Moving target detection and clutter suppression integrated method and system for low-altitude security

Abstract

The invention discloses a moving target detection and clutter suppression integrated method and system for low-altitude security and protection, and relates to the technical field of electrical digital data processing, and the method comprises the steps: obtaining a radar echo in a to-be-processed period, obtaining a clutter Doppler spectrum width in the radar echo, and selecting a target filter according to the clutter Doppler spectrum width, processing the radar echo according to a target filter and outputting a processed slow time signal; performing plot screening on the processed slow time signal, screening out a plot to be processed, and converting the screened plot to be processed from a polar coordinate system to a rectangular coordinate system; performing correlation constraint on the trace points to be processed in the rectangular coordinate system and forming a plurality of correlation trace point aggregation; and acquiring a space coordinate and a movement speed of each associated trace point aggregation. The method has the advantages of adaptive selection, high positioning precision and combined constraint.

Description

Technical Field

[0001] This invention relates to the field of electronic digital data processing technology, specifically to an integrated method and system for moving target detection and clutter suppression for low-altitude security. Background Technology

[0002] In the field of low-altitude security radar detection, especially in complex terrain environments with high-intensity clutter and variable Doppler bandwidth, and in scenarios where effective monitoring of low-altitude slow-moving small targets such as drones is required, existing moving target detection methods face some challenges.

[0003] Specifically, due to the influence of surface obstacles, wind, and non-uniform meteorological environments in low-altitude areas, the Doppler spectrum of ground clutter and sea clutter is severely broadened and dynamically changes. Existing static filters struggle to achieve the optimal balance between clutter suppression depth and target fidelity. Insufficient clutter suppression can overwhelm weak target signals, while excessive suppression can filter out slow-moving targets with specific Doppler characteristics, reducing the detection probability. Furthermore, the point extraction stage lacks a refined screening mechanism for potential points in the slow-time radar echo signal, easily misjudging clutter residues or noise peaks as targets, introducing a large number of false tracks to interfere with subsequent processing. Moreover, point data extracted solely from range and Doppler information does not effectively address the three-dimensional spatial mapping error of low-altitude targets during the polar-to-rectangular coordinate conversion process. In particular, the range positioning deviation caused by terrain elevation undulations further reduces the accuracy of the target's initial spatial coordinates. Additionally, existing methods lack constraint criteria adapted to the characteristics of slow-moving low-altitude targets in the point association stage, failing to effectively distinguish between continuous trajectory segments of real targets and isolated points formed by clutter. Ultimately, this results in insufficient detection probability, positioning accuracy, and continuous stable tracking capability for slow-moving small targets under complex low-altitude clutter interference. Summary of the Invention

[0004] To address the technical problems mentioned above, this invention provides an integrated method and system for moving target detection and clutter suppression for low-altitude security.

[0005] An integrated method for moving target detection and clutter suppression for low-altitude security includes: acquiring radar echoes within a processing period and obtaining the clutter Doppler spectral width in the radar echoes; selecting a target filter based on the clutter Doppler spectral width; processing the radar echoes according to the target filter and outputting the processed slow-time signal; performing point screening on the processed slow-time signal and selecting points to be processed, and transforming the selected points to be processed from the polar coordinate system to the rectangular coordinate system; applying correlation constraints to the points to be processed in the rectangular coordinate system and forming multiple correlated point aggregations; and acquiring the spatial coordinates and motion velocity of each correlated point aggregation.

[0006] Optionally, the target filter type can be selected based on the clutter Doppler spectral width: when the clutter Doppler spectral width is ≤50Hz, a three-pulse canceller is used as the target filter for clutter suppression; when the clutter Doppler spectral width is >50Hz, a five-pulse canceller is used as the target filter for clutter suppression.

[0007] Optionally, processing the radar echo according to the target filter and outputting the processed slow-time signal includes: applying the selected target filter to perform inter-pulse cancellation processing on the radar echo to suppress clutter components and retain moving target information; and outputting a slow-time signal after clutter suppression that contains potential traces corresponding to the moving target.

[0008] Optionally, the process of filtering and selecting traces to be processed from the processed slow-time signal includes: extracting multiple potential traces from the processed slow-time signal and obtaining distance data, velocity data, and amplitude data for each potential trace; retaining initial traces whose distance data is less than a preset distance and whose velocity data is less than a preset velocity, and forming multiple first-level traces; retaining first-level traces whose amplitude data is less than a preset multiple of the average clutter amplitude, and forming multiple second-level traces, and using the second-level traces as traces to be processed.

[0009] Optionally, constraining the points to be processed in the Cartesian coordinate system and forming multiple associated point aggregates includes: if the absolute difference of the distance data between any i-th point and j-th point to be processed is less than one distance resolution unit and the absolute difference of the velocity data is less than one velocity resolution unit, then the i-th point and j-th point to be processed are associated and form an associated point aggregate.

[0010] Optionally, obtaining the spatial coordinates and motion speed of each associated point cluster includes: obtaining the weighted centroid of each associated point cluster and using the spatial coordinates of the weighted centroid as the spatial coordinates of each associated point cluster; and using the average value of the velocity data of all unprocessed points in each associated point cluster as the motion speed of each associated point cluster.

[0011] A moving target detection and clutter suppression integrated system for low-altitude security is also provided. The system includes: a clutter suppression module, used to acquire radar echoes within the processing period and obtain the clutter Doppler spectral width in the radar echoes, select a target filter based on the clutter Doppler spectral width, process the radar echoes according to the target filter, and output the processed slow-time signal; a moving target trace preprocessing module, used to filter traces in the processed slow-time signal and select traces to be processed, and transform the selected traces to be processed from the polar coordinate system to the rectangular coordinate system; a moving target trace aggregation module, used to perform correlation constraints on the traces to be processed in the rectangular coordinate system and form multiple correlated trace aggregations; and a moving target trace detection output module, used to acquire the spatial coordinates and motion velocity of each correlated trace aggregation.

