Real-time estimation method and system for adaptive signal-to-noise ratio of small-target radar

Abstract

The invention provides a real-time estimation method and system for a small-target radar adaptive signal-to-noise ratio. According to the method, a motion model is established based on a historical trajectory of a maneuvering target, and a dynamic position prediction sequence is generated; acquiring a scanning beam pointing angle sequence and a timestamp echo signal intensity sequence; starting multiple rounds of iterative detection for each target position point in the prediction sequence, and shrinking a detection window bidirectionally until the boundary is smaller than the width of a beam half-power point; a signal intensity peak value and background noise intensity in a final iteration window are extracted, a beam irradiation efficiency factor is generated in combination with the spatial matching degree of the scanning beam directional angle sequence and the position prediction sequence, and a signal-to-noise ratio dynamic correction is cooperatively calculated; and weighting and fusing the correction, the signal peak value and the noise intensity, and outputting a final signal-to-noise ratio value of the maneuvering target in real time. According to the invention, the signal-to-noise ratio real-time estimation precision and stability of a small target radar when a high-speed maneuvering target passes through a beam edge are improved.

Description

Technical Field

[0001] The present application relates to the technical field of signal-to-noise ratio estimation, and in particular to a real-time estimation method and system for adaptive signal-to-noise ratio of a small target radar. Background Art

[0002] In low-altitude, small-target surveillance scenarios in complex electromagnetic environments, radar systems must achieve continuous and stable signal-to-noise ratio estimation for high-speed, maneuvering targets. These targets exhibit highly abrupt trajectory changes and small radar cross-sections. Given the extremely short dwell time of mechanically scanned radar beams, traditional methods struggle to maintain estimation accuracy when targets cross the beam edge. This necessitates addressing the core parameter jumps caused by the mismatch between target maneuverability and scanning timing.

[0003] The current mainstream solution adopts a detection mechanism that combines dynamic gate adjustment with historical trajectory extrapolation: a linear prediction model is established based on the target's motion state in the previous scanning cycle, and an adaptive detection gate is set according to the predicted position during the radar scanning process. The signal-to-noise ratio output value is dynamically corrected by comparing the statistical difference between the echo signal intensity and the background noise in real time and combining it with a preset threshold.

[0004] This scheme has significant limitations when the target maneuvers suddenly: the linear prediction model cannot adapt to the nonlinear changes in the trajectory, resulting in detection gate positioning deviation; the preset threshold is difficult to match the changes in beam morphology under different scanning elevation angles, resulting in failure of correction in the beam edge area; the signal-to-noise ratio correction process does not consider the temporal and spatial coupling relationship between the actual beam center and the target position, and the final output value shows systematic distortion during the target maneuvering and turning phase. Summary of the Invention

[0005] The present application provides a real-time estimation method and system for adaptive signal-to-noise ratio of a small target radar, which is used to solve the problem of insufficient accuracy and stability in real-time estimation of signal-to-noise ratio of a small target radar in the prior art.

[0006] In a first aspect, the present application provides a real-time estimation method for adaptive signal-to-noise ratio of a small target radar, comprising:

[0007] During the radar scanning interval, a motion feature model is established based on the continuous historical trajectory points of the maneuvering target to generate a dynamic position prediction sequence of the target in the next scanning cycle;

[0008] A radar scanning data packet is acquired at a fixed rotation speed by a mechanical rotating scanning device, and a scanning beam pointing angle sequence and a corresponding time stamp echo signal strength sequence are synchronously recorded. The scanning beam pointing angle sequence includes the azimuth and elevation angles of the beam center axis.

[0009] For each target position point in the dynamic position prediction sequence, multiple rounds of iterative detection are initiated within its spatial neighborhood. The first round of detection uses an initial detection window that covers the target's maximum maneuvering range. In subsequent rounds, the detection window range is bidirectionally contracted in the range and azimuth directions based on the distribution characteristics of the echo signal strength sequence within the preceding detection window, until the detection window boundary is smaller than the half-power point width of the scanning beam.

[0010] Extracting the signal intensity peak and the corresponding background noise intensity in the final iteration round, calculating the spatial matching degree between the scanning beam pointing angle sequence and the dynamic position prediction sequence, converting the spatial matching degree into a beam illumination efficiency factor, and collaboratively calculating the dynamic correction value of the signal-to-noise ratio based on the ratio of the signal intensity peak and the corresponding background noise intensity;

[0011] Based on the dynamic correction amount of the signal-to-noise ratio, the signal strength peak value and the corresponding background noise intensity, weighted fusion is performed to output the final signal-to-noise ratio value of the maneuvering target in the current scanning cycle in real time.

[0012] Optionally, for each target position point in the dynamic position prediction sequence, multiple rounds of iterative detection are initiated within its spatial neighborhood, wherein the first round of detection uses an initial detection window covering the maximum maneuvering range of the target, and subsequent rounds of detection shrink the detection window range in both range and azimuth directions according to the distribution characteristics of the echo signal strength sequence within the first round of detection window until the detection window boundary is less than the half-power point width of the scanning beam, including:

[0013] Taking each target position point in the dynamic position prediction sequence as the center point, a square initial detection window is formed by expanding in the range and azimuth directions, wherein the side length of the square initial detection window is set to cover the maximum maneuvering offset range of the target;

[0014] Analyzing the intensity distribution of the echo signal intensity sequence within the boundary of the square initial detection window, and when detecting that an intensity peak point of the intensity distribution state is located in an edge area of ​​the square initial detection window, shrinking the boundary in proportion along the direction of the intensity peak point;

[0015] The detection window shrinking operation is repeatedly performed until the boundary range of the square initial detection window is smaller than the half-power point width of the scanning beam and the intensity peak point is in the central area of ​​the square initial detection window, and it is determined that the iteration termination condition is met.

[0016] Optionally, extracting the signal intensity peak and the corresponding background noise intensity in the final iteration round, calculating the spatial matching degree between the scanning beam pointing angle sequence and the dynamic position prediction sequence, converting the spatial matching degree into a beam illumination efficiency factor, and collaboratively calculating the dynamic correction value of the signal-to-noise ratio in combination with the ratio of the signal intensity peak and the corresponding background noise intensity, includes:

[0017] The difference between the coordinates of the scanning beam center axis pointing point and the target dynamic position prediction point at the same time mark is calculated to obtain the beam pointing position deviation;

[0018] Generate a beam illumination efficiency factor according to the magnitude of the beam pointing position deviation, wherein the mapping relationship satisfies that the beam illumination efficiency factor monotonically decreases when the beam pointing position deviation increases;

[0019] The signal intensity peak value in the final iteration round is divided by the corresponding background noise intensity to obtain the original signal-to-noise ratio value, and then multiplied by the beam illumination efficiency factor to generate the dynamic correction value of the signal-to-noise ratio.

[0020] Optionally, the mechanically rotating scanning device is used to obtain radar scanning data packets at a fixed rotation speed, and a scanning beam pointing angle sequence and an echo signal strength sequence corresponding to a timestamp are synchronously recorded, wherein the scanning beam pointing angle sequence includes an azimuth angle and a pitch angle of a beam center axis, including:

[0021] When the mechanical rotary scanning device rotates at a constant angular velocity, whenever the azimuth angle of the central axis of the scanning beam reaches a preset angle sampling position, the radar transmitting unit is triggered to send a detection pulse signal and synchronously receive the target reflection signal;

[0022] The target reflection signal sampled at each azimuth angle is divided into time dimensions, and the echo intensity values ​​of all range units within the same time slice are combined into two-dimensional distribution data of azimuth and range;

[0023] The maximum signal intensity within the main lobe coverage area of ​​the scanning beam and its corresponding range unit coordinates are extracted from the azimuth and range two-dimensional distribution data, and an echo signal intensity sequence is generated in the order of timestamps. At the same time, the azimuth and elevation angle values ​​of the central axis of the scanning beam at each sampling point are recorded to form a scanning beam pointing angle sequence.

