A drone swarm presence monitoring system and method based on broadcast signal reflection

Abstract

This invention discloses a drone swarm presence monitoring system and method based on broadcast signal reflection, relating to the field of drone monitoring technology. It includes multiple receiving and sensing nodes, a data transmission network, a time synchronization unit, and a central processing unit. Each receiving and sensing node includes an observation receiving channel, a reference receiving channel, and an acquisition and local processing module, used to receive FM broadcast signals and extract the characteristics of intensity and phase changes over time to form a sensing feature vector. The central processing unit includes a reference coherence correction module, a background feature learning module, an existence determination module, and an alarm and recording module. It suppresses background drift caused by multipath and environmental noise through phase correction or coherent cancellation based on a direct wave reference, establishes a background feature vector, and compares it with a real-time feature vector to determine the presence of the drone swarm and output an alarm. It utilizes existing broadcast signals as an illumination source to enhance the detection capability of low-altitude, slow-moving, small-sized drone swarms.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) monitoring technology, and in particular to a UAV swarm presence monitoring system and method based on broadcast signal reflection. Background Technology

[0002] In recent years, with the continuous improvement of the miniaturization, cost reduction, and intelligence of drone platforms, drones have been widely used in logistics transportation, power line inspection, emergency rescue, agricultural plant protection, aerial film and television shooting, and military reconnaissance. At the same time, the rapid growth in the number of drones has also brought serious challenges to urban public safety, low-altitude airspace management, and the protection of key areas.

[0003] Against the backdrop of rapid development of the low-altitude economy, the future low-altitude airspace will present a complex situation of mixed operation of drones, manned aircraft, and new types of aircraft. In order to achieve "manageable, controllable, and traceable" low-altitude airspace, regulatory agencies urgently need to obtain information on the identity, time, and location of flight targets to support conflict avoidance, collision warning, and flight path management.

[0004] Currently, monitoring technologies for unmanned aerial vehicles (UAVs) mainly include active radar, photoelectric imaging, infrared detection, acoustic arrays, and radio frequency (RF) detection. While traditional active radar systems possess strong target detection capabilities, they are costly, complex to deploy, have limited detection capabilities for low-altitude, slow-moving, small targets, and are susceptible to ground clutter and multipath interference. Optical and infrared systems rely on line-of-sight conditions and are significantly affected by weather and lighting conditions. Acoustic systems have limited effective detection range. RF detection methods rely on the target's active communication signals, making them difficult to use against silent or autonomous UAV swarms. Therefore, in complex low-altitude environments, the presence detection of small-sized, low-reflection-section, swarming UAVs remains a technological bottleneck.

[0005] Passive radar technology utilizes existing electromagnetic signals in the environment as illumination sources, detecting targets by receiving reflections or disturbances of these signals. It offers advantages such as no self-emission required, good concealment, low cost, and wide coverage. FM radio signals, as a type of electromagnetic signal, are widely distributed in urban environments, typically operating at frequencies between 87.5 and 108 MHz. With stable signal power and strong penetration, they serve as an ideal continuous illumination source. During flight, UAV swarms exhibit reflection, scattering, blocking, and multipath modulation effects on FM electromagnetic waves. By constructing a ground-based FM receiving and sensing array and analyzing signal disturbance characteristics, it is hoped that the presence of UAV swarms can be detected and situational awareness achieved, thus enabling the construction of a low-cost monitoring system without the need for additional high-power transmitting equipment.

[0006] In summary, the existing technology has at least the following technical problems: Existing drone monitoring technologies have insufficient ability to detect the presence of low-altitude, slow-moving, small-sized drone swarms, and are susceptible to multipath interference and environmental noise in complex urban environments. They also lack effective passive sensing methods for silent or swarming targets. Summary of the Invention

[0007] The purpose of this invention is to provide a drone swarm presence monitoring system and method based on broadcast signal reflection, in order to solve the technical problems of existing drone monitoring technology having insufficient ability to detect the presence of low-altitude, slow-speed, small-sized drone swarms, and lacking effective passive sensing means for silent or swarming targets in complex urban environments where they are susceptible to multipath interference and environmental noise.

[0008] The preferred technical solutions among the many technical solutions provided by this invention can produce a variety of technical effects, which are described in detail below.

[0009] To address the aforementioned technical problems, the present invention provides the following technical solution: This invention provides a drone swarm presence monitoring system based on broadcast signal reflection, comprising multiple receiving and sensing nodes deployed on the ground below the monitoring area and forming a planar array; a data transmission network for uploading the sensing feature vectors of each receiving and sensing node to a central processing unit; a time synchronization unit for providing a time reference for each receiving and sensing node, ensuring that the sensing feature vectors of each receiving and sensing node meet the synchronization requirements of joint processing; and a central processing unit connected to the data transmission network, comprising a reference coherence correction module, a background feature learning module, an existence determination module, and an alarm and recording module; wherein each receiving and sensing node includes: an observation receiving channel for receiving FM broadcast signals and outputting an observation baseband signal; and a reference receiving channel for receiving a direct wave reference of the same FM broadcast signal and outputting a reference baseband signal. The system includes: an acquisition and local processing module for extracting the intensity and phase characteristics of the observed baseband signal over time and forming the sensing feature vector of the receiving sensing node; a reference coherence correction module for performing phase correction and / or coherence cancellation on the observed baseband signal or sensing feature vector of each receiving sensing node based on the corresponding direct wave reference to suppress background drift caused by multipath and environmental noise; a background feature learning module for establishing a background feature vector V0 when there is no UAV swarm in the monitoring area and updating V0 online; an existence determination module for acquiring a real-time feature vector V1, calculating the difference between V1 and V0 and comparing it with a determination threshold, and outputting a UAV swarm existence determination when the difference exceeds the determination threshold; and an alarm and recording module for generating alarm information and recording historical data when outputting a UAV swarm existence determination.