[0012] Optionally, the clutter suppression module is also used to: suppress clutter by using a three-pulse canceller as the target filter when the clutter Doppler spectral width is ≤50Hz; and suppress clutter by using a five-pulse canceller as the target filter when the clutter Doppler spectral width is >50Hz.

[0013] Optionally, the clutter suppression module is also used to: apply a selected target filter to perform inter-pulse cancellation processing on the radar echo, suppress clutter components and retain moving target information; and output a slow-time signal that has been clutter suppressed and contains potential traces corresponding to the moving target.

[0014] Optionally, the moving target trace preprocessing module is also used to: extract multiple potential traces from the processed slow-time signal, and obtain the distance data, velocity data and amplitude data of each potential trace; retain the initial traces whose distance data is less than a preset distance and whose velocity data is less than a preset velocity, and form multiple first-level traces; retain the first-level traces whose amplitude data is less than a preset multiple of the average clutter amplitude, and form multiple second-level traces, and use the second-level traces as traces to be processed.

[0015] The beneficial effects of this invention are reflected in: In the integrated moving target detection and clutter suppression method for low-altitude security, firstly, by adaptively selecting narrow or wide notch cancellers based on the real-time clutter Doppler spectrum width, the Doppler characteristics of low-speed targets are preserved to the maximum extent while ensuring deep suppression of high-intensity clutter, thus improving the retention rate of weak targets. Furthermore, through a two-level spot selection mechanism that includes physical constraints (near-range and low-speed thresholds) and statistical tests (amplitude below a preset multiple of the clutter mean), residual clutter, noise peaks, and long-range / high-speed interference are effectively filtered out, reducing false alarms. Additionally, a three-dimensional spatial correction is performed on the conversion from polar coordinates to rectangular coordinates to eliminate distance positioning errors caused by changes in ground altitude, improving the initial positioning accuracy of the spot. To further improve positioning accuracy, a dual strong constraint correlation condition based on spatial proximity (range difference less than one range resolution cell) and velocity consistency (velocity difference less than one velocity resolution cell) is proposed. This ensures that neighboring points of the same target formed by radar resolution ambiguity or fluctuations within a single cycle are accurately aggregated, while eliminating spatially isolated or velocity-abrupt clutter points, thus improving the reliability of target aggregation. Finally, high-precision position output is obtained through amplitude-weighted spatial centroid calculation, and the positioning robustness is improved by utilizing the weight of high signal-to-noise ratio points. Average velocity calculation is used for aggregated points with highly consistent velocities to efficiently output target velocity. Ultimately, accurate detection and spatial positioning of low, slow, and small targets are achieved under complex terrain with strong clutter. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a schematic diagram of the steps of the integrated moving target detection and clutter suppression method for low-altitude security according to the present invention; Figure 2 This is a schematic diagram of part of step S1 in the integrated moving target detection and clutter suppression method for low-altitude security of the present invention; Figure 3 This is a schematic diagram of another part of the steps in S1 of the integrated moving target detection and clutter suppression method for low-altitude security of the present invention; Figure 4 This is a schematic diagram of part of step S2 in the integrated moving target detection and clutter suppression method for low-altitude security of the present invention; Figure 5 This is a schematic diagram of part of step S4 in the integrated method for moving target detection and clutter suppression for low-altitude security of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] like Figure 1 As shown, an integrated method for moving target detection and clutter suppression for low-altitude security is provided. In one embodiment, the method includes: S1. Obtain the radar echo within the processing period and obtain the clutter Doppler spectral width in the radar echo. Select the target filter according to the clutter Doppler spectral width, process the radar echo according to the target filter, and output the processed slow-time signal. S2. Perform point screening on the processed slow-time signal and select the points to be processed, and transform the selected points to be processed from the polar coordinate system to the rectangular coordinate system. S3. Apply association constraints to the points to be processed in the rectangular coordinate system and form multiple associated point aggregates; S4. Obtain the spatial coordinates and movement speed of each associated point.

[0022] In this embodiment, it should be noted that in step S1, in the low-altitude security radar application scenario, the core objective of step S1 is to achieve dynamic adaptive suppression of clutter, laying the foundation for subsequent detection of real moving targets. This step begins with acquiring raw radar echo data within a specific processing time window. The radar transmits a series of pulse signals and receives echoes reflected from the target and the environment; these echoes are sampled and stored according to distance. Then, the Doppler spectral width of the clutter in the environment is estimated from these raw echoes. The Doppler spectral width is a key indicator for measuring the range of clutter energy distribution on the Doppler frequency axis. Its magnitude directly reflects the intensity and consistency of the motion of scattering bodies in the environment (such as swaying trees, undulating waves, or surface aeolian objects). A larger spectral width means that the clutter covers a wider area in the frequency domain and has a stronger interference effect.

[0023] Furthermore, based on the calculated clutter Doppler spectral width, a suitable target filter type is dynamically selected. The main purpose of the target filter is to suppress clutter while preserving as much of the possible moving target signal as possible. The selection is based on the fact that different filter designs have different characteristics in terms of the width of their suppression notch (i.e., the range of Doppler frequencies that can be effectively filtered out). For clutter environments with a narrow spectral width (such as calm ground clutter), a filter with a relatively narrow suppression notch (such as a three-pulse canceller) can effectively filter out most of the clutter energy, avoiding unnecessary suppression of moving targets (especially slow-moving targets) with adjacent Doppler frequencies. Conversely, for clutter environments with a significantly broadened spectral width (such as vegetation in strong winds or rough sea surfaces), a filter with a wider suppression notch (such as a five-pulse canceller) is required to cope with the large range of clutter energy distribution and ensure sufficient clutter suppression depth.