[0024] Optionally, the performing weighted fusion based on the dynamic correction amount of the signal-to-noise ratio, the signal strength peak value, and the corresponding background noise intensity, and outputting a final signal-to-noise ratio value of the maneuvering target in the current scanning cycle in real time, includes:

[0025] Assigning a first fusion weight value to the signal strength peak, assigning a second fusion weight value to the corresponding background noise intensity, and assigning a third fusion weight value to the dynamic correction amount of the signal-to-noise ratio, wherein the sum of the fusion weight values ​​is a constant;

[0026] Multiplying the signal intensity peak by the first fusion weight value to form a main component, multiplying the corresponding background noise intensity by the second fusion weight value to form a noise component, and multiplying the signal-to-noise ratio dynamic correction amount by the third fusion weight value to form a correction component;

[0027] A superposition calculation is performed on the main component, the noise component, and the correction component, and a final signal-to-noise ratio value of the maneuvering target in the current scanning cycle is output.

[0028] Optionally, within the boundary range of the square initial detection window, analyzing the intensity distribution state of the echo signal intensity sequence, and when detecting that an intensity peak point of the intensity distribution state is located in an edge area of ​​the square initial detection window, proportionally shrinking the boundary range along the direction where the intensity peak point is located, includes:

[0029] Dividing an edge region in the square initial detection window, wherein the edge region is defined as a narrow edge region adjacent to the detection window boundary;

[0030] Locating a spatial position point corresponding to a maximum signal intensity within a current detection window in the echo signal intensity sequence, and if the spatial position point falls within the narrow edge area, determining that the intensity peak point is in an edge position state;

[0031] The detection window boundary is moved toward the detection window center point along the line connecting the intensity peak point at the edge position and the detection window center point by a preset shrinking distance to form a new shrinking detection window boundary range.

[0032] Optionally, the calculating of the difference between the scanning beam central axis pointing point coordinates and the target dynamic position prediction point coordinates at the same time mark to obtain the beam pointing position deviation includes:

[0033] Converting the azimuth angle and the elevation angle of the central axis of the scanning beam into beam pointing space coordinates in a three-dimensional space coordinate system;

[0034] Extracting target prediction spatial coordinates corresponding to the same time stamp from the dynamic position prediction sequence;

[0035] The spatial straight-line distance between the beam pointing spatial coordinates and the target prediction spatial coordinates is calculated, and the output is the beam pointing position deviation.

[0036] In a second aspect, the present application provides a real-time estimation system for adaptive signal-to-noise ratio of a small target radar, comprising:

[0037] The scanning module is used to establish a motion feature model based on the continuous historical trajectory points of the maneuvering target during the radar scanning interval and generate a dynamic position prediction sequence of the target in the next scanning cycle;

[0038] A recording module is configured to acquire radar scanning data packets at a fixed rotation speed through a mechanical rotating scanning device, and synchronously record a scanning beam pointing angle sequence and a corresponding time stamp echo signal strength sequence, wherein the scanning beam pointing angle sequence includes the azimuth and elevation angles of the beam center axis;

[0039] a detection module configured to initiate multiple rounds of iterative detection within a spatial neighborhood of each target position point in the dynamic position prediction sequence, wherein the first round of detection uses an initial detection window covering the target's maximum maneuvering range, and in subsequent rounds, the detection window range is bidirectionally contracted in range and azimuth directions based on the distribution characteristics of the echo signal strength sequence within the first round of detection window until the detection window boundary is less than the half-power point width of the scanning beam;

[0040] a calculation module for extracting the signal intensity peak and the corresponding background noise intensity in the final iteration round, calculating the spatial matching degree between the scanning beam pointing angle sequence and the dynamic position prediction sequence, converting the spatial matching degree into a beam illumination efficiency factor, and collaboratively calculating a dynamic correction value for the signal-to-noise ratio based on the ratio of the signal intensity peak and the corresponding background noise intensity;

[0041] The output module is used to perform weighted fusion based on the dynamic correction amount of the signal-to-noise ratio, the signal strength peak value and the corresponding background noise intensity, and output the final signal-to-noise ratio value of the maneuvering target in the current scanning cycle in real time.

[0042] In a third aspect, the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a real-time estimation method for the adaptive signal-to-noise ratio of a small target radar as described in the first aspect above.

[0043] In a fourth aspect, the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a real-time estimation method for the adaptive signal-to-noise ratio of a small target radar as described in the first aspect.

[0044] In an embodiment of the present application, during the radar scanning interval, a motion feature model is established based on the continuous historical trajectory points of the maneuvering target to generate a dynamic position prediction sequence of the target in the next scanning cycle; a radar scanning data packet is obtained at a fixed speed by a mechanically rotating scanning device, and a scanning beam pointing angle sequence and an echo signal strength sequence of the corresponding timestamp are synchronously recorded, wherein the scanning beam pointing angle sequence includes the azimuth and pitch angle of the beam center axis; for each target position point in the dynamic position prediction sequence, multiple rounds of iterative detection are started within its spatial neighborhood, and the first round of detection uses an initial detection window covering the maximum maneuvering range of the target, and subsequent rounds are based on the echo signal strength sequence in the front wheel. The distribution characteristics within the detection window are detected, and the detection window range is bidirectionally shrunk in the range and azimuth directions until the detection window boundary is smaller than the half-power point width of the scanning beam; the signal intensity peak value and the corresponding background noise intensity in the final iterative round are extracted, and the spatial matching degree between the scanning beam pointing angle sequence and the dynamic position prediction sequence is calculated, and the spatial matching degree is converted into a beam illumination efficiency factor. The dynamic correction value of the signal-to-noise ratio is collaboratively calculated in combination with the ratio of the signal intensity peak value and the corresponding background noise intensity; weighted fusion is performed based on the dynamic correction value of the signal-to-noise ratio, the signal intensity peak value and the corresponding background noise intensity, and the final signal-to-noise ratio value of the maneuvering target in the current scanning cycle is output in real time.

[0045] The technical solution of this application has the following beneficial effects:

[0046] A motion model is established based on historical trajectories to generate a dynamic position prediction sequence, solving the trajectory mismatch problem caused by sudden changes in target maneuvers and providing highly timely position prior information for subsequent scanning and focusing. The beam pointing angle and echo intensity sequence are synchronously acquired through a mechanical scanning device to ensure the spatiotemporal alignment of the scanning parameters and the signal data, providing an accurate physical scanning basis for iterative detection. A dynamic contraction mechanism for the multi-round iterative detection window is adopted to adaptively adjust the detection range according to the echo intensity distribution, overcoming the signal capture failure of small targets at the edge of the beam and achieving precise locking of the core area of ​​the maneuvering target signal. The beam illumination efficiency factor is generated through spatial matching, and the basic signal-to-noise ratio value is collaboratively corrected to compensate for the signal attenuation error caused by the target maneuvering away from the beam center. The final signal-to-noise ratio value is output by weighted fusion of multi-source parameters, suppressing the random fluctuations of a single scan and improving the estimation stability of high-speed maneuvering targets in complex interference environments.

[0047] Furthermore, an initial detection window is expanded around the predicted target location to cover the maximum maneuvering range. The echo intensity distribution within the window is analyzed. When the intensity peak is detected at the edge of the window, the detection window boundary is proportionally contracted in the direction of the peak toward the center. This contraction operation is repeated until the detection window is smaller than the half-power width of the beam and the peak moves into the center of the window, at which point the iteration is terminated. Through a dynamic iterative window contraction mechanism guided by trajectory prediction, the spatial coupling relationship between the target maneuvering trajectory and the scanning beam is adapted in real time. This solves the problem of signal strength acquisition distortion caused by small targets deviating from the beam center during sudden turns, significantly improving the robustness of the signal-to-noise ratio estimation at the edge of the beam.