[0010] In one embodiment, the sensing feature vector includes at least one or more of the following features: intensity features: intensity mean, intensity variance, intensity first-order difference, or sliding window energy; phase features: phase mean, phase variance, phase change rate, or phase jump count; time-frequency features: short-time Fourier transform energy spectrum statistics, wavelet energy distribution, or spectral peak drift; cross-node correlation features: cross-correlation peak value, correlation coefficient, or spatial consistency index between any two of the receiving sensing nodes.

[0011] In one embodiment, the reference coherence correction module includes one or a combination of the following processes: phase locking / phase alignment based on the direct wave reference; normalization processing based on the direct wave reference to eliminate amplitude drift; coherence cancellation based on the direct wave reference to suppress fixed multipath components; and at least one of the phase locking and / or the coherence cancellation is performed within the central processing unit, and / or within the acquisition and local processing module of each of the receiving sensing nodes.

[0012] In one embodiment, each of the receiving sensing nodes includes: a broadband FM antenna, a low-noise amplifier, a software-defined radio receiver, an analog-to-digital converter, and a timing module; the timing module is a GPS timing module or a network timing module; the data transmission network is an Ethernet, 4G / 5G, or LoRa network.

[0013] In one embodiment, the difference metric of the existence decision module is one or a combination of the following: Euclidean distance, correlation coefficient distance, feature subspace projection residual, and eigenvalue change; the decision threshold is a fixed threshold or an adaptive threshold, wherein the adaptive threshold is determined based on historical background statistics to balance the detection probability and the false alarm rate.

[0014] In one embodiment, the background feature learning module uses recursive least squares, exponential moving average, or online machine learning models to perform drift tracking updates on the background feature vector V0, and resets or segments V0 when a sudden environmental change is detected.

[0015] In one embodiment, the central processing unit further includes an array joint processing module, which performs beamforming and / or spatial correlation processing based on the sensing feature vectors of multiple receiving sensing nodes to obtain a spatial consistency index, and incorporates the spatial consistency index into the difference metric to improve the robust presence determination capability of low-altitude, slow-speed, small-sized UAV swarms in multipath environments.

[0016] In one embodiment, the central processing unit further includes a multi-frequency illumination source selection and diversity fusion module, which selects at least two different FM broadcast signals at different FM frequencies as illumination sources within the FM broadcast band, forms sensing feature vectors corresponding to the FM frequencies respectively, and performs diversity fusion; the diversity fusion is weighted fusion, confidence voting, or fusion based on a learning model, in order to improve the detectability of weak disturbances and reduce the false alarm rate.

[0017] It also provides a method for monitoring the presence of UAV swarms based on the reflection of broadcast signals, which is applied to a UAV swarm presence monitoring system. The method includes the following steps: S1, signal reception and conversion output: multiple receiving and sensing nodes receive FM broadcast signals through observation receiving channels and output observation baseband signals, while obtaining direct wave references through reference receiving channels and outputting reference baseband signals. S2. Sensing feature vector formation: Based on the reference baseband signal, perform phase correction and / or coherent cancellation on the observed baseband signal, extract the features of intensity and phase changes over time, and form the sensing feature vector of each receiving sensing node. S3. Establish background features: When there are no drone swarms in the monitoring area, establish a background feature vector V0 and update it online; S4. Difference Measure Calculation and Threshold Comparison: Real-time feature vector V1 is acquired, the difference measure between V1 and V0 is calculated and compared with the decision threshold. S5. Existence Decision: When the difference metric exceeds the decision threshold, output the existence determination of the drone swarm, generate alarm information and record historical data.

[0018] In one implementation, before or during S4, beamforming and / or spatial correlation processing are performed on the sensing feature vectors of multiple receiving sensing nodes to obtain a spatial consistency index; and / or, after forming sensing feature vectors at different FM frequencies, diversity fusion is performed, and then a difference metric and an existence decision are calculated.

[0019] The beneficial effects of this invention are as follows: (i) Significantly improve the ability to detect the presence of low-altitude, slow-moving, small-sized UAV swarms This technical solution utilizes FM broadcast signals as a continuous illumination source. Ground-based array receiving and sensing nodes detect electromagnetic wave reflections, blockages, and diffraction disturbances caused by UAV swarms, and construct a sensing feature vector based on intensity and phase changes. Compared to traditional methods that rely solely on targets actively emitting signals or radar echo intensity, this solution does not depend on the UAVs' own communication links. It enables passive sensing of UAV swarms that are flying silently or have low radar cross-sections, improving the ability to detect the presence of low-altitude, slow-moving, small-sized targets.

[0020] (ii) Enhancing robustness in complex urban multipath environments In urban environments, building reflections, multipath propagation, and environmental noise can cause drastic fluctuations in background signals, affecting detection accuracy. This technical solution incorporates a reference coherence correction module in the central processing unit. Based on a direct-wave reference, it performs phase correction and / or coherent cancellation processing on the observed baseband signal, effectively suppressing the influence of stable multipath components and common amplitude-phase drift on background features. This improves the stability of the background feature vector V0, reduces the false alarm rate, and enhances the system's robustness in complex electromagnetic environments.