[0024] Furthermore, after selecting the target filter, it is applied to the inter-pulse processing of the aforementioned original radar echo. The core mechanism of this processing is to utilize the differences in phase and amplitude correlation between adjacent pulse echoes: clutter typically has high temporal correlation (i.e., adjacent pulse echoes change gradually), while moving targets cause significant inter-pulse phase changes (i.e., Doppler shift) due to their radial velocity. The target filter, by performing cancellation processing (such as pulse cancellation operation), essentially calculates the difference (increment) between adjacent pulse echoes. This calculation can significantly weaken or eliminate highly correlated signal components corresponding to strong clutter; while the echo of a moving target, because it is usually offset from the clutter center frequency at the Doppler frequency and its inter-pulse phase change is different from that of clutter, is relatively preserved or enhanced in this differential operation.

[0025] Furthermore, the processed output is a slow-time signal with clutter suppression. This signal corresponds to each individual range gate and is represented as a complex sequence (e.g., a 1×N vector) along the slow-time axis (i.e., arranged in the order of pulse emission). Each element in the sequence represents the amplitude and phase information (or simplified as amplitude value) of the echo received by that range gate at that specific pulse emission time. The key point is that the output signal mainly contains two types of components: first, residual noise that cannot be completely eliminated by the filter; and second, the potential moving target response signals that are highlighted or retained after filter processing—these responses are the source of the "traces" (potential points) for subsequent detection of possible targets. For each range gate output, this slow time series serves as preparation for further analysis. Key initial screening information about potential targets at that location can be obtained by analyzing the series: multiplying the range gate number by the radar's range resolution allows estimation of the possible distance to the target; spectral analysis (such as FFT) of the slow time series reveals its primary frequency domain energy distribution, thus estimating its velocity (since Doppler frequency is proportional to radial velocity); simultaneously, statistical analysis of the amplitude values ​​in the series (e.g., calculating the maximum value) yields the amplitude intensity (or signal-to-noise ratio) characterizing the target signal relative to the background noise level. This slow-time signal matrix, containing noise and target tracking information, is the final output of step S1, which is fed to step S2 for fine-tuning of target selection and coordinate transformation.

[0026] In S2, a refined target screening process is implemented in a signal environment containing residual noise and potential moving target traces to reduce false alarm rates and improve the reliability of subsequent processing. The screening process first extracts all potential candidate traces from the slow-time signals of all range gates output from S1 by analyzing the slow-time series of each range gate. For each candidate trace, three key parameters are extracted: range data (calculated by multiplying the specific range gate number of the trace by the radar's inherent range resolution, determining the target's radial distance relative to the radar), velocity data (obtained by performing spectral analysis (such as FFT) on the slow-time series of the range gate to obtain the Doppler frequency corresponding to its main energy, and then converting it according to the radar wavelength to obtain the target's radial velocity), and amplitude data (usually measured by the maximum value of the signal amplitude within the slow-time series or a specific statistic, reflecting the level of the trace's signal strength relative to background noise, often referred to as the trace signal-to-noise ratio). This extraction process essentially discretizes the continuous slow-time signal in the range and Doppler dimensions, forming a series of independent candidate events carrying target information.

[0027] Furthermore, after obtaining all potential targets and their distance, velocity, and amplitude data, S2 implements a multi-level threshold filtering mechanism for screening. The first level of filtering is based on physical constraints: considering that the main focus of low-altitude security is on threats that are relatively close to the radar and have low speeds (such as low-altitude, slow, small drones), only those initial targets with distance data less than a certain preset distance limit (e.g., setting the coverage detection area range) and velocity data less than a certain preset speed limit (e.g., excluding high-speed aircraft or birds) are retained. This step significantly filters out long-range or high-speed interference, but the retained first-level targets may still contain residual low-altitude clutter, noise spikes, and potential slow-moving targets.

[0028] The second-level filtering is based on statistical significance testing: It calculates the average background clutter amplitude level (i.e., the mean clutter amplitude) of all first-level traces (or specific areas) within the current processing period, and then retains only those first-level traces whose amplitude data is less than a preset multiple (commonly three times) of this mean clutter amplitude, forming the final second-level traces, i.e., the traces to be processed. This step aims to filter out strong signals that significantly exceed the average noise / clutter level, retaining signal points that are more likely to be associated with real, weak targets.

[0029] Finally, a crucial coordinate system transformation is performed on these carefully selected points to be processed: since the original radar measurements are expressed in polar coordinates (range, azimuth, elevation), they need to be accurately transformed to a Cartesian coordinate system (X, Y, Z, height) used for target tracking and display. This transformation process specifically incorporates a terrain elevation database to correct for errors between the actual target height and the radar measurement height caused by ground undulations. For example, in mountainous areas or areas with buildings, the radar beam may be obstructed, or the height point corresponding to its measured elevation angle may be below the ground plane or unreasonable (such as penetrating a slope). Elevation data can compensate for these errors, accurately mapping the azimuth and measurement distance of the point, combined with elevation model data, to the correct horizontal position (X, Y) and height (Z) in three-dimensional space, thus improving the initial positioning accuracy of the point in three-dimensional space.

[0030] In S3, the unprocessed point traces (i.e., the point traces output from S2, now located in the terrain-corrected Cartesian coordinate system) undergo refined screening and precise spatial correction. An association constraint based on the consistency criteria of spatial location and motion velocity is applied to these discrete point traces, aiming to aggregate them into a physically meaningful aggregate representing potential moving targets. The background to this step is that even after strong clutter suppression and refined screening, the point traces of real low-altitude, slow-moving small targets (such as UAVs) may still appear as a few scattered, weak, and spatially adjacent point traces within a single processing cycle (e.g., due to target reflection characteristics and measurement noise, a target may be detected by multiple adjacent distance or velocity units), rather than a single, definitive point. Simultaneously, residual clutter or random noise point traces are often isolated.