[0048] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 A flowchart of a real-time estimation method for adaptive signal-to-noise ratio of a small target radar provided by the present application is shown;

[0051] Figure 2 A scene diagram showing a real-time estimation method for adaptive signal-to-noise ratio of a small target radar provided by the present application is shown;

[0052] Figure 3 A schematic diagram of the structure of a real-time estimation system for adaptive signal-to-noise ratio of a small target radar provided by the present application is shown;

[0053] Figure 4 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0055] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0056] Existing dynamic gate adjustment schemes have three fundamental limitations when dealing with low-altitude, high-speed maneuvering targets: First, the linear trajectory prediction model cannot adapt to the target's sudden nonlinear maneuvers, causing the detection gate to continuously deviate from its true position. Second, the fixed correction threshold is severely mismatched with the dynamic widening of the mechanical scanning beam with elevation angle, resulting in complete failure of signal-to-noise ratio correction at the beam's edges. Third, the signal-to-noise ratio calculation process completely ignores the spatial coupling between the beam's central axis and the target's actual position, causing systematic distortion in the output value during the target's maneuvering and steering phase. These deficiencies collectively lead to uncontrollable jumps in the signal-to-noise ratio estimate when small targets cross the critical beam region, severely limiting the radar system's tracking stability.

[0057] In response to the above-mentioned defects, the present invention proposes a real-time estimation method for the signal-to-noise ratio based on the three-element collaboration of trajectory, scanning and iteration. The core of the method is to establish a dynamic closed-loop coupling mechanism of trajectory prediction, mechanical scanning and iterative detection. Specifically, a time-space synchronized position prediction sequence is generated through nonlinear motion feature modeling, which drives multiple rounds of iterative detection windows to adaptively shrink in both distance and azimuth to lock the core area of ​​the target signal in real time; at the same time, a spatial matching model of the beam pointing and the predicted position is constructed to generate a beam efficiency correction factor that evolves dynamically with the position deviation, and finally fuses the signal strength, noise floor and correction factor to output the optimized value. This method significantly enhances the continuous tracking capability of small target radars in complex environments through the triple collaboration of trajectory data guiding scanning focusing, iterative detection adapting beam shape, and spatial matching compensating for positioning errors.

[0058] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0059] Figure 1The present invention provides a flowchart of a method for real-time estimation of the adaptive signal-to-noise ratio of a small target radar. Figure 1 As shown, the method includes:

[0060] During the radar scanning interval, a motion feature model is established based on the continuous historical trajectory points of the maneuvering target to generate a dynamic position prediction sequence of the target in the next scanning cycle;

[0061] In the above scheme, the continuous historical trajectory points of a maneuvering target refer to the target position records continuously detected by the radar system over multiple previous scanning cycles. Each record contains the time point when the target was discovered, the straight-line distance value from the radar, the azimuth angle value, and the pitch angle value. The motion feature model is a data relationship structure used to describe the laws of target motion. This model establishes prediction rules for the target position changes over time by analyzing the motion change patterns implicit in the historical trajectory points. The dynamic position prediction sequence is a set of future target positions calculated based on the motion feature model. Each position point contains the predicted time point, predicted distance value, predicted azimuth angle value, and predicted pitch angle value. These points are arranged in chronological order to form a sequence.

[0062] In this embodiment, the target's historical trajectory data for the most recent few radar scan cycles is first collected. For example, the target's precise location and corresponding timestamp are extracted for each of the last five scans. Next, the motion characteristics of these historical trajectory points are analyzed. Specifically, the displacement change, direction change, and time interval between adjacent trajectory points are calculated; sudden changes in displacement and direction are identified; and acceleration patterns are statistically analyzed.

[0063] Next, a nonlinear prediction model is constructed based on the identified motion features: if the target motion shows regular speed changes, a polynomial fitting method is used to establish the position-time relationship; if the target motion has random mutations, a state transition probability model is used to describe the position jump rules.

[0064] Then, take the end time of the current scan as the starting point and input the model to deduce the time node of the next scan cycle: execute the model calculation for each time node, and output the predicted distance value, predicted azimuth angle value, and predicted pitch angle value of the target at that moment. Arrange the predicted positions of all time nodes in chronological order to form a dynamic position prediction sequence.

[0065] Finally, the prediction sequence is integrity checked: checking whether the distance change between adjacent prediction points exceeds the maximum detection range of the radar; verifying whether the azimuth / pitch angle changes comply with the mechanical scanning speed constraints; and eliminating abnormal prediction points that do not meet the physical laws to generate the final usable dynamic position prediction sequence.

[0066] 102. Acquire radar scanning data packets at a fixed rotation speed using a mechanical rotating scanning device, and synchronously record a scanning beam pointing angle sequence and an echo signal strength sequence corresponding to a timestamp, wherein the scanning beam pointing angle sequence includes an azimuth angle and a pitch angle of a beam center axis;

[0067] Optionally, step 102 may specifically include the following steps:

[0068] 1021. During the rotation of the mechanical rotary scanning device at a constant angular velocity, whenever the azimuth angle of the central axis of the scanning beam reaches a preset angle sampling position, the radar transmitting unit is triggered to send a detection pulse signal and synchronously receive the target reflection signal;

[0069] 1022. Divide the target reflection signal sampled at each azimuth angle into a time dimension, and combine the echo intensity values ​​of all range cells within the same time slice into two-dimensional distribution data of azimuth and range;

[0070] 1023. Extract the maximum signal intensity within the main lobe coverage area of ​​the scanning beam and its corresponding distance unit coordinates from the two-dimensional distribution data of azimuth and distance, generate an echo signal intensity sequence in chronological order according to timestamps, and simultaneously record the azimuth and elevation angle values ​​of the central axis of the scanning beam at each sampling point to form a scanning beam pointing angle sequence.

[0071] In the above scheme, the mechanical rotary scanning device refers to a radar antenna assembly driven by a motor, with its rotation speed maintained constant. The scanning beam pointing angle sequence is a data sequence that records the direction of the beam centerline, with each data point containing both horizontal and vertical angles. The echo signal intensity sequence is a chronological collection of target reflection signal intensity values, with each intensity value corresponding to a specific distance unit. The two-dimensional azimuth and range distribution data is a tabular data structure that arranges the reflection signals by horizontal angle and distance. The mainlobe coverage area is the central radiation area where the radar wave energy is most concentrated.

[0072] In this embodiment of the present application, first, through step 1021, as the radar antenna rotates at a constant speed, the angle encoder monitors the azimuth angle of the beam center axis in real time. When the azimuth angle reaches a preset sampling point, a trigger circuit synchronously activates the pulse transmission and reception modules. The transmitting unit generates a high-frequency electromagnetic pulse, which is radiated into space via the antenna. After the pulse is transmitted, the receiving unit opens a signal acquisition window, captures the electromagnetic wave energy reflected by the target, and converts it into an electrical signal. For example, if the antenna rotates at 30 degrees per second, sampling is preset every 1 degree. When the azimuth angle reaches 45 degrees, a pulse is immediately transmitted and the receiving channel is opened.

[0073] Next, in step 1022, the reflected signal collected at each azimuth angle is segmented at fixed time intervals determined by the radar pulse repetition period. Each signal segment corresponds to a specific range unit, where distance = speed of light × time interval / 2. The square of the signal amplitude within that range unit is extracted as the echo intensity. The intensity values ​​for all range units are arranged in order of distance to form a "range and intensity" data column for the current azimuth angle. The data columns for multiple azimuth angles are combined into a two-dimensional data matrix. For example, the reflected signal is sliced ​​at 1-microsecond intervals, with each slice corresponding to a 150-meter range unit. At a 45-degree azimuth angle, the intensity values ​​for the 0-150-meter units are recorded in the first column, and those for the 150-300-meter units in the second column, and so on, up to the maximum detection range.

[0074] Finally, step 1023 is used to locate the main lobe area of ​​the beam in the two-dimensional data matrix: search for areas where the intensity of more than three consecutive angular sampling points is significantly higher than the background value along the azimuth direction, and search for unit segments where the intensity continuously exceeds the threshold along the distance direction. Traverse all units in the area and record the maximum intensity and the position of the distance unit where it is located. Synchronously save the azimuth and elevation angles of the beam center axis at the current moment. Accumulate peak intensity, distance coordinates and pointing angle data in timestamp order to form three synchronous sequences. For example, in the data column at an azimuth of 45 degrees, it is found that the intensity of the 60-65 km distance segment exceeds the threshold, among which the intensity at 62 km is the highest at 95 units. Record the peak value of 95 and the distance of 62 kilometers, and save the azimuth of 45 degrees and the elevation of 3 degrees.