[0021] (iii) Achieving long-term adaptive operation capability This technical solution incorporates a background feature learning module to update and track the background feature vector V0 online. This adapts to changes in electromagnetic properties caused by environmental variations over time, avoiding performance degradation issues associated with fixed thresholds or static background models. By comparing the difference between the real-time feature vector V1 and the background feature vector V0, adaptive existence determination is achieved, improving the system's stability and reliability during long-term operation.

[0022] (iv) Support multi-node joint processing to improve detection reliability A ground-based array is constructed using multiple receiving and sensing nodes, and a time synchronization unit ensures that the sensing feature vectors of each node meet the joint processing conditions. The central processing unit performs joint analysis based on the multi-node data, improving the ability to identify spatial consistency disturbances, thereby enhancing the detection sensitivity of UAV swarm activities and reducing the risk of misjudgment caused by sporadic noise or single-point anomalies.

[0023] (v) Reduce system construction costs and improve concealment This technical solution utilizes existing FM broadcasting infrastructure as the illumination source, eliminating the need for high-power active transmitting equipment. The UAV swarm presence monitoring system only requires the deployment of receiving sensor nodes and a central processing unit to operate, offering advantages such as low construction cost, flexible deployment, and wide coverage. Furthermore, because the UAV swarm presence monitoring system operates in passive receiving mode, it possesses strong concealment capabilities, making it suitable for long-term monitoring of sensitive areas such as airport airspace, surrounding areas of important facilities, and large event venues.

[0024] In summary, this technical solution has significant advantages in detecting the presence of low-altitude, slow-speed, small-sized UAV swarms, robustness in complex environments, adaptive operation capabilities, and system economy. It can effectively solve the technical problem that existing UAV monitoring technologies lack effective passive sensing means for silent or swarming targets in complex low-altitude environments. Attached Figure Description

[0025] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is one of the structural components of the UAV swarm monitoring system of the present invention; Figure 2 This is the second schematic diagram of the structural composition of the UAV swarm monitoring system of the present invention; Figure 3 This is a schematic diagram of the structural composition of the drone swarm presence monitoring system according to the second embodiment of the present invention; Figure 4 This is a schematic diagram of the process steps of the drone swarm monitoring method of the present invention.

[0027] The accompanying figure is labeled as follows: 1. The drone swarm has a monitoring system; 2. Receiving sensor node; 21. Observation receiving channel; 22. Reference receiving channel; 23. Acquisition and local processing module; 3. Data transmission network; 4. Time synchronization unit; 5. Central Processing Unit; 51. Reference Coherence Correction Module; 52. Background Feature Learning Module; 53. Existence Decision Module; 54. Alarm and Recording Module; 55. Array Joint Processing Module; 56. Multi-Frequency Illumination Source Selection and Diversity Fusion Module. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0029] A specific implementation provides a drone swarm presence monitoring system and method based on broadcast signal reflection. The drone swarm presence monitoring system includes multiple receiving and sensing nodes, a data transmission network, a time synchronization unit, and a central processing unit. Each receiving and sensing node includes an observation receiving channel, a reference receiving channel, and an acquisition and local processing module, used to receive FM broadcast signals and extract the intensity and phase characteristics changing over time to form a sensing feature vector. The central processing unit includes a reference coherence correction module, a background feature learning module, an existence determination module, and an alarm and recording module, which suppresses background drift caused by multipath and environmental noise through phase correction and / or coherence cancellation based on direct wave reference. The system establishes a background feature vector and compares it with the real-time feature vector to determine the presence of a drone swarm and output an alarm. This technical solution utilizes existing broadcast signals as an illumination source, offering advantages such as low cost, good concealment, strong detection capability for low-altitude, slow-moving, small-sized drone swarms, and high robustness in complex urban environments. It is suitable for long-term monitoring and security assurance of airport airspace, important facilities, and urban low-altitude airspace. It effectively solves the technical problems of existing drone monitoring technologies, such as insufficient detection capability for the presence of low-altitude, slow-moving, small-sized drone swarms, susceptibility to multipath interference and environmental noise in complex urban environments, and lack of effective passive sensing means for silent or swarming targets.

[0030] The first implementation of a drone swarm presence monitoring system, for example Figure 1As shown, the system includes multiple receiving and sensing nodes 2, deployed on the ground below the monitoring area and forming a planar array; a data transmission network 3, used to upload the sensing feature vectors of each receiving and sensing node 2 to a central processing unit 5; a time synchronization unit 4, used to provide a time reference for each receiving and sensing node 2, so that the sensing feature vectors of each receiving and sensing node 2 meet the synchronization requirements of joint processing; and a central processing unit 5, connected to the data transmission network 3, including a reference coherence correction module 51, a background feature learning module 52, an existence determination module 53, and an alarm and recording module 54. Each receiving and sensing node 2 includes: an observation receiving channel 21, used to receive FM broadcast signals and output an observation baseband signal; and a reference receiving channel 22, used to receive the direct wave reference of the same FM broadcast signal and output a reference baseband signal. The local processing module 23 is used to extract the intensity and phase characteristics of the observed baseband signal over time and form the sensing feature vector of the receiving sensing node 2; the reference coherence correction module 51 is used to perform phase correction and / or coherence cancellation on the observed baseband signal or sensing feature vector of each receiving sensing node 2 based on the corresponding direct wave reference to suppress background drift caused by multipath and environmental noise; the background feature learning module 52 is used to establish a background feature vector V0 when there is no UAV swarm in the monitoring area and update V0 online; the existence decision module 53 is used to obtain the real-time feature vector V1, calculate the difference measure between V1 and V0 and compare it with the decision threshold, and output the UAV swarm existence determination when the difference measure exceeds the decision threshold; the alarm and recording module 54 is used to generate alarm information and record historical data when the UAV swarm existence determination is output.

