condition of oscillation in six directions for complex ship targets. Step 2: Obtain multiple temporally consecutive single-frame HRRPs of the target and perform preprocessing; HRRPs are high-resolution one-dimensional range profiles. Step 3: Extract the peak points of multiple temporally consecutive single-frame HRRP after preprocessing, associate the same scattering point at different times using a deep learning algorithm, and combine the scattering point position coordinate change model to obtain the maximum number of range cells traversed by each scattering point on the radar line of sight; including: The peak points of HRRP in multiple consecutive single frames after preprocessing are extracted and a scatter plot is formed, which is used as the sample points to be classified. Deep learning algorithms are used to cluster the classified sample points, so as to automatically select the sample points corresponding to the same scattering point at each time point in the scatter plot and mark them as being grouped into the same scattering point. By combining the target scattering point position information provided by the scattering point position coordinate change model, the maximum number of range cells occupied by each scattering point in the radar line of sight can be obtained; Step 4: Based on the maximum number of distance cells that the scattering point spans in the radar line of sight, derive the elevation inversion formula using geometric relationships, and invert the elevation information of each scattering point.

2. The method for elevation inversion of marine micro-moving targets based on one-dimensional range profiles according to claim 1, characterized in that, Step 1 includes: based on sea condition and wind data, using a complex ship target scattering model to analyze the ship's oscillation state during the observation period, and determining the coordinates of each scattering point of the target at each moment during the observation period for different sea state levels.

3. The method for elevation inversion of marine micro-moving targets based on one-dimensional range profiles according to claim 1, characterized in that, Step 2 includes: During the observation period, the radar system continuously transmits broadband linear frequency modulated signals to continuously perform one-dimensional imaging operations on the target, acquiring multiple single-frame HRRPs of the target in a temporally continuous manner. Perform velocity compensation, envelope alignment, and noise and clutter removal operations on multiple single-frame HRRPs.

4. The method for elevation inversion of marine micro-moving targets based on one-dimensional range profiles according to claim 1, characterized in that, The deep learning algorithm is a deep embedding clustering method.

5. The method for elevation inversion of marine micro-moving targets based on a one-dimensional range image according to claim 4, characterized in that, The deep embedding clustering method includes inputting sample points, environmental prior information, and target characteristic prior information into the network, optimizing the cluster centers through weighted joint space, iteratively updating the category assignment of scattering points, and outputting the maximum number of distance units for each scattering point for elevation inversion.

6. The method for elevation inversion of marine micro-moving targets based on one-dimensional range profiles according to claim 1, characterized in that, Step 4 includes: Set the coordinates of the target scattering point at the initial moment; When the ship swings to its maximum angle, update the coordinates of the target scattering point. Based on the changes in the number of range cells on the radar line of sight and the maximum swing angle, the elevation inversion formula is derived.

7. A device for inverting the elevation of a small marine target based on a one-dimensional range image, characterized in that, include: The micro-motion target modeling module is used to construct a scattering model of complex ship targets. Combined with sea state characteristics, it forms a model of the position coordinate changes of scattering points at different heights under the condition of oscillation in six directions of complex ship targets. The HRRP acquisition module is used to acquire multiple temporally consecutive single-frame HRRPs of the target and perform preprocessing; the HRRP is a high-resolution one-dimensional range profile. The scattering point clustering and aggregation module is used to extract the peak points of multiple temporally consecutive single-frame HRRP after preprocessing. It associates the same scattering point at different times using a deep learning algorithm and, combined with a scattering point position coordinate change model, obtains the maximum number of range cells spanned by each scattering point on the radar line of sight; including: The peak points of HRRP in multiple consecutive single frames after preprocessing are extracted and a scatter plot is formed, which is used as the sample points to be classified. Deep learning algorithms are used to cluster the classified sample points, so as to automatically select the sample points corresponding to the same scattering point at each time point in the scatter plot and mark them as being grouped into the same scattering point. By combining the target scattering point position information provided by the scattering point position coordinate change model, the maximum number of range cells occupied by each scattering point in the radar line of sight can be obtained; The elevation inversion module is used to derive the elevation inversion formula based on the maximum number of distance cells that a scattering point spans on the radar line of sight, combined with geometric relationships, and to invert the elevation information of each scattering point.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the elevation inversion method for marine micro-moving targets based on a one-dimensional range image as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to implement the elevation inversion method for marine micro-moving targets based on a one-dimensional range image as described in any one of claims 1-6.

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