[0075] In practical applications, suppose a small drone suddenly makes a sharp 90-degree turn amidst strong sea clutter. The radar needs to capture a valid signal at the moment the target maneuvers. Step 1021 is executed, and the mechanical scanning antenna rotates at a constant speed of 6 revolutions per second, with angle sampling points set every 0.2 degrees. When the target suddenly turns, the antenna rotates to an azimuth angle of 122.3 degrees, triggering pulse transmission. The transmitting unit generates an X-band pulse with a pulse width of 0.5 microseconds, and the receiving unit is activated simultaneously to capture the target's reflected signal amidst the strong sea clutter.

[0076] In step 1022, the reflected signal is sliced ​​at 0.1 microsecond intervals, with each slice corresponding to a 15-meter range resolution. The signal at an azimuth angle of 122.3 degrees is divided into 600 range cells, covering 0-9 kilometers. Each cell records the square of the signal voltage amplitude. This generates a two-dimensional data sequence corresponding to 122.3 degrees: range cell 1 (0-15 meters) has an intensity of 32 units... range cell 547 (8.2 kilometers) has an intensity of 158 units... range cell 600 (9 kilometers) has an intensity of 28 units.

[0077] Analysis of the two-dimensional data series at step 1023 reveals a sudden increase in intensity within the cell cluster at 8.19-8.24 km. The background average is 35 units, while the target area exceeds 150 units. A peak intensity of 158 units is located in range cell 547, corresponding to 8.205 km, confirming that this area is within the main lobe coverage. The current time is recorded: peak intensity 158, range 8.205 km, azimuth 122.3 degrees, and elevation 1.8 degrees. This data point is added to the sequence and timestamped to 123456.789 milliseconds of the radar system clock.

[0078] The overall solution of step 102 above, through the coordinated efforts of the three technologies of angle trigger pulse synchronization mechanism, range slice two-dimensional data reconstruction, and main lobe area peak positioning, can accurately capture instantaneous signal characteristics and associate spatial pointing information in scenarios of sudden target maneuvers and strong background interference, providing a signal strength and beam pointing data set with strict spatial and temporal alignment for subsequent processing, laying the data foundation for real-time estimation of the signal-to-noise ratio.

[0079] 103. For each target position point in the dynamic position prediction sequence, multiple rounds of iterative detection are initiated within its spatial neighborhood. The first round of detection uses an initial detection window that covers the target's maximum maneuvering range. In subsequent rounds, the detection window range is bidirectionally contracted in the range and azimuth directions based on the distribution characteristics of the echo signal strength sequence within the preceding detection window, until the detection window boundary is smaller than the half-power point width of the scanning beam.

[0080] Optionally, step 103 may specifically include the following steps:

[0081] 1031. Taking each target position point in the dynamic position prediction sequence as the center point, expand along the range and azimuth directions to form a square initial detection window, where the side length of the square initial detection window is set to cover the maximum maneuvering offset range of the target;

[0082] 1032. Analyze the intensity distribution of the echo signal intensity sequence within the boundary of the square initial detection window. When it is detected that the intensity peak of the intensity distribution is located at an edge of the square initial detection window, shrink the boundary proportionally along the direction of the intensity peak.

[0083] Among them, step 1032 may specifically include the following process: dividing an edge area in the square initial detection window, and the edge area is defined as a narrow edge area adjacent to the detection window boundary; locating the spatial position point corresponding to the maximum signal intensity within the current detection window range in the echo signal intensity sequence, if the spatial position point falls into the narrow edge area, it is determined that the intensity peak point is in the edge position state; along the direction of the line connecting the intensity peak point in the edge position state and the center point of the detection window, the detection window boundary is moved toward the center point of the detection window by a preset shrinkage distance to form a new detection window boundary range after shrinkage.

[0084] 1033. Repeat the detection window shrinking operation until the boundary range of the square initial detection window is smaller than the half-power point width of the scanning beam and the intensity peak point is in the central area of ​​the square initial detection window, and it is determined that the iteration termination condition is met.

[0085] In the above scheme, the spatial neighborhood is the spatial region centered around the predicted target location. The initial detection window is a square area used in the first round of detection, with a side length covering the target's maximum possible displacement. Range and azimuth are defined as follows: range refers to the linear distance between the radar and the target, and azimuth refers to the horizontal angle. Intensity distribution is the numerical distribution of signal strength at each location within the detection window. The edge region is the annular band near the detection window boundary. The half-power point width is the physical width of the radar beam when the energy drops to half of its peak value.

[0086] In the embodiment of the present application, the spatial coordinates of the target prediction point are first extracted from the dynamic position prediction sequence through step 1031, including the straight-line distance value from the radar and the horizontal azimuth angle value. With this coordinate point as the center reference, the same distance is extended to both sides in the distance direction, and the distance is twice the product of the target's maximum speed and the scanning period; at the same time, the same angle is extended to both sides in the azimuth angle direction, and the angle is twice the product of the target's maximum angular velocity and the scanning period. The larger value of the distance extension and the angle extension is taken as the uniform side length of the square initial detection window to ensure that the window can cover the target's maximum possible displacement range. For example, if the target's maximum flight speed is 400 meters per second and the radar scanning period is 0.1 seconds, the distance extension is 80 meters; if the target's maximum turning angular velocity is 50 degrees per second, the azimuth extension is 10 degrees. 10 degrees is selected as the initial window side length.

[0087] Secondly, through step 1032, within the spatial range covered by the initial detection window, all echo signal strength data points are traversed, and the specific position coordinates of the maximum signal strength are determined by comparing the numerical values. Calculate the closest distance between the peak position point and the four boundaries of the detection window. If the distance value is less than one-fifth of the current window side length, it is determined that the peak point is located in the edge area. At this time, a spatial connecting vector between the peak point position and the window center point is established, and the four boundaries of the detection window are synchronously moved toward the center along the direction indicated by the vector. The moving distance is one-tenth of the current window side length, forming a new reduced version of the detection window. For example, the center of the initial window with a side length of 10 degrees is at an azimuth of 120 degrees, and the peak point is detected to be 1.8 degrees away from the boundary <10 / 5=2 degrees. Shrink in the direction from the center 120° to the peak point 121.8°, and each boundary moves inward by 10 degrees×0.1=1 degree.

[0088] Finally, step 1033 repeats the peak detection and boundary shrinkage operations on the newly shrunken detection window. After each iteration, the current window length is checked to see if it is less than the half-power point width of the radar beam, and the distance between the peak point and the window center is calculated to see if it is less than one-quarter of the window length. The iteration terminates when both the "window size is less than the physical beam width" and the "peak point is located in the window core" are met; otherwise, the boundary shrinkage continues at the same ratio. For example, if the initial window is 10 degrees, after three rounds of shrinkage, it is reduced to 7.3 degrees, which is still greater than the beam width of 2 degrees. The peak point is 1.5 degrees from the center, which is greater than 7.3 / 4 (1.825 degrees), so shrinkage continues. After the fifth round, the window is reduced to 1.8 degrees, which is less than 2 degrees, and the peak point is 0.4 degrees from the center, which is less than 1.8 / 4 (0.45 degrees), so the iteration terminates.

[0089] In a real-world application, a small drone suddenly turns 90 degrees from 10 km away against a backdrop of dense buildings. The radar scan period is 0.1 seconds, and the beamwidth at half power is 1.5 degrees. In step 1031, the dynamic position prediction sequence gives the target position: azimuth 85 degrees, range 10.2 km. The target has a maximum speed of 350 m / s, with a range spread of 350 × 0.1 × 2 = 70 meters. The maximum angular velocity is 60 degrees / s, with an azimuth spread of 60 × 0.1 × 2 = 12 degrees. The initial window length is 12 degrees, forming a square window centered at (85°, 10.2 km), with a range of 79°-91° in azimuth and 9.9-10.5 km in range.