[0031] Specifically, compared with existing UAV monitoring technologies such as active radar, photoelectric detection, acoustic arrays, and radio frequency detection, this technical solution constructs a UAV swarm presence monitoring system 1 based on FM broadcast signal reflection, which has the following technical effects: significantly improving the presence detection capability of low-altitude, slow-moving, small-sized UAV swarms; this technical solution uses FM broadcast signals as a continuous illumination source, and senses the electromagnetic wave reflection, obstruction, and diffraction disturbances caused by the UAV swarm through ground-based array receiving sensing nodes 2, and constructs a sensing feature vector based on intensity and phase changes. Compared with traditional methods that rely solely on the target's active signal transmission or radar echo intensity, this technical solution does not rely on the UAV's own communication link, and can passively sense UAV swarms that are flying silently or have low reflectivity, improving the presence detection capability of low-altitude, slow-moving, small-sized targets.

[0032] Enhancing robustness in complex urban multipath environments: In urban environments, building reflections, multipath propagation, and environmental noise can cause drastic fluctuations in background signals, affecting detection accuracy. This technical solution incorporates a reference coherence correction module 51 in the central processing unit 5. Based on a direct-wave reference, it performs phase correction and / or coherent cancellation processing on the observed baseband signal, effectively suppressing the influence of stable multipath components and common amplitude and phase drift on background features, improving the stability of the background feature vector V0, reducing the false alarm rate, and enhancing the system's robustness in complex electromagnetic environments.

[0033] To achieve long-term adaptive operation capability, this technical solution includes a background feature learning module 52, which performs online updates and drift tracking of the background feature vector V0. This allows it to adapt to changes in electromagnetic properties caused by environmental variations over time, avoiding performance degradation issues caused by fixed thresholds or static background models. By comparing the difference between the real-time feature vector V1 and the background feature vector V0, adaptive existence determination is achieved, improving the stability and reliability of the system during long-term operation.

[0034] It supports multi-node joint processing to improve detection reliability. Multiple receiving and sensing nodes 2 form a ground surface array, and with the help of time synchronization unit 4, the sensing feature vectors of each receiving and sensing node 2 meet the joint processing conditions. The central processing unit 5 performs joint analysis based on multi-node data, which improves the ability to identify spatial consistency disturbances, thereby enhancing the detection sensitivity of UAV swarm activities and reducing the risk of misjudgment caused by sporadic noise or single-point anomalies.

[0035] This technical solution reduces system construction costs and improves concealment. It utilizes existing FM broadcasting infrastructure as the illumination source, eliminating the need for high-power active transmitting equipment. The UAV swarm presence monitoring system 1 only requires the deployment of receiving sensor nodes 2 and a central processing unit 5 to operate, offering advantages such as low construction costs, flexible deployment, and wide coverage. Furthermore, because the UAV swarm presence monitoring system 1 operates in passive receiving mode, it possesses strong concealment, making it suitable for long-term monitoring of sensitive areas such as airport airspace, surrounding areas of important facilities, and large event venues.

[0036] In summary, this technical solution has significant advantages in detecting the presence of low-altitude, slow-speed, small-sized UAV swarms, robustness in complex environments, adaptive operation capabilities, and system economy. It can effectively solve the technical problem that existing UAV monitoring technologies lack effective passive sensing means for silent or swarming targets in complex low-altitude environments.

[0037] As one alternative implementation method: Regarding the specific form of the aforementioned sensing feature vector, this implementation is, for example... Figure 1As shown, the sensing feature vector includes at least one or more of the following features: intensity features: intensity mean, intensity variance, intensity first-order difference or sliding window energy; phase features: phase mean, phase variance, phase change rate or phase jump count; time-frequency features: short-time Fourier transform energy spectrum statistics, wavelet energy distribution or spectral peak drift; cross-node correlation features: cross-correlation peak value, correlation coefficient or spatial consistency index between any two receiving sensing nodes 2.

[0038] In application, during actual operation, each receiving and sensing node 2 continuously performs sliding time window processing on the observed baseband signal. Through statistical analysis of the changes in intensity and phase over time, a sensing feature vector containing intensity features, phase features, time-frequency features, and cross-node correlation features is constructed.

[0039] Among them, intensity features are used to characterize the energy disturbance caused by UAV swarms to the electromagnetic propagation path; phase features are used to characterize the changes in the propagation path length of electromagnetic waves and the dynamic disturbance of scatterers; time-frequency features are used to enhance the ability to identify slow periodic disturbances or non-stationary signal components; and cross-node correlation features are used to identify multi-node spatially consistent disturbance patterns.

[0040] By constructing feature vectors through the synergistic use of the aforementioned multi-dimensional features, the system can make judgments based on a single energy peak or a single phase jump, and instead conduct a comprehensive analysis of electromagnetic environment changes in a statistical sense. This improves the sensitivity to the presence detection of low-altitude, slow-moving, small-sized UAV swarms, reduces the risk of misjudgment caused by single-point noise or occasional multipath changes, and effectively solves the problem of traditional detection methods being insensitive to weak disturbances.

[0041] The sliding window length can be set to an adjustable parameter within the range of 10ms to 500ms; the feature dimension can be reduced by feature selection algorithm; cross-node related features can be analyzed by normalized cross-correlation or covariance matrix eigenvalue analysis; if necessary, principal component analysis (PCA) or autoencoder can be introduced to achieve feature compression.