[0031] Furthermore, S3 defines a dual-threshold association condition: for any two points to be processed (marked as the i-th and j-th), calculate the absolute difference of their distance data in the Cartesian coordinate system (i.e., the Euclidean distance between point i and point j in the three-dimensional space of X, Y, Z) and the absolute difference of their velocity data (i.e., the difference in radial velocity between point i and point j).

[0032] Two points are considered correlated only when both differences simultaneously meet the following strict constraints: The spatial distance difference is less than one range resolution unit, meaning the spatial interval between points i and j is very small, less than the minimum physical interval that radar equipment can effectively distinguish between two targets in the range dimension (radial, along the radar beam direction). This indicates that they are highly overlapping or extremely close in physical location, and are likely the same actual target with blurred range resolution. The velocity difference is less than one velocity resolution unit, meaning the difference in radial velocity (velocity components approaching or moving away from the radar) between points i and j is very small, less than the minimum velocity interval that radar equipment can effectively distinguish between two target movements in the Doppler frequency dimension (velocity dimension). This indicates that their motion states (velocities towards / away from the radar) are highly consistent, conforming to the kinematic characteristics of a single target.

[0033] Furthermore, the aforementioned association conditions essentially establish a tightly coupled association window, requiring that the associated points exhibit extremely high similarity in both spatial location and velocity—that is, spatial adjacency and consistent velocity. Once an association is established between points that meet the conditions, a preliminary, localized aggregation of associated points will be formed.

[0034] This aggregation process is iterative: it traverses all pairs of traces to be processed (all possible combinations), and for each pair of traces that meets the conditions, they are assigned to the same aggregate. If a new trace simultaneously meets the association conditions with a trace in multiple different aggregates, then, depending on the specific implementation (such as simple merging), these associated aggregates can be merged. The resulting aggregates of associated traces have clear physical meaning: the set of spatially densely adjacent traces with highly consistent motion speeds contained within each aggregate has a high confidence level in representing the same real, low-altitude, slow-moving target.

[0035] For example, a slowly flying UAV target, within one radar observation cycle, may generate several points in space around its true location with slightly fluctuating velocity values ​​due to echo fluctuations or detection by different range / velocity units. Through the association constraints of S3, these points reflecting different aspects of the same target, because their positions are sufficiently close (less than a range threshold) and their velocities are sufficiently close (less than a velocity threshold), will be aggregated into a single target unit. Points that are spatially isolated (far from other points) or whose velocity direction or magnitude is significantly abnormal (e.g., points with sudden, large velocity jumps) cannot find association objects that meet the dual-threshold conditions and are therefore excluded from aggregation, significantly reducing the possibility of non-target points (false points) entering subsequent steps. This process effectively transforms a large number of discrete, scattered points in three-dimensional space (mixed with target points and clutter / noise points) into a relatively small number of aggregated units with highly consistent internal point characteristics, laying the foundation for finally extracting the stable spatial coordinates and motion velocity information of the aggregate.

[0036] In S4, multiple associated point aggregates that aggregate highly consistent points from the output of S3 are received. The core task is to calculate and output the stable spatial coordinates of each aggregate in three-dimensional space and the average velocity it represents. This information is crucial for ultimately confirming the existence and motion state of the target.

[0037] The spatial coordinates are calculated using a weighted centroid method. First, for each point within the aggregation, its amplitude data (point signal-to-noise ratio) is considered as a weighting factor. Amplitude data essentially reflects the reliability and strength of the point's measurement value—a point with higher amplitude means a stronger target signal response, and its measurement position is usually less affected by noise or clutter, resulting in relatively higher positioning accuracy. Then, the product of each point's spatial coordinates (X, Y, Z) in the Cartesian coordinate system and its amplitude data is calculated, and these products are summed for all points within the aggregation. Simultaneously, the amplitude data of all points within the aggregation is also summed. Finally, the sum of the products is divided by the sum of the amplitude data, and the resulting weighted average position is the weighted centroid of the associated point aggregation. The spatial coordinates (X_w, Y_w, Z_w) of this weighted centroid are output as the position coordinates of the entire aggregation in three-dimensional space. This method gives greater influence to points with high signal-to-noise ratio and more reliable position estimation, making the final output target position coordinates less susceptible to excessive offset by individual points with high noise or poor positioning within the aggregation, thereby improving the accuracy and robustness of target spatial position estimation and getting closer to the target's true physical center of gravity.

[0038] Furthermore, the velocity calculation represented by the aggregated points is performed using an average value. The velocity data (radial velocity estimate of each point) of all points within the aggregate are arithmetically averaged, and the resulting average is used as the overall velocity output of the aggregate. The rationality and effectiveness of this approach stem from the effect achieved by the S3 association constraint: since S3 requires that the velocity difference between any points within the aggregate must be less than one velocity resolution cell of the radar when forming the aggregate, this ensures that the velocity estimates of all points within the aggregate are very close, with minimal fluctuations, and essentially revolve around the same true radial velocity value.

[0039] After processing in step S4, each aggregate of associated points is characterized as a moving unit with a definite three-dimensional spatial location (weighted centroid coordinates) and a consistent average radial velocity. The number of these units is far less than the number of points to be processed in the input of S3. Each unit has undergone clutter suppression, fine-tuning, three-dimensional spatial positioning correction, and strong consistency aggregation of space and velocity, which significantly improves its confidence in representing real low-altitude, slow-moving small targets (such as UAVs).