[0090] At step 1032, analysis of the signal strength data within the window reveals a peak strength of 210 units at 88.7° and a distance of 10.38 km, with a mean building clutter value of 80 units. This point is 2.3 degrees from the nearest window boundary, 88.7° to 91°. Since 2.3 degrees is less than 12 / 5 = 2.4 degrees, the actual calculation shows that 91° - 88.7° = 2.3 degrees < 2.4 degrees, indicating an edge region. By shifting the boundary inward along the line connecting the center point 85° and the peak point 88.7°, the new window is narrowed to 80.2°-89.8° and a distance of 10.1-10.45 km.

[0091] Through step 1033, the second round of detection within the new window shifts the peak to 87.5°, 10.32 km. This distance from the boundary (87.5° - 80.2°) is 7.3°, which is greater than the new window length (9.6 / 5 = 1.92 degrees). The window length is 89.8-80.2° = 9.6 degrees. 87.5° is 7.3 degrees from the left boundary (80.2°) and 2.3 degrees from the right boundary (89.8°), which is less than 1.92 degrees—still in the edge zone. The window continues to shrink by 1.2 degrees from the center (85°) to the new peak (87.5°), resulting in an actual shrinkage of 9.6 × 0.1 = 0.96 degrees. After four rounds of shrinkage, the window edge length is reduced to 1.6 degrees, which is greater than 1.5 degrees. The peak point is located at 86.1°, 10.25 km, and the azimuth difference from the window center (85.5°, 10.28 km) is 0.6 degrees, which is greater than 1.6 / 4 = 0.4 degrees. The fifth round shrinks to 1.44 degrees < 1.5 degrees, and the peak is 0.35 degrees < 1.44 / 4 = 0.36 degrees from the center, meeting the termination conditions.

[0092] The overall solution of step 103 above, through the dynamic adaptation of the initial window to the target's maneuverability, the intelligent boundary contraction strategy based on the peak position, and the iterative termination mechanism with dual physical constraints, enables the detection window to accurately converge to the target's true signal area in an environment with sudden target maneuvers and strong clutter interference, effectively overcoming the signal attenuation problem caused by the beam edge effect, and providing reliable guarantee for accurate estimation of the signal-to-noise ratio.

[0093] 104. Extract the signal intensity peak value and the corresponding background noise intensity in the final iteration round, and simultaneously calculate the spatial matching degree between the scanning beam pointing angle sequence and the dynamic position prediction sequence. Convert the spatial matching degree into a beam illumination efficiency factor, and collaboratively calculate the dynamic correction value of the signal-to-noise ratio based on the ratio of the signal intensity peak value and the corresponding background noise intensity.

[0094] Optionally, step 104 may specifically include the following steps:

[0095] 1041. Calculate the difference between the coordinates of the scanning beam central axis pointing point and the coordinates of the target dynamic position prediction point at the same time mark to obtain the beam pointing position deviation;

[0096] Among them, step 1041 may specifically include the following processes: converting the azimuth angle and pitch angle of the central axis of the scanning beam into the beam pointing space coordinates in the three-dimensional space coordinate system; extracting the target prediction space coordinates corresponding to the same time mark from the dynamic position prediction sequence; calculating the spatial straight-line distance between the beam pointing space coordinates and the target prediction space coordinates, and outputting it as the beam pointing position deviation.

[0097] 1042. Generate a beam illumination efficiency factor by mapping according to the magnitude of the beam pointing position deviation, wherein the mapping relationship satisfies that the beam illumination efficiency factor monotonically decreases when the beam pointing position deviation increases;

[0098] 1043. Divide the peak signal intensity in the final iteration round by the corresponding background noise intensity to obtain an original signal-to-noise ratio value, and then multiply the original signal-to-noise ratio value by the beam illumination efficiency factor to generate a dynamic correction value for the signal-to-noise ratio.

[0099] In the above scheme, spatial matching is a quantitative indicator of the degree of spatial overlap between the actual radar beam pointing and the predicted target position. The beam illumination efficiency factor is a factor that characterizes the effectiveness of the beam energy in covering the target. The beam pointing position deviation is the three-dimensional straight-line distance between the beam center point and the predicted target point. The background noise intensity is the average energy value of the interference signal in the area near the target signal.

[0100] In the embodiment of the present application, first, two sets of spatial coordinate data at the same time point are obtained through step 1041: the first set is the actual pointing coordinates of the central axis of the scanning beam, which are converted into spatial point positions in a three-dimensional rectangular coordinate system by the azimuth and elevation angle values ​​recorded by the antenna angle encoder and combined with the geographical location of the radar station; the second set is the target prediction coordinates at that moment in the dynamic position prediction sequence, including distance values ​​and azimuth angle values, which are also converted into spatial points in a three-dimensional rectangular coordinate system). The straight-line distance between the two spatial points is calculated, and the distance value is the beam pointing position deviation. For example, the beam pointing angle (120.5°, 2.1°) is converted to spatial coordinate point A (X=8120, Y=150, Z=300), and the target prediction point (121.3°, 8.2km) is converted to spatial coordinate point B (X=8145, Y=185, Z=305), and the distance between points AB is calculated [(8145-8120) 2 +(185-150) 2 +(305-300) 2 ]^(1 / 2)≈43.3 meters.

[0101] Secondly, a physical mapping model of position deviation and illumination efficiency factor is established through step 1042. This model follows the law of radar beam energy distribution: when the deviation is zero, the efficiency factor is 1, and the center of the beam completely covers the target; when the deviation is equal to the beam half-power point radius, the efficiency factor drops to 0.5, and the target is at the half-attenuation point of the beam energy; when the deviation exceeds the beam width, the efficiency factor approaches 0. The measured deviation is converted into an efficiency factor through a preset discrete mapping table or continuous function relationship. For example, the beam half-power point radius is 150 meters, and the measured deviation is 43.3 meters. The linear mapping rule is used: efficiency factor = 1-deviation / (2×half-power radius) = 1-43.3 / 300≈0.855.

[0102] Finally, step 1043 extracts two key parameters from the final iterative detection window: 1) The maximum value of all signal strength values ​​within the window is taken as the peak signal strength; 2) A background region is defined around the peak location, excluding cells adjacent to the peak, and the arithmetic mean of the signal strength within this region is calculated as the background noise intensity. The peak value is divided by the noise intensity to obtain the original signal-to-noise ratio (SNR), which is then multiplied by the beam illumination efficiency factor output in step 1042 to generate the final dynamic correction for the SNR. For example, if the peak intensity within the detection window is 205 units, the background region is 8.12-8.18 km away (excluding 8.15 km ± 50 meters), and the noise mean is 38 units, the original SNR is 205 / 38, which is approximately 5.39. Multiplying this by the efficiency factor of 0.855 yields a corrected SNR of approximately 4.61.

[0103] In real-world applications, a high-speed drone performs a sharp 90-degree turn amidst densely packed high-rise buildings. The radar beam's half-power radius is 120 meters, and there's strong background interference from building reflections. At step 1041, the current timestamp is 123456.000 milliseconds, and the scan beam's pointing angles are recorded as: 85.7° in azimuth and 3.2° in elevation. Through coordinate conversion: beam pointing point spatial coordinates = (radar station coordinates X0 + 8150 × cos85.7° × cos3.2°, Y0 + 8150 × sin85.7° × cos3.2°, Z0 + 8150 × sin3.2°) calculated as (X = 8150.2, Y = 122.5, Z = 455.3); target dynamic position prediction point: azimuth 86.3°, distance 8.15km, converted to (X = 8140.8, Y = 215.6, Z = 450.1); deviation = [(8150.2 - 8140.8)] 2 +(122.5-215.6) 2 +(455.3-450.1) 2 ]^(1 / 2)≈93.7 meters

[0104] The nonlinear mapping rule is adopted through step 1042: deviation ≤ 60 meters: efficiency factor = 1-(deviation / 120)^2; 60 meters < deviation ≤ 240 meters: efficiency factor = 0.75-0.25×(deviation-60) / 180; the current deviation is 93.7 meters > 60 meters, substitute into the calculation: efficiency factor = 0.75-0.25×(93.7-60) / 180 = 0.75-0.25×0.1872≈0.703.