[0042] Regarding the signal processing method of the aforementioned reference coherence correction module 51, this embodiment, for example... Figure 1 As shown, the reference coherence correction module 51 includes one or a combination of the following processes: phase locking / phase alignment based on the direct wave reference; normalization processing based on the direct wave reference to eliminate amplitude drift; coherent cancellation based on the direct wave reference to suppress fixed multipath components; and at least one of phase locking and / or coherent cancellation is performed within the central processing unit 5, and / or within the acquisition and local processing module 23 of each receiving sensing node 2.

[0043] During system operation, the reference receiving channel 22 continuously receives the direct wave component of the same FM broadcast signal and uses it as an amplitude and phase reference. The central processing unit 5 or the acquisition and local processing module 23 performs phase locking and amplitude normalization processing based on this direct wave reference, thereby eliminating common interference factors such as broadcast station power fluctuations, receiver link gain drift, and local oscillator phase drift. Based on this, coherent cancellation techniques are used to estimate and cancel stable multipath components in the observed signal that are highly correlated with the reference height, and the residual signal is extracted for feature construction.

[0044] This processing mechanism works in conjunction with the background feature learning module 52 to maintain the statistical stability of the background feature vector V0 in complex urban environments, making it sensitive only to new disturbances. This significantly reduces the false alarm rate, improves the detection reliability in complex multipath environments, and solves the problem of existing passive detection methods being sensitive to urban environments.

[0045] Coherent cancellation can be achieved using least squares projection, adaptive filtering (LMS / RLS), or phase-locked loop with weighted coefficients. When the reference signal-to-noise ratio is below the threshold, it can automatically switch to a backup FM frequency. Coherent correction can be performed at the receiving sensing node 2 to reduce data transmission or at the central end to improve processing accuracy.

[0046] Regarding the structural composition of the aforementioned receiving and sensing node 2, this embodiment is, for example... Figure 1 As shown, each receiving sensing node 2 includes: a broadband FM antenna, a low-noise amplifier, a software-defined radio receiver, an analog-to-digital converter, and a timing module; the timing module is a GPS timing module or a network timing module; the data transmission network 3 is an Ethernet, 4G / 5G, or LoRa network.

[0047] In application, each receiving sensing node 2 receives broadcast signals via a broadband FM antenna. After the signal quality is improved by a low-noise amplifier, it is input into a software-defined radio receiver for down-conversion processing and then generates a digital baseband signal via an analog-to-digital converter. The timing module provides a unified time reference, enabling the data from multiple nodes to be aligned on the time axis.

[0048] This hardware architecture ensures stable signal links and high phase consistency, providing a reliable data foundation for subsequent multi-node joint processing. Through the synergy of time synchronization and data transmission network 3, a system architecture of distributed acquisition and centralized processing is achieved, effectively solving the spatial correlation error problem caused by data asynchrony among multiple nodes.

[0049] The broadband FM antenna uses a directional antenna or a dual-polarized antenna; the timing accuracy is preferably better than 1 microsecond; the data can be uploaded after compression encoding; the receiving sensing node 2 is powered by solar energy to achieve long-term deployment.

[0050] Regarding the difference measurement method and the calculation method of the decision threshold of the existence decision module 53 mentioned above, this implementation is as follows: Figure 1 As shown, the difference measure of the existence decision module 53 is one of the following or a combination thereof: Euclidean distance, correlation coefficient distance, feature subspace projection residual, and eigenvalue change; the decision threshold is a fixed threshold or an adaptive threshold, the adaptive threshold is determined based on historical background statistics to balance the detection probability and the false alarm rate.

[0051] In application, the existence determination module 53 makes a determination based on the difference between the real-time feature vector V1 and the background feature vector V0. It quantifies the statistical deviation between the current electromagnetic environment and the background environment using methods such as Euclidean distance, correlation coefficient distance, or subspace residual. An existence determination is output when the deviation exceeds a threshold.

[0052] This mechanism transforms traditional instantaneous energy detection into multidimensional statistical difference detection, enabling the system to identify persistent, weak disturbances and avoid false alarms caused by single abnormal peak values. The adaptive threshold is dynamically updated based on historical background statistics, thus maintaining stable detection performance under changing environmental conditions.

[0053] The decision threshold can be adaptively calculated using the CFAR algorithm; a multi-level alarm mechanism is introduced; and the decision strategy is automatically adjusted according to the time period (day / night).

[0054] Regarding the data processing method of the aforementioned background feature learning module 52, this implementation is as follows: Figure 1 As shown, the background feature learning module 52 uses recursive least squares, exponential moving average or online machine learning models to perform drift tracking updates on the background feature vector V0, and resets or segments V0 when environmental mutations are detected.

[0055] When applied, the background feature learning module 52 continuously performs statistical modeling on the background feature vector V0 under stable environmental conditions, and updates the background feature vector V0 by recursive least squares or exponential moving average, so that the system can adapt to temperature changes, broadcast power adjustments or seasonal environmental changes.

[0056] When a sudden environmental change is detected (such as a large-scale movement of people or the entry of a large metal object), a reset or segmented modeling strategy is implemented to avoid background model contamination. This mechanism significantly enhances the long-term stability of the system and solves the problem of performance degradation over time in traditional static threshold systems.

[0057] Background update freeze strategy can be set; an anomaly detection mechanism is introduced to avoid mislearning; and a multi-background model is built based on sliding time window.