[0040] In summary, in the integrated moving target detection and clutter suppression method for low-altitude security, firstly, by adaptively selecting narrow or wide notch cancellers based on the real-time clutter Doppler spectrum width, the Doppler characteristics of low-speed targets are preserved to the maximum extent while ensuring deep suppression of high-intensity clutter, thus improving the retention rate of weak targets. Furthermore, through a two-level spot selection mechanism that includes physical constraints (near-range and low-speed thresholds) and statistical tests (amplitude below a preset multiple of the clutter mean), residual clutter, noise peaks, and long-range / high-speed interference are effectively filtered out, reducing false alarms. Additionally, a three-dimensional spatial correction is performed on the conversion from polar coordinates to rectangular coordinates to eliminate distance positioning errors caused by changes in ground altitude, further improving spot detection accuracy. Initial positioning accuracy is improved. Furthermore, a dual strong constraint correlation condition based on spatial proximity (range difference less than one range resolution unit) and velocity consistency (velocity difference less than one velocity resolution unit) is proposed. This ensures that neighboring points of the same target formed by radar resolution ambiguity or fluctuations within a single cycle are accurately aggregated, while eliminating spatially isolated or velocity-abrupt clutter points, thus improving the reliability of target aggregation. Finally, high-precision position output is obtained through amplitude-weighted spatial centroid calculation. The weight of high signal-to-noise ratio points is used to improve positioning robustness, and average velocity calculation is applied to aggregated points with highly consistent velocities to efficiently output target velocity. Ultimately, accurate detection and spatial positioning of low, slow, and small targets are achieved under complex terrain with strong clutter.

[0041] like Figure 2 As shown, in one embodiment, selecting the target filter type based on the clutter Doppler spectral width in S1 includes: S11. When the clutter Doppler spectral width is ≤50Hz, a three-pulse canceller is used as the target filter for clutter suppression. S12. When the clutter Doppler spectral width is >50Hz, a five-pulse canceller is used as the target filter for clutter suppression.

[0042] In this embodiment, it should be noted that in S11, when the calculated clutter Doppler spectral width is at a relatively narrow level (in specific implementation, it is less than or equal to a specific frequency threshold, such as 50Hz, as the judgment criterion), it indicates that the scattering objects in the environment move relatively slowly and uniformly, and the clutter energy is mainly concentrated in a relatively narrow Doppler frequency band.

[0043] For this scenario, a three-pulse canceller is chosen as the target filter. Essentially, a three-pulse canceller is a first-order differential filter with a relatively narrow suppression notch. It works by calculating a specific difference between the echo signals of three consecutive pulses (e.g., the second pulse echo minus the first, or a more complex combination), taking advantage of the high correlation (slow change) between pulses in clutter. Clutter components are effectively canceled out in this differential operation, while moving targets, due to phase changes between pulses (caused by the Doppler effect), do not have their signal components completely canceled out and may even be highlighted. Choosing this narrow-notch filter effectively suppresses narrow-spectrum clutter energy while minimizing suppression of near-zero frequency (zero velocity) or low Doppler frequency regions. This is crucial for preserving slow-moving small targets with speeds close to ground objects (low relative speed). For example, in calm weather, the clutter spectrum generated by swaying trees on the ground is narrow; a three-pulse canceller can effectively suppress this clutter while allowing the signal of a slowly flying drone nearby to be visible.

[0044] Furthermore, when the calculated clutter Doppler spectrum is significantly broadened (in specific implementations, this is judged by a value greater than a specific frequency threshold, such as 50 Hz), it indicates that the scatterers in the environment are moving violently and are directionally dispersed (such as vegetation swaying violently in strong winds or a rough sea surface), and the clutter energy occupies a wide range in the Doppler domain.

[0045] For this scenario, a five-pulse canceller is chosen as the target filter. A five-pulse canceller is a higher-order filter (typically second-order), and its structure involves more pulse echo combination operations (e.g., two-stage differential combination of adjacent pulse echoes), thus producing a wider suppression notch. This wider notch covers a broader Doppler frequency range, thereby more effectively suppressing broadband clutter with widely distributed energy across frequencies. While the wider notch of the five-pulse canceller may provide slightly stronger suppression for targets near zero frequency, in a strong clutter background, it is essential to prioritize deep suppression of broadband clutter to expose the target. For example, under strong sea winds, the clutter generated by sea spray is very broad; the wide notch of the five-pulse canceller can effectively suppress this diffuse clutter energy interference. Even at the cost of sacrificing sensitivity to extremely slow-moving targets, this is a necessary choice to ensure overall detection performance and prevent the target from being completely overwhelmed by clutter.

[0046] like Figure 3 As shown, in one embodiment, S1, processing the radar echo according to the target filter and outputting the processed slow-time signal, includes: S13. Apply the selected target filter to perform inter-pulse cancellation processing on the radar echo to suppress clutter components and retain moving target information. S14. Output a slow-time signal that has been clutter-suppressed and contains the potential traces corresponding to the moving target.

[0047] In this embodiment, it should be noted that step S13 is a specific execution process of S11 or S12. After selecting the target filter (whether it is a three-pulse or five-pulse canceller), it is applied to the raw radar echo data within the processing period for processing.

[0048] The essence of the processing is to perform inter-pulse cancellation operations. This process operates on a pulse-by-pulse sequence and at each range gate. The core algorithm of the filter calculates the difference between the echo signal of the current pulse and a specific linear combination of the echo signals of one or more previous pulses, according to its design rules (such as difference equations). Because clutter has a high temporal correlation between pulses (the phase and amplitude changes of adjacent echoes are small), this difference operation causes related clutter components to cancel each other out or be significantly attenuated due to their high similarity. Conversely, moving targets have radial velocities, and their echoes have significant phase changes (Doppler phase shift) between adjacent pulses. This change makes it impossible to completely eliminate the target component in the difference operation, and its intensity may even be enhanced relative to the suppressed clutter background.

[0049] This calculation systematically scans the radar data stream. After performing filtering operations on N consecutive pulses at each range gate, a sequence of N sampling points after clutter suppression is obtained. This step is the core operation of the entire method for suppressing clutter interference and is crucial for achieving target signal visibility.

[0050] In S14, after the inter-pulse cancellation processing (pulse cancellation operation) in S13, the original echo sequence of each range gate is transformed into a new processed sequence, which is called the processed slow-time signal. At this point, the signal is no longer the original data containing strong clutter components. Its key components become: residual background noise (thermal noise and environmental noise that cannot be perfectly filtered out) and potential moving target response signals. These moving target response signals are the target echo traces preserved by clutter suppression in S13, and they are the source of the "potential traces" that are of interest in subsequent detection stages.