[0105] The final iteration window parameters after step 1043 are as follows: peak position: azimuth 86.3°, distance 8.15 km; signal strength peak: 218 units, drone reflected signal; background area: distance 8.12-8.18 km, excluding 8.145-8.155 km, a total of 40 distance units; background noise intensity calculation: unit intensity value list: [35, 42, 38...33], including building reflection interference; arithmetic mean = (35+42+...+33) / 40 = 39.6 units; original signal-to-noise ratio = 218 / 39.6≈5.51; dynamic correction amount = 5.51×0.703≈3.87.

[0106] The overall solution of step 104 above effectively compensates for the beam illumination energy loss caused by target maneuvering by accurately quantifying the spatial deviation between the beam pointing and the target position, establishing an efficiency factor mapping model that conforms to physical laws, and collaboratively correcting the original signal-to-noise ratio value. This outputs an optimized signal-to-noise ratio value that is closer to the actual signal strength under strong background interference.

[0107] 105. Perform weighted fusion based on the dynamic correction value of the signal-to-noise ratio, the signal strength peak value, and the corresponding background noise intensity, and output the final signal-to-noise ratio value of the maneuvering target in the current scanning cycle in real time.

[0108] Optionally, step 105 may specifically include the following steps:

[0109] 1051. Assign a first fusion weight value to the signal strength peak, assign a second fusion weight value to the corresponding background noise intensity, and assign a third fusion weight value to the dynamic correction value of the signal-to-noise ratio, where the sum of the fusion weight values ​​is a constant.

[0110] 1052. Multiply the signal intensity peak by the first fusion weight value to form a main component, multiply the corresponding background noise intensity by the second fusion weight value to form a noise component, and multiply the signal-to-noise ratio dynamic correction amount by the third fusion weight value to form a correction component.

[0111] 1053. Perform a superposition calculation on the main component, the noise component, and the correction component, and output a final signal-to-noise ratio value of the maneuvering target in the current scanning cycle.

[0112] In the above scheme, the first fusion weight value is the core importance coefficient assigned to the signal strength peak in the final calculation. The second fusion weight value is the interference suppression coefficient assigned to the background noise intensity in the final calculation. The third fusion weight value is the spatial compensation coefficient assigned to the dynamic correction value of the signal-to-noise ratio in the final calculation. The main component is the core signal representation value of the signal strength peak after weight scaling. The noise component is the environmental interference representation value of the background noise intensity after weight scaling. The correction component is the beam error compensation value of the dynamic correction value of the signal-to-noise ratio after weight scaling.

[0113] In the embodiment of the present application, first, step 1051 is performed to preset a weight distribution rule based on the target type and environmental characteristics: the signal strength peak is assigned the highest weight as the direct observation value, the background noise intensity is assigned an inhibitory weight as the interference term, and the dynamic correction value of the signal-to-noise ratio is assigned a balancing weight as the spatial compensation term. The sum of the three weights is strictly maintained at 1. The specific weight values ​​are dynamically adjusted according to the radar operating mode: the correction weight is increased when the target is highly maneuverable, and the noise weight is reduced when the background interference is large. For example, for a high-speed target in a complex background, the peak weight is set to 0.6, the noise weight is set to 0.15, and the correction weight is set to 0.25.

[0114] Next, step 1052 performs a weighted calculation: the peak signal strength value is multiplied by the first weight to produce the primary component, which carries the target core energy information; the background noise intensity value is multiplied by the second weight to produce the noise component, which quantifies the intensity of environmental interference; and the dynamic signal-to-noise ratio correction value is multiplied by the third weight to produce the correction component, which reflects the beamspace compensation value. These three components are calculated independently but maintain the same dimension. For example, the peak value is 205 units × 0.6 = 123, the noise value is 38 units × 0.15 = 5.7, and the correction value is 3.87 × 0.25 = 0.9675.

[0115] Finally, in step 1053, the three components are algebraically fused: the noise component is subtracted from the primary component to suppress environmental interference, and the correction component is added to incorporate spatial compensation. The final output is a physically complete signal-to-noise ratio value. This calculation is completed before the end of the scan cycle to ensure real-time performance. For example: 123 (primary component) - 5.7 (noise component) + 0.9675 (correction component) = 118.2675, which outputs 118.3.

[0116] In practical applications, in a UAV ultra-low-altitude penetration scenario, as shown in step 1051, during the current scan cycle, a high-speed UAV climbs rapidly against a background of strong sea clutter. The signal strength peak of 218 units reflects true target energy but fluctuates. The background noise intensity of 39.6 units indicates strong sea surface reflection interference. The dynamic signal-to-noise ratio correction of 3.87 indicates a slight beam deviation. According to the maneuvering target weighting rule: the peak is assigned a base confidence weight of 0.65, the noise is assigned a suppression weight of 0.15 due to strong interference, and the correction is assigned a compensation weight of 0.20 due to the target's climbing motion. The sum of 0.65 + 0.15 + 0.20 = 1.00 verifies that it meets the constant requirement.

[0117] Through step 1052, the main component calculation takes the peak value 218 units multiplied by the first weight 0.65 to obtain 141.70 target core energy; the noise component calculation takes the background noise 39.6 units multiplied by the second weight 0.15 to obtain 5.94 quantized sea clutter interference; the correction component calculation takes the dynamic correction amount 3.87 multiplied by the third weight 0.20 to obtain 0.774 reflecting the beam space compensation value, and the dimensions of the three components are unified into dimensionless values.

[0118] In step 1053, the fusion calculation uses the formula "primary component minus noise component plus correction component": 141.70 - 5.94 = 135.76 to eliminate environmental interference. 135.76 + 0.774 = 136.534 is then incorporated to account for spatial attenuation. After rounding, the final signal-to-noise ratio (SNR) value of 136.5 is output to the radar tracking system in real time at the end of the scan cycle. The original peak SNR of 218 / 39.6 = 5.51 is overestimated due to not accounting for spatial attenuation. Even with the dynamic correction of 3.87, it is still low. After weighted fusion, the output is 136.5, equivalent to a SNR of 13.65, which more closely matches the target's true reflection characteristics and achieves optimal balance under target maneuvers and strong interference.

[0119] The overall solution of step 105 above, through a triple weight dynamic allocation mechanism, component independent weighted calculation and algebraic fusion optimization, effectively suppresses background interference while retaining the core signal characteristics of the target, accurately incorporates beam space compensation, and outputs a final signal-to-noise ratio value with clear physical meaning and strong environmental adaptability, significantly improving the reliability of parameter estimation of high-speed maneuvering targets in complex scenarios.

[0120] The following is a complete embodiment of steps 101 to 105:

[0121] like Figure 2As shown, in a UAV canyon penetration scenario, step 101 is executed: the radar system extracts 10 consecutive historical trajectory points of the target during the scanning interval, with timestamp sequence T1-T10, corresponding to the azimuth angle sequence [85.1°, 85.3°, 85.8°...86.7°] and the distance sequence [8.15km, 8.17km, 8.21km...8.35km]. Analysis of the trajectory points reveals that the azimuth angle changes from 0.2° to 0.9° and the distance increment increases from 20 meters to 80 meters within a time interval of 0.1 seconds, indicating that the target is performing an accelerated turning maneuver. A nonlinear motion model is established: azimuth angle change rate = 0.5 × t 2 +0.3×t, t is the time increment, and the distance change rate = 4×t+0.2×t 2 Taking T10 as the starting point, three prediction points within 0.1 seconds of the next cycle are deduced: t = 0.033 seconds → azimuth 87.2°, distance 8.38 km; t = 0.067 seconds → azimuth 87.9°, distance 8.42 km; t = 0.1 seconds → azimuth 88.6°, distance 8.47 km, generating a dynamic position prediction sequence.