[0058] Regarding the aforementioned central processing unit 5, this implementation is, for example... Figure 2As shown, the central processing unit 5 also includes an array joint processing module 55, which performs beamforming and / or spatial correlation processing based on the sensing feature vectors of multiple receiving sensing nodes 2 to obtain a spatial consistency index, and incorporates the spatial consistency index into the difference metric to improve the robust presence determination capability of low-altitude slow-speed small-sized UAV swarms in multipath environments.

[0059] In application, the central processing unit 5 extracts spatial consistency indices by performing beamforming or spatial correlation processing on the sensing feature vectors of multiple receiving sensing nodes 2. UAV swarms typically generate correlated disturbance patterns in space, while random noise usually exhibits spatially uncorrelated characteristics.

[0060] By incorporating spatial consistency indices into the difference metric, the UAV swarm presence monitoring system 1 can distinguish between real target disturbances and local noise anomalies, improving its robust presence determination capability for swarm flying targets, and significantly reducing false alarms, especially in complex multipath environments.

[0061] The array geometry can be linear or area array; spatial processing can employ MVDR or minimum variance beamforming; and it can support regional directional enhancement detection.

[0062] A second implementation of a drone swarm monitoring system, for example Figure 3 As shown, the difference between this embodiment and the first embodiment is that the central processing unit 5 further includes a multi-frequency illumination source selection and diversity fusion module 56, which selects at least two different FM broadcast signals at different FM frequencies as illumination sources within the FM broadcast band, forms sensing feature vectors for the corresponding FM frequencies respectively, and performs diversity fusion; the diversity fusion is weighted fusion, confidence voting, or fusion based on a learning model, in order to improve the detectability of weak disturbances and reduce the false alarm rate.

[0063] The UAV swarm monitoring system 1 selects multiple different FM frequencies as illumination sources within the FM broadcast band. These different FM frequencies exhibit varying propagation characteristics and multipath modes. By constructing sensing feature vectors for each FM frequency separately and performing diversity fusion, the impact of single-frequency fading or occasional interference on the detection results can be reduced.

[0064] The diversity fusion module integrates multi-frequency detection results through weighted fusion or confidence voting, thereby increasing the detection probability of weak disturbances and reducing the false alarm rate, thus enhancing the overall reliability of the system.

[0065] Weights can be dynamically allocated based on the signal-to-noise ratio; primary and backup frequencies can be set; and FM radio station switching strategies are supported.

[0066] The third implementation of a drone swarm monitoring system, for example Figure 1As shown, the difference between this embodiment and the first embodiment is that the reference coherence correction module 51 is configured to: construct a weighted coefficient based on the reference baseband signal of the reference receiving channel 22 within a preset sliding time window, and apply the weighted coefficient to the observation baseband signal of the observation receiving channel 21 to achieve phase correction and / or amplitude normalization processing; wherein, the phase component of the weighted coefficient is used to cancel the common phase drift, and the amplitude component of the weighted coefficient is used to cancel the common gain drift.

[0067] The reference coherence correction module 51 is further configured to: within a sliding time window, perform coherent fixed component estimation on the observed baseband signal after weighting coefficient correction based on the reference baseband signal after weighting coefficient correction, so as to obtain the estimated signals of the direct wave leakage component and / or stable multipath component coherent with the reference baseband signal in the observed baseband signal.

[0068] Coherent fixed component estimation includes one or a combination of the following: Least squares projection estimation: Estimate scalar or vector coefficients to minimize the approximation error of the reference baseband signal after weighted coefficient transformation to the observed baseband signal; Adaptive filtering estimation: Using an LMS or RLS adaptive filter, with the reference baseband signal as input and the observed baseband signal as the desired output, the estimated signal is obtained.

[0069] The coherence correction module 51 is further configured to: coherently cancel the estimated signal from the observed baseband signal after the weighting coefficient correction to obtain the residual baseband signal; the existence decision module 53 constructs a real-time feature vector V1 based on the residual baseband signal and compares it with the background feature vector V0 to output the existence determination of the UAV swarm.

[0070] The feature vector constructed based on the residual baseband signal includes at least one or more of the following features: residual energy, residual energy change rate, residual variance, residual phase change rate, residual phase jump count, residual time-frequency domain energy spectrum statistics, and cross-correlation peak or spatial consistency index among the residual signals of multiple receiving sensing nodes 2.

[0071] The reference coherence correction module 51 also includes trigger and back-off logic: when the signal-to-noise ratio of the reference baseband signal is lower than the threshold and / or the reference phase jitter exceeds the threshold, the coherent fixed component estimation and coherent cancellation are suspended, and phase correction and / or amplitude normalization processing are maintained; and / or, the backup FM frequency or backup broadcast station is switched as the reference signal source.

[0072] In application, the reference coherence correction module 51 first constructs weighting coefficients based on the reference baseband signal output from the reference receiving channel 22 within a preset sliding time window. These weighting coefficients include phase and amplitude components. The phase component is used to compensate for common phase drift introduced by the broadcast signal source and the receiving link, while the amplitude component is used to compensate for common gain drift or power fluctuations. By applying the weighting coefficients to the observed baseband signal of the observed receiving channel 21, phase locking and amplitude normalization of the observed signal are achieved, ensuring that the signals across multiple nodes are under a unified amplitude and phase reference, providing consistent input conditions for subsequent joint processing.

[0073] After completing the weighted coefficient correction, the reference coherence correction module 51 further estimates the coherent fixed components of the observed baseband signal based on the weighted coefficient-corrected reference baseband signal. This estimation process extracts the direct wave leakage component and stable multipath component that are highly coherent with the reference signal from the observed signal through least squares projection estimation or adaptive filtering estimation. Subsequently, the estimated signal is coherently canceled from the observed baseband signal to obtain the residual baseband signal.