[0051] These potential points are not the final confirmed target points; they still require rigorous screening and verification based on distance, velocity, and amplitude information. The output is an independent slow time series (vector) of length N (corresponding to N pulses) for each distance gate. This output is the foundational data that step S1 ultimately delivers to the next stage (S2) for processing.

[0052] like Figure 4 As shown, in one embodiment, step S2 involves filtering the processed slow-time signal for traces and selecting the traces to be processed, including: S21. Extract multiple potential points from the processed slow-time signal and obtain the distance data, velocity data and amplitude data of each potential point. S22. Retain initial points whose distance data is less than a preset distance and whose speed data is less than a preset speed, and form multiple first-level points; S23. Retain the first-level traces whose amplitude data is less than a preset multiple of the average clutter amplitude, and form multiple second-level traces, and use the second-level traces as traces to be processed.

[0053] In this embodiment, it should be noted that in S21, all possible candidate point trace information is initially identified and extracted from the processed slow-time signal. The processing method is to scan each range gate one by one: for a specific range gate k, its output slow-time sequence contains the amplitude (and phase) information of N pulse echoes after clutter suppression. The purpose of analyzing this sequence is to find out whether there is an "event" that may represent the target at this location (range gate).

[0054] The key operations are threefold: First, acquiring the range data of the target point: directly multiplying the range gate number of the target point by the radar's inherent range resolution (a known parameter) to calculate the straight-line distance of the target point relative to the radar. Second, acquiring the velocity data of the target point: performing a spectral transform (usually using Fast Fourier Transform / FFT) on the slow time series (an amplitude sequence of N points) to obtain the corresponding Doppler spectrum; identifying the dominant peak frequency (i.e., the frequency point with the highest energy) in the spectrum, and converting this dominant Doppler frequency into the target's radial velocity (the velocity component toward or away from the radar) according to the radar's transmission frequency (or wavelength). Third, acquiring the amplitude data of the target point: performing statistical analysis on the N amplitude values ​​of the slow time series (e.g., taking the maximum value) to obtain a value characterizing the signal strength of the point, which is often called the signal-to-noise ratio (SNR) of the target point because it implicitly reflects the strength of the signal point relative to the background noise level. After processing all range gates, a series of discrete potential targets are extracted, each containing the three key initial pieces of information: range, velocity, and amplitude. This process completes the initial structuring from continuous slow-time signals to discrete candidate points.

[0055] In step S22, all potential tracks and their initial parameters (distance, velocity, and amplitude data) extracted from step S21 are received. The task is to apply first-level physical constraints to perform preliminary screening of these potential tracks, significantly reducing the number of candidate tracks. The screening is based on two key prior knowledge points: first, the main monitoring targets of low-altitude security radar (such as small drones) typically operate in areas close to the radar; second, these targets move at relatively low speeds (far lower than high-speed individuals in aircraft or flocks of birds). Therefore, two physical thresholds are set: a preset distance upper limit (defining the maximum distance boundary of the target of radar interest) and a preset velocity upper limit (defining the maximum radial velocity boundary of the target of interest). The specific screening logic is: only tracks whose distance data is less than the preset distance upper limit (i.e., within the effective detection range of the radar interest) and whose velocity data is less than the preset velocity upper limit (i.e., excluding high-speed non-interested targets or interference). This step is a coarse screening, designed to quickly filter out obvious irrelevant signals or interference sources (such as large aircraft at long distances, high-speed birds, etc.). The set of tracks retained after screening is called the first-level tracks.

[0056] In S23, strong interference signals or local noise peaks caused by residual clutter are further eliminated to significantly reduce the false alarm rate in subsequent processing. First, the average background intensity of all first-level clutter points (or focused on a specific spatial area) within the current processing cycle is dynamically calculated, i.e., the average clutter amplitude. This average value reflects the average level of residual clutter and noise that has not been filtered out by the first-level screening under the current radar detection environment.

[0057] Next, a preset multiple is set (e.g., a multiple set based on empirical values ​​to control false alarm levels). This multiple defines a threshold at which the signal is significantly higher than the background clutter. The filtering logic is as follows: examine the amplitude data (signal-to-noise ratio) of each first-level trace one by one, retaining only those traces whose amplitude data is less than this preset multiple multiplied by the average clutter amplitude. This step is essentially a statistical significance test: it filters out traces whose amplitude deviates significantly from and is significantly higher than the current average background clutter level. These traces often correspond to strong clutter remnants, multipath interference, or occasional noise spikes, rather than real weak, slow-moving target echoes. Traces whose amplitude is below the preset multiple threshold are considered weak signals that are closer to the background and more likely to represent real small targets.

[0058] After this round of screening based on statistical significance, the resulting set of point traces is called the second-level point traces. These are then sent to subsequent steps as point traces to be processed (such as three-dimensional spatial transformation from polar coordinates to rectangular coordinates and point trace association aggregation). These point traces to be processed are more consistent with the characteristics of the low-altitude, slow-moving, small targets of interest in terms of physical location, movement speed, and signal strength. This greatly improves the overall quality and reliability of the point trace data input to subsequent processing stages. For example, a local high-amplitude point trace located in the low-altitude region, formed by ground object flashing caused by strong winds, was excluded. Although its distance and speed passed the first-level screening, it was effectively filtered out by S23 because its amplitude was much higher than the average background clutter.

[0059] In one implementation, S3 involves associating constraints on the points to be processed in the Cartesian coordinate system and forming an aggregation of multiple associated points, including: If the absolute difference in distance data between any i-th and j-th point traces to be processed is less than one distance resolution unit and the absolute difference in velocity data is less than one velocity resolution unit, then the i-th and j-th point traces to be processed are associated and form an associated point trace aggregation.