[0122] Execute step 102: The mechanical scanning antenna rotates at a constant speed of 6 revolutions per second, triggering pulse emission every 0.1° in azimuth. When the target turns to 87.5° in azimuth, a Ku-band pulse with a pulse width of 0.5 μs is emitted. The receiver synchronously collects the reflected signal and divides it into 800 range cells at 0.1 μs intervals, corresponding to a 15-meter range resolution. A two-dimensional data table is generated for 87.5° in azimuth: Cell 1 (0-15m) has an intensity of 32... Cell 553 (8.295km) has an intensity of 185... Cell 800 (12km) has an intensity of 28. The peak intensity of 185 units, or 553 cells, is extracted from the main lobe region in azimuths 87.3°-87.7° and at a range of 8.25-8.35km. The current pointing angle of 87.5° / 2.1° in elevation is recorded and added to the sequence.

[0123] Execute step 103: For the predicted point t = 0.067 seconds, azimuth 87.9° / distance 8.42 km, set an initial detection window with a side length covering the maximum maneuvering range: ±6° in azimuth and ±0.6 km in distance. The initial detection window range is: azimuth 84.9°-90.9°, distance 7.82-9.02 km. Analyze the echo sequence within the window and find the peak point azimuth 88.7° / distance 8.38 km. The edge is determined to be 1.2° < 6 / 5 = 1.2°. The edge is contracted by 10% from the center (87.9°) to the peak (88.7°), reducing the window length to 10.8°. After four rounds of iterations: in the third round, when the window length was 2.5°, the peak distance from the center was 0.6°>2.5 / 4=0.625°, and it continued to shrink; in the fourth round, the window length was 1.9°<beam half-power width 2.1° and the peak distance from the center was 0.4°<1.9 / 4=0.475°, and the final window was output, with an azimuth of 87.5°-88.3° and a distance of 8.32-8.48km.

[0124] Execute step 104: Finally, extract the signal peak value of 205 units in the window, with the azimuth of 88.1° and the distance of 8.41km. The background noise is taken from the adjacent units, and the peak point between 8.38-8.44km is excluded. The average value is 42 units. Calculate the spatial matching degree: the beam pointing at the current moment is 87.5° / 2.1° → spatial coordinates (8145,135,452), the predicted point is 87.9° / 8.42km → (8148,218,449), and the deviation is [(8145-8148)]. 2 +(135-218) 2 +(452-449) 2 ]^(1 / 2)≈83.1 meters. Mapping beam illumination efficiency factor: Beam half-power radius 120 meters, deviation 83.1 meters → factor = 0.75-0.25×(83.1-60) / 180≈0.72. Original signal-to-noise ratio = 205 / 42≈4.88, dynamic correction = 4.88×0.72≈3.51.

[0125] Step 105 assigns weights: peak weight 0.65 for high confidence, noise weight 0.15 for strong suppression, and correction weight 0.20 for compensation. Calculated components: Main component = 205 × 0.65 = 133.25; Noise component = 42 × 0.15 = 6.3; Correction component = 3.51 × 0.20 = 0.702. Fusion output: 133.25 - 6.3 + 0.702 = 127.652, resulting in a final signal-to-noise ratio of 127.7.

[0126] This solution uses a fifth-order collaborative mechanism to optimize dynamic position prediction driven by trajectory data, precise spatiotemporal synchronization of mechanical scanning and signal acquisition, adaptive contraction and locking of iterative detection windows, physical correction of beam spatial matching, and multi-parameter weighted fusion optimization. It significantly overcomes the signal-to-noise ratio estimation distortion problem of traditional methods in the beam edge area under scenarios of sudden target maneuvers and strong terrain clutter interference, achieves stable tracking of high-speed small targets throughout the entire process, and enhances the radar system's target characteristic identification capability in complex electromagnetic environments.

[0127] Figure 3 The present invention provides a schematic diagram of a real-time estimation system for adaptive signal-to-noise ratio of a small target radar. Figure 3 As shown, the system includes:

[0128] The scanning module 31 is used to establish a motion feature model based on the continuous historical trajectory points of the maneuvering target during the radar scanning interval and generate a dynamic position prediction sequence of the target in the next scanning cycle;

[0129] A recording module 32 is configured to acquire radar scanning data packets at a fixed rotation speed through a mechanical rotating scanning device, and synchronously record a scanning beam pointing angle sequence and a corresponding time stamp echo signal strength sequence, wherein the scanning beam pointing angle sequence includes the azimuth and elevation angles of the beam center axis;

[0130] A detection module 33 is configured to initiate multiple rounds of iterative detection within the spatial neighborhood of each target position point in the dynamic position prediction sequence. The first round of detection uses an initial detection window that covers the target's maximum maneuvering range. In subsequent rounds, the detection window range is bidirectionally contracted in the range and azimuth directions based on the distribution characteristics of the echo signal strength sequence within the first round of detection window until the detection window boundary is less than the half-power point width of the scanning beam.

[0131] A calculation module 34 is configured to extract the signal intensity peak value and the corresponding background noise intensity in the final iteration round, calculate the spatial matching degree between the scanning beam pointing angle sequence and the dynamic position prediction sequence, convert the spatial matching degree into a beam illumination efficiency factor, and calculate a dynamic correction value for the signal-to-noise ratio in conjunction with the ratio of the signal intensity peak value to the corresponding background noise intensity;

[0132] The output module 35 is used to perform weighted fusion based on the dynamic correction amount of the signal-to-noise ratio, the signal intensity peak value and the corresponding background noise intensity, and output the final signal-to-noise ratio value of the maneuvering target in the current scanning cycle in real time.

[0133] Figure 3 The real-time estimation system of the adaptive signal-to-noise ratio of the small target radar can be performed Figure 1The implementation principles and technical effects of the method for real-time estimation of the adaptive signal-to-noise ratio of a small target radar described in the illustrated embodiment will not be elaborated upon. The specific manner in which the various modules and units in the real-time estimation system for the adaptive signal-to-noise ratio of a small target radar in the aforementioned embodiment perform their operations has been described in detail in the related embodiments of the method and will not be further elaborated upon here.

[0134] In one possible design, Figure 3 The embodiment of the small target radar adaptive signal-to-noise ratio real-time estimation system can be implemented as a computing device, such as Figure 4 As shown, the computing device may include a storage component 41 and a processing component 42;

[0135] The storage component 41 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 42 .

[0136] The processing component 42 is used for the above Figure 1 The embodiment provides a real-time estimation method for the adaptive signal-to-noise ratio of a small target radar.

[0137] The processing component 42 may include one or more processors to execute computer instructions to perform all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0138] The storage component 41 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0139] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0140] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0141] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0142] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0143] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The embodiment shown is a real-time estimation method for adaptive signal-to-noise ratio of a small target radar.

[0144] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0145] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0146] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by means of hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A real-time estimation method for adaptive signal-to-noise ratio of small target radar, characterized in that: include: During the radar scanning interval, a motion feature model is established based on the continuous historical trajectory points of the maneuvering target to generate a dynamic position prediction sequence of the target in the next scanning cycle; A radar scanning data packet is acquired at a fixed rotation speed by a mechanical rotating scanning device, and a scanning beam pointing angle sequence and a corresponding time stamp echo signal strength sequence are synchronously recorded. The scanning beam pointing angle sequence includes the azimuth and elevation angles of the beam center axis. For each target position point in the dynamic position prediction sequence, multiple rounds of iterative detection are initiated within its spatial neighborhood. The first round of detection uses an initial detection window that covers the target's maximum maneuvering range. In subsequent rounds, the detection window range is bidirectionally contracted in the range and azimuth directions based on the distribution characteristics of the echo signal strength sequence within the preceding detection window, until the detection window boundary is smaller than the half-power point width of the scanning beam. Extracting the signal intensity peak and the corresponding background noise intensity in the final iteration round, calculating the spatial matching degree between the scanning beam pointing angle sequence and the dynamic position prediction sequence, converting the spatial matching degree into a beam illumination efficiency factor, and collaboratively calculating the dynamic correction value of the signal-to-noise ratio based on the ratio of the signal intensity peak and the corresponding background noise intensity; Based on the dynamic correction amount of the signal-to-noise ratio, the signal strength peak value and the corresponding background noise intensity, weighted fusion is performed to output the final signal-to-noise ratio value of the maneuvering target in the current scanning cycle in real time.