[0074] Compared to the original observed signal, the residual baseband signal significantly reduces the impact of stable multipath and slow drift noise on feature construction, while enhancing the sensitivity to disturbances introduced by dynamic scatterers (such as UAV swarms). The existence decision module 53 constructs a real-time feature vector V1 based on the residual baseband signal and compares it with the background feature vector V0 by measuring the difference, thus transforming the system's decision criterion from "changes in the original electromagnetic environment" to "changes in dynamic disturbances after background removal".

[0075] This processing mechanism works in conjunction with the background feature learning module 52 to improve the stability of V0 on the one hand, and make V1 more prominent in terms of target disturbance features on the other hand, thereby significantly reducing the false alarm rate and improving the detection sensitivity of low-altitude, slow-speed, small-sized UAV swarms. It can maintain stable performance, especially in complex urban multipath environments, and solves the problem that existing passive monitoring systems are easily affected by stable multipath and thus misjudgment or missed judgment.

[0076] Furthermore, the triggering and backoff logic set in the reference coherence correction module 51 further enhances the reliability of the UAV swarm presence monitoring system 1. When the signal-to-noise ratio of the reference baseband signal is detected to be lower than the threshold or the phase jitter exceeds the preset range, the UAV swarm presence monitoring system 1 suspends coherent fixed component estimation and coherent cancellation, and only maintains phase correction or amplitude normalization processing to avoid errors introduced by erroneous fixed component estimation; at the same time, it can automatically switch to the backup FM frequency or backup broadcast station as the reference signal source to maintain the stability of the reference reference. This mechanism ensures that the UAV swarm presence monitoring system 1 can still maintain basic detection capabilities under the condition of abnormal reference signal or partial obstruction, and improves the continuous operation stability of the UAV swarm presence monitoring system 1.

[0077] In summary, this embodiment, through a closed-loop structure of "reinforced coefficient construction - coherent fixed component estimation - coherent cancellation - residual feature construction - difference decision", enables the UAV swarm existence monitoring system 1 to significantly enhance its ability to detect weakly disturbed targets while maintaining the advantages of low cost and passive monitoring, and improves its robustness and engineering feasibility in complex multipath environments.

[0078] Based on the above embodiments of the UAV swarm presence monitoring system, a UAV swarm presence monitoring method based on broadcast signal reflection is provided, which can be applied to the UAV swarm presence monitoring system, such as... Figure 4 As shown, it includes the following steps: S1, signal reception and conversion output: multiple receiving and sensing nodes receive FM broadcast signals and output observation baseband signals through observation receiving channels, and at the same time obtain direct wave references through reference receiving channels and output reference baseband signals; S2. Sensing feature vector formation: Based on the reference baseband signal, perform phase correction and / or coherent cancellation on the observed baseband signal, extract the features of intensity and phase changes over time, and form the sensing feature vector of each receiving sensing node. S3. Establish background features: When there are no drone swarms in the monitoring area, establish a background feature vector V0 and update it online; S4. Difference Measure Calculation and Threshold Comparison: Real-time feature vector V1 is acquired, the difference measure between V1 and V0 is calculated and compared with the decision threshold. S5. Existence Decision: When the difference metric exceeds the decision threshold, output the existence determination of the drone swarm, generate alarm information and record historical data.

[0079] Specifically, before or during S4, beamforming and / or spatial correlation processing are performed on the sensing feature vectors of multiple receiving sensing nodes to obtain a spatial consistency index; and / or, after forming sensing feature vectors at different FM frequencies, diversity fusion is performed, and then the difference metric and existence decision are calculated.

[0080] In application, the UAV swarm presence monitoring system executes steps S1-S5 in a loop. A closed-loop detection process is formed through signal reception, reference coherence correction, feature construction, background learning, and difference determination. Spatial consistency indicators and multi-frequency diversity results work synergistically in the difference measurement calculation stage, ensuring that detection decisions are supported by information from time, frequency, and spatial dimensions.

[0081] This multidimensional fusion mechanism significantly improves the ability to detect the presence of low-altitude, slow-moving, small-sized UAV swarms and maintains high robustness in complex urban electromagnetic environments.

[0082] The detection cycle can be set from 0.1s to 5s; anomaly classification alarms are introduced; and the detection results are uploaded to the airspace management platform.

[0083] In one optional implementation, the reference coherence correction module can construct weighted coefficients within a sliding time window, perform phase locking and amplitude normalization on the observed baseband signal, and perform coherent cancellation after estimating the fixed multipath components based on least squares projection or adaptive filtering to obtain the residual baseband signal.

[0084] The residual baseband signal is used to construct the sensing feature vector, thereby enhancing the sensitivity to dynamic scattering disturbances. In application, this processing chain forms a closed-loop structure of "reference phase-locked loop—fixed component estimation—coherent cancellation—residual feature construction—background difference decision," which helps to further improve the detection accuracy in complex multipath environments.

[0085] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described.