[0060] In this embodiment, it should be noted that the association condition is the core rule of step S3, which is used to determine whether two points to be processed (marked as the i-th and j-th) represent the same potential moving target in the Cartesian coordinate system, thereby deciding whether they should be aggregated into a unit.

[0061] The judgment is based on two strict criteria that must be met simultaneously: First, the Euclidean distance difference between the two points in three-dimensional space (i.e., the absolute difference in the straight-line distance between points i and j in the X, Y, and Z coordinates) must be less than one inherent range resolution cell of the radar. This means that the spatial interval between the two points is very small, less than the minimum physical interval limit at which the radar can clearly distinguish two independent targets in the range direction (along the radar beam). This condition ensures that the associated points are highly overlapping or inseparable in spatial position, and are likely to be repeated responses detected by the same physical target in multiple adjacent range gates due to radar resolution limitations or target echo fluctuations.

[0062] Second, the absolute difference between the radial velocity estimates of the two points (i.e., the absolute value of the velocity of point i minus the velocity of point j) must be less than one velocity resolution cell of the radar. This means that the difference in the radial velocities calculated by the two points is extremely small, less than the smallest velocity interval that the radar can distinguish based on the Doppler effect. This condition ensures that the associated points are consistent in their motion state, both pointing to the same target with radial velocities, which conforms to the kinematic characteristics of a single target having a stable velocity vector.

[0063] Finally, only when a pair of points simultaneously meets both the requirements of spatial proximity and consistent velocity are they considered highly correlated and assigned to the same associated point aggregation as components of the same target.

[0064] like Figure 5 As shown, in one embodiment, obtaining the spatial coordinates and motion velocity of each associated point aggregate in S4 includes: S41. Obtain the weighted centroid of each associated point cluster and use the spatial coordinates of the weighted centroid as the spatial coordinates of each associated point cluster. S42. The average speed data of all unprocessed points in each associated point aggregation is taken as the movement speed of each associated point aggregation.

[0065] In this embodiment, it should be noted that in S41, multiple associated point aggregates formed in S3 are received (each aggregate contains multiple spatially adjacent and velocity-consistent point aggregates to be processed). The goal is to calculate the optimal spatial coordinates of each aggregate as a whole in three-dimensional space. The method used is to calculate the weighted centroid of the aggregate. Specifically, the spatial coordinates (three-dimensional rectangular coordinates X, Y, Z) of each point aggregate and its corresponding amplitude data (i.e., the point aggregate signal-to-noise ratio, representing the reliability and signal strength of the point aggregate detection) are extracted first.

[0066] Then, the amplitude data of each point is used as its weighting factor. The principle is that points with higher amplitude represent stronger target reflection signals, and their position measurements are usually less affected by noise and clutter, resulting in higher accuracy and reliability. Therefore, they should have a greater influence in the final position estimation.

[0067] Next, a weighted average calculation is performed: the spatial coordinate components (X, Y, Z) of each point are multiplied by their own magnitude weights, and then the weighted coordinate components (i.e., the product of components and weights) of all points within the aggregation are summed. At the same time, the magnitude weight values ​​of all points are also summed (equivalent to the total weight).

[0068] Finally, dividing the sum of the weighted coordinate components obtained above by the sum of their corresponding total weights yields the weighted centroid coordinates (X_w, Y_w, Z_w) of the entire aggregation. These weighted centroid coordinates are the final spatial coordinates output by the entire aggregation unit.

[0069] In S42, the average velocity of each associated point cluster is calculated. The input is all the points to be processed contained in each cluster formed in S3. The method is relatively simple: directly calculate the arithmetic mean of the velocity data of all points in the cluster (i.e., the estimated radial velocity value of each point). This average value is used as the overall velocity output of the cluster. Its core rationale comes from the strict association condition of S3: because the points clustered together must satisfy the condition that the absolute value of the velocity difference is less than one velocity resolution unit, this means that the estimated radial velocity values ​​of them are extremely small in difference from each other, and are almost all concentrated near the true radial velocity of the target, forming a compact distribution.

[0070] Therefore, these velocity values ​​are highly consistent and revolve around a central value. No complex weighting is required (the linear relationship between amplitude data and velocity measurements is not strong). A simple arithmetic average can stably, efficiently, and accurately estimate the average radial velocity of the individual moving target represented by the aggregate. For example, an aggregate may contain three points whose velocity estimates, although slightly different due to noise (e.g., V1, V2, V3), are all extremely close to the target's true velocity V. Furthermore, |V1-V2|, |V1-V3|, and |V2-V3| are all much smaller than a velocity resolution. In this case, the average velocity (V1+V2+V3) / 3 can reliably represent V.

[0071] Also provided is an integrated moving target detection and clutter suppression system for low-altitude security, the system comprising: The clutter suppression module is used to acquire radar echoes within the processing period and obtain the clutter Doppler spectral width in the radar echoes. It selects a target filter based on the clutter Doppler spectral width, processes the radar echoes based on the target filter, and outputs the processed slow-time signal. The moving target point preprocessing module is used to filter the points in the processed slow-time signal and select the points to be processed, and transform the selected points to be processed from the polar coordinate system to the rectangular coordinate system. The moving target point aggregation module is used to perform association constraints on the points to be processed in the Cartesian coordinate system and form multiple associated point aggregations. The moving target point detection output module is used to obtain the spatial coordinates and movement speed of each associated point.

[0072] In one embodiment, the clutter suppression module is further configured to: use a three-pulse canceller as the target filter for clutter suppression when the clutter Doppler spectral width is ≤50Hz; and use a five-pulse canceller as the target filter for clutter suppression when the clutter Doppler spectral width is >50Hz.

[0073] In one implementation, the clutter suppression module is further configured to: apply a selected target filter to perform inter-pulse cancellation processing on the radar echo, suppress clutter components and retain moving target information; and output a slow-time signal that has been clutter suppressed and contains potential traces corresponding to the moving target.