2. The method according to claim 1, characterized in that For each target position point in the dynamic position prediction sequence, multiple rounds of iterative detection are initiated within the spatial neighborhood of the target point, wherein the first round of detection uses an initial detection window covering the maximum maneuvering range of the target, and subsequent rounds of detection shrink the detection window range in both the range and azimuth directions according to the distribution characteristics of the echo signal strength sequence within the first round of detection window until the detection window boundary is less than the half-power point width of the scanning beam, including: Taking each target position point in the dynamic position prediction sequence as the center point, a square initial detection window is formed by expanding in the range and azimuth directions, wherein the side length of the square initial detection window is set to cover the maximum maneuvering offset range of the target; Analyzing the intensity distribution of the echo signal intensity sequence within the boundary of the square initial detection window, and when detecting that an intensity peak point of the intensity distribution state is located in an edge area of ​​the square initial detection window, shrinking the boundary in proportion along the direction of the intensity peak point; The detection window shrinking operation is repeatedly performed until the boundary range of the square initial detection window is smaller than the half-power point width of the scanning beam and the intensity peak point is in the central area of ​​the square initial detection window, and it is determined that the iteration termination condition is met.

3. The method according to claim 1, characterized in that The method extracts the signal intensity peak value and the corresponding background noise intensity in the final iteration round, calculates the spatial matching degree between the scanning beam pointing angle sequence and the dynamic position prediction sequence, converts the spatial matching degree into a beam illumination efficiency factor, and collaboratively calculates the dynamic correction value of the signal-to-noise ratio based on the ratio of the signal intensity peak value and the corresponding background noise intensity, including: The difference between the coordinates of the scanning beam center axis pointing point and the target dynamic position prediction point at the same time mark is calculated to obtain the beam pointing position deviation; Generate a beam illumination efficiency factor according to the magnitude of the beam pointing position deviation, wherein the mapping relationship satisfies that the beam illumination efficiency factor monotonically decreases when the beam pointing position deviation increases; The signal intensity peak value in the final iteration round is divided by the corresponding background noise intensity to obtain the original signal-to-noise ratio value, and then multiplied by the beam illumination efficiency factor to generate the dynamic correction value of the signal-to-noise ratio.

4. The method according to claim 1, wherein The radar scanning data packet is obtained at a fixed rotation speed by a mechanical rotating scanning device, and a scanning beam pointing angle sequence and an echo signal strength sequence corresponding to a timestamp are synchronously recorded. The scanning beam pointing angle sequence includes the azimuth angle and the pitch angle of the beam center axis, including: When the mechanical rotary scanning device rotates at a constant angular velocity, whenever the azimuth angle of the central axis of the scanning beam reaches a preset angle sampling position, the radar transmitting unit is triggered to send a detection pulse signal and synchronously receive the target reflection signal; The target reflection signal sampled at each azimuth angle is divided into time dimensions, and the echo intensity values ​​of all range units within the same time slice are combined into two-dimensional distribution data of azimuth and range; The maximum signal intensity within the main lobe coverage area of ​​the scanning beam and its corresponding range unit coordinates are extracted from the azimuth and range two-dimensional distribution data, and an echo signal intensity sequence is generated in the order of timestamps. At the same time, the azimuth and elevation angle values ​​of the central axis of the scanning beam at each sampling point are recorded to form a scanning beam pointing angle sequence.

5. The method according to claim 1, wherein The weighted fusion based on the dynamic correction amount of the signal-to-noise ratio, the signal strength peak value and the corresponding background noise intensity is output in real time as a final signal-to-noise ratio value of the maneuvering target in the current scanning cycle, including: Assigning a first fusion weight value to the signal strength peak, assigning a second fusion weight value to the corresponding background noise intensity, and assigning a third fusion weight value to the dynamic correction amount of the signal-to-noise ratio, wherein the sum of the fusion weight values ​​is a constant; Multiplying the signal intensity peak by the first fusion weight value to form a main component, multiplying the corresponding background noise intensity by the second fusion weight value to form a noise component, and multiplying the signal-to-noise ratio dynamic correction amount by the third fusion weight value to form a correction component; A superposition calculation is performed on the main component, the noise component, and the correction component, and a final signal-to-noise ratio value of the maneuvering target in the current scanning cycle is output.

6. The method according to claim 2, characterized in that The step of analyzing the intensity distribution of the echo signal intensity sequence within the boundary of the square initial detection window and proportionally shrinking the boundary in the direction of the intensity peak when an intensity peak of the intensity distribution is detected to be located at an edge of the square initial detection window comprises: Dividing an edge region in the square initial detection window, wherein the edge region is defined as a narrow edge region adjacent to the detection window boundary; Locating a spatial position point corresponding to a maximum signal intensity within a current detection window in the echo signal intensity sequence, and if the spatial position point falls within the narrow edge area, determining that the intensity peak point is in an edge position state; The detection window boundary is moved toward the detection window center point along the line connecting the intensity peak point at the edge position and the detection window center point by a preset shrinking distance to form a new shrinking detection window boundary range.

7. The method according to claim 3, characterized in that The difference calculation between the scanning beam central axis pointing point coordinates and the target dynamic position prediction point coordinates at the same time mark to obtain the beam pointing position deviation includes: Converting the azimuth angle and the elevation angle of the central axis of the scanning beam into beam pointing space coordinates in a three-dimensional space coordinate system; Extracting target prediction spatial coordinates corresponding to the same time stamp from the dynamic position prediction sequence; The spatial straight-line distance between the beam pointing spatial coordinates and the target prediction spatial coordinates is calculated, and the output is the beam pointing position deviation.

8. A real-time estimation system for adaptive signal-to-noise ratio of small target radar, characterized in that: include: The scanning module is used to establish a motion feature model based on the continuous historical trajectory points of the maneuvering target during the radar scanning interval and generate a dynamic position prediction sequence of the target in the next scanning cycle; A recording module is configured to acquire radar scanning data packets at a fixed rotation speed through a mechanical rotating scanning device, and synchronously record a scanning beam pointing angle sequence and a corresponding time stamp echo signal strength sequence, wherein the scanning beam pointing angle sequence includes the azimuth and elevation angles of the beam center axis; a detection module configured to initiate multiple rounds of iterative detection within a spatial neighborhood of each target position point in the dynamic position prediction sequence, wherein the first round of detection uses an initial detection window covering the target's maximum maneuvering range, and in subsequent rounds, the detection window range is bidirectionally contracted in range and azimuth directions based on the distribution characteristics of the echo signal strength sequence within the first round of detection window until the detection window boundary is less than the half-power point width of the scanning beam; a calculation module for extracting the signal intensity peak and the corresponding background noise intensity in the final iteration round, calculating the spatial matching degree between the scanning beam pointing angle sequence and the dynamic position prediction sequence, converting the spatial matching degree into a beam illumination efficiency factor, and collaboratively calculating a dynamic correction value for the signal-to-noise ratio based on the ratio of the signal intensity peak and the corresponding background noise intensity; The output module is used to perform weighted fusion based on the dynamic correction amount of the signal-to-noise ratio, the signal strength peak value and the corresponding background noise intensity, and output the final signal-to-noise ratio value of the maneuvering target in the current scanning cycle in real time.

9. A computing device, characterized in that The method comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a real-time estimation method for the adaptive signal-to-noise ratio of a small target radar as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the real-time estimation method for the adaptive signal-to-noise ratio of a small target radar according to any one of claims 1 to 7 is implemented.

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