Claims

1. A drone swarm presence monitoring system based on broadcast signal reflection, characterized in that, It includes multiple receiving and sensing nodes, which are deployed on the ground below the monitoring area and form a surface array; A data transmission network is used to upload the sensing feature vectors of each of the receiving sensing nodes to the central processing unit; The time synchronization unit is used to provide a time reference for each of the receiving and sensing nodes, so that the sensing feature vectors of each of the receiving and sensing nodes meet the synchronization requirements of joint processing. A central processing unit is connected to the data transmission network. The central processing unit includes a reference coherence correction module, a background feature learning module, an existence determination module, and an alarm and recording module. Wherein, any of the receiving sensing nodes includes: an observation receiving channel for receiving FM broadcast signals and outputting an observation baseband signal; a reference receiving channel for receiving a direct wave reference of the same FM broadcast signal and outputting a reference baseband signal; and an acquisition and local processing module for extracting the characteristics of intensity and phase changes over time from the observation baseband signal and forming a sensing feature vector of the receiving sensing node. The reference coherence correction module is used to perform phase correction and / or coherence cancellation on the observed baseband signal or sensed feature vector of each of the receiving sensing nodes based on the corresponding direct wave reference, so as to suppress background drift caused by multipath and environmental noise. The background feature learning module is used to establish a background feature vector V0 when there is no drone swarm in the monitoring area, and to update V0 online. The existence determination module is used to obtain the real-time feature vector V1, calculate the difference measure between V1 and V0 and compare it with the determination threshold. When the difference measure exceeds the determination threshold, the existence determination of the drone swarm is output. The alarm and recording module is used to generate alarm information and record historical data when the existence of a drone swarm is determined.

2. The drone swarm existence monitoring system according to claim 1, characterized in that, The sensing feature vector includes at least one or more of the following features: Intensity characteristics: mean intensity, variance intensity, first-order difference intensity, or sliding window energy; Phase characteristics: phase mean, phase variance, phase change rate, or phase jump count; Time-frequency characteristics: short-time Fourier transform energy spectrum statistics, wavelet energy distribution, or spectral peak shift; Cross-node correlation characteristics: the peak value of cross-correlation, correlation coefficient or spatial consistency index between any two of the aforementioned receiving and sensing nodes.

3. The drone swarm existence monitoring system according to claim 1, characterized in that, The reference coherence correction module includes one or a combination of the following processes: Phase locking / phase alignment based on the direct wave reference; Normalization based on the direct wave reference is used to eliminate amplitude drift; Coherent cancellation based on the direct wave reference is used to suppress fixed multipath components; Furthermore, at least one of the phase locking and / or the coherent cancellation is performed within the central processing unit, and / or within the acquisition and local processing module of each of the receiving sensing nodes.

4. The drone swarm existence monitoring system according to claim 1, characterized in that, Each of the aforementioned receiving and sensing nodes includes: a broadband FM antenna, a low-noise amplifier, a software-defined radio receiver, an analog-to-digital converter, and a timing module; The timing module is a GPS timing module or a network timing module; The data transmission network is an Ethernet, 4G / 5G, or LoRa network.

5. The drone swarm existence monitoring system according to claim 1, characterized in that, The difference measure of the existence decision module is one or a combination of the following: Euclidean distance, correlation coefficient distance, feature subspace projection residual, and eigenvalue change. The decision threshold can be a fixed threshold or an adaptive threshold. The adaptive threshold is determined based on historical background statistics to balance the detection probability and the false alarm rate.

6. The drone swarm existence monitoring system according to claim 1, characterized in that, The background feature learning module uses recursive least squares, exponential moving average, or online machine learning models to perform drift tracking updates on the background feature vector V0, and resets or segments V0 when environmental mutations are detected.

7. The drone swarm existence monitoring system according to claim 1, characterized in that, The central processing unit further includes an array joint processing module, which performs beamforming and / or spatial correlation processing based on the sensing feature vectors of multiple receiving sensing nodes to obtain a spatial consistency index, and incorporates the spatial consistency index into the difference metric to improve the robust presence determination capability of low-altitude, slow-speed, small-sized UAV swarms in multipath environments.

8. The drone swarm existence monitoring system according to claim 1, characterized in that, The central processing unit also includes a multi-frequency illumination source selection and diversity fusion module, which selects at least two different FM broadcast signals at different FM frequencies as illumination sources, forms sensing feature vectors corresponding to the FM frequencies, and performs diversity fusion. The diversity fusion is weighted fusion, confidence voting, or fusion based on a learning model, in order to improve the detectability of weak perturbations and reduce the false alarm rate.

9. A method for monitoring the presence of unmanned aerial vehicle (UAV) swarms based on broadcast signal reflection, applied to the UAV swarm presence monitoring system according to any one of claims 1-8, characterized in that, Includes the following steps: S1. Signal reception and conversion output: Multiple receiving and sensing nodes receive FM broadcast signals and output observation baseband signals through observation receiving channels, and at the same time obtain direct wave references through reference receiving channels and output reference baseband signals. S2. Sensing feature vector formation: Based on the reference baseband signal, perform phase correction and / or coherent cancellation on the observed baseband signal, extract the features of intensity and phase changes over time, and form the sensing feature vector of each receiving sensing node. S3. Establish background features: When there are no drone swarms in the monitoring area, establish a background feature vector V0 and update it online; S4. Difference Measure Calculation and Threshold Comparison: Real-time feature vector V1 is acquired, the difference measure between V1 and V0 is calculated and compared with the decision threshold. S5. Existence Decision: When the difference metric exceeds the decision threshold, output the existence determination of the drone swarm, generate alarm information and record historical data.

10. The method for monitoring the presence of unmanned aerial vehicle (UAV) swarms according to claim 9, characterized in that, Before or during S4, beamforming and / or spatial correlation processing are performed on the sensing feature vectors of multiple receiving sensing nodes to obtain a spatial consistency index; and / or, after forming sensing feature vectors at different FM frequencies, diversity fusion is performed, and then the difference metric and existence decision are calculated.

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