[0074] In one embodiment, the moving target trace preprocessing module is further configured to: extract multiple potential traces from the processed slow-time signal, and acquire distance data, velocity data, and amplitude data of each potential trace; retain initial traces whose distance data is less than a preset distance and whose velocity data is less than a preset velocity, and form multiple first-level traces; retain first-level traces whose amplitude data is less than a preset multiple of the average clutter amplitude, and form multiple second-level traces, and use the second-level traces as traces to be processed.

[0075] In this embodiment, it should be noted that the specific operation method of the above-mentioned integrated moving target detection and clutter suppression system for low-altitude security has been described in detail in the embodiments of the integrated moving target detection and clutter suppression method for low-altitude security, and will not be elaborated here.

[0076] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0077] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0078] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for low altitude security, characterized in that, The method comprises: acquiring radar echoes in a processing period and acquiring a clutter Doppler spectrum width in the radar echoes, and selecting a target filter according to the clutter Doppler spectrum width, processing the radar echoes according to the target filter, and outputting a processed slow-time signal; performing plot filtering on the processed slow-time signal and filtering out to-be-processed plots, and converting the filtered to-be-processed plots from a polar coordinate system to a rectangular coordinate system; performing association constraint on the to-be-processed plots in the rectangular coordinate system and forming a plurality of associated plot aggregations; acquiring spatial coordinates and motion velocities of the associated plot aggregations.

2. The method for ground target detection and clutter suppression integration for low altitude security and defense according to claim 1, characterized in that, The selection of the target filter type according to the clutter Doppler spectrum width comprises: when the clutter Doppler spectrum width is less than or equal to 50 Hz, a three-pulse canceller is used as the target filter for clutter suppression; when the clutter Doppler spectrum width is greater than 50 Hz, a five-pulse canceller is used as the target filter for clutter suppression.

3. The method for ground moving target detection and clutter suppression integration for low altitude security and defense according to claim 1, characterized in that, The processing of the radar echoes according to the target filter and the output of the processed slow-time signal comprise: applying the selected target filter to inter-pulse cancellation processing of the radar echoes, suppressing clutter components and retaining motion target information; outputting a slow-time signal after clutter suppression and containing potential plots corresponding to motion targets.

4. The method for ground moving target detection and clutter suppression integration for low altitude security and defense according to claim 1, characterized in that, The plot filtering on the processed slow-time signal and the filtering out of to-be-processed plots comprise: extracting a plurality of potential plots from the processed slow-time signal, and acquiring distance data, velocity data and amplitude data of each potential plot; retaining initial plots with distance data less than a preset distance and velocity data less than a preset velocity, and forming a plurality of first-level plots; retaining first-level plots with amplitude data less than a preset multiple of a clutter amplitude mean value, forming a plurality of second-level plots, and taking the second-level plots as to-be-processed plots.

5. The method for ground moving target detection and clutter suppression integration for low altitude security and defense according to claim 1, characterized in that, The association constraint on the to-be-processed plots in the rectangular coordinate system and the formation of a plurality of associated plot aggregations comprise: if the absolute difference of distance data between any ith to-be-processed plot and jth to-be-processed plot is less than a distance resolution unit and the absolute difference of velocity data is less than a velocity resolution unit, the ith to-be-processed plot and the jth to-be-processed plot have association and form an associated plot aggregation.

6. The method for ground moving target detection and clutter suppression integration for low altitude security and defense according to claim 1, characterized in that, The acquisition of spatial coordinates and motion velocities of the associated plot aggregations comprises: acquiring a weighted centroid of each associated plot aggregation, and taking the spatial coordinates of the weighted centroid as the spatial coordinates of each associated plot aggregation; taking the average value of velocity data of all to-be-processed plots in each associated plot aggregation as the motion velocity of each associated plot aggregation.

7. A moving target detection and clutter suppression integrated system for low altitude security, characterized in that, The system comprises: a clutter suppression module, configured to acquire radar echoes in a processing period and acquire a clutter Doppler spectrum width in the radar echoes, and select a target filter according to the clutter Doppler spectrum width, process the radar echoes according to the target filter, and output a processed slow-time signal; a moving target plot preprocessing module, configured to perform plot filtering on the processed slow-time signal and filter out to-be-processed plots, and convert the filtered to-be-processed plots from a polar coordinate system to a rectangular coordinate system; a moving target plot aggregation module, configured to perform association constraint on the to-be-processed plots in the rectangular coordinate system and form a plurality of associated plot aggregations; and a moving target plot association module, configured to acquire spatial coordinates and motion velocities of the associated plot aggregations. A moving target track detection output module is configured to acquire spatial coordinates and motion velocities of each associated track aggregation.

8. The system according to claim 7, wherein, The clutter suppression module is further configured to: When the clutter Doppler spectrum width is less than or equal to 50 Hz, a three-pulse canceller is used as a target filter to suppress the clutter; When the clutter Doppler spectrum width is greater than 50 Hz, a five-pulse canceller is used as a target filter to suppress the clutter.

9. The system according to claim 7, wherein the system is characterized by: The clutter suppression module is further configured to: The selected target filter is applied to the radar echo for inter-pulse cancellation processing to suppress the clutter component and retain the motion target information; A slow-time signal containing potential tracks corresponding to the motion target after the clutter suppression is output.

10. The integrated system of moving target detection and clutter suppression for low altitude security and safety according to claim 7, wherein, The moving target track preprocessing module is further configured to: Extract a plurality of potential tracks from the processed slow-time signal, and acquire distance data, velocity data and amplitude data of each potential track; Retain initial tracks with distance data less than a preset distance and velocity data less than a preset velocity, and form a plurality of first-level tracks; Retain first-level tracks with amplitude data less than a preset multiple of the mean value of the clutter amplitude, form a plurality of second-level tracks, and take the second-level tracks as tracks to be processed.

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