A drone swarm direction and distance monitoring system based on broadcast signal reflection

Abstract

This invention discloses a drone swarm azimuth and distance monitoring system based on broadcast signal reflection, belonging to the field of drone monitoring technology. The system includes an illumination source selection unit, a reference receiving channel, a monitoring receiving channel, a frequency conversion acquisition unit, a filtering estimation unit, an azimuth and distance calculation unit, and a detection and tracking unit. The system selects an FM radio station as a non-cooperative illumination source, acquires the direct signal through the reference channel, and acquires the reflected signal from the drone swarm through the monitoring channel. Dual-channel down-conversion and synchronous sampling are completed under the action of the same local oscillator. The system extracts the time delay difference through cross-correlation and converts it into a bistatic distance difference. Candidate solutions are generated under antenna pointing sector constraints, and residual optimization is performed to achieve joint azimuth and distance calculation. Multi-target detection and tracking are performed in the time-delay-Doppler domain. It is suitable for continuous monitoring of drone swarms in urban low-altitude airspace and solves the technical problem of insufficient stable detection capability of existing drone monitoring technologies for 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 location and distance monitoring system based on broadcast signal reflection. Background Technology

[0002] In recent years, with the maturity of drone technology and a significant reduction in manufacturing costs, drones have been widely used in logistics, aerial surveying, agricultural plant protection, and emergency rescue. At the same time, the number of drones has exploded, especially in urban low-altitude airspace, where the trend of multiple types and batches of drones operating together is becoming increasingly apparent. Against this backdrop, problems such as uncontrolled drone flight, illegal intrusion, and cross-regional operations are frequent, posing serious challenges to urban public safety, low-altitude economic order, and the protection of key areas.

[0003] In the field of urban security, drones may be used for illegal reconnaissance, delivery of contraband, and disruption of public order. Regarding the protection of key areas, it is necessary to build an "electronic fence" system with real-time detection capabilities. Once an unauthorized drone intrudes, its location and distance should be identified promptly to provide target indication for subsequent interference, expulsion, or disposal measures. Against the backdrop of rapid development of the low-altitude economy, the future low-altitude airspace will present a complex situation of mixed drone, manned aircraft, and even flying cars. Airspace traffic management departments urgently need to establish a real-time monitoring system similar to "traffic police" to achieve "manageability and control," clearly defining "who is flying, when, and where" to ensure low-altitude flight safety.

[0004] Existing drone monitoring technologies mainly include active radar monitoring, optical imaging monitoring, infrared monitoring, and acoustic array monitoring. Active radar systems detect targets by emitting electromagnetic waves and receiving echoes, offering strong real-time performance and authority. However, these systems are expensive, energy-intensive, and susceptible to multipath interference and ground clutter in complex urban environments. Optical and infrared monitoring are limited by weather conditions, light intensity, and line-of-sight range, resulting in limited ability to identify small, low-reflectivity targets. Acoustic monitoring is easily affected by environmental noise, has limited coverage, and struggles to achieve large-scale continuous monitoring. Especially for low-altitude, slow-moving, small-sized drone swarms, traditional active or multi-sensor fusion solutions are insufficient in terms of cost, coverage, and stealth.

[0005] In recent years, passive radar technology has gradually attracted attention. This technology utilizes existing broadcast signals in the environment (such as FM radio and digital television signals) as illumination sources. It does not require active electromagnetic wave emission; instead, it estimates the target's azimuth and range by receiving the reflection of the broadcast signal from the target and performing coherent processing. FM radio signals are stable in frequency, have a wide coverage area, and high transmission power, and possess good continuous coverage characteristics in urban and suburban areas, making them suitable as illumination sources for passive detection. Compared to active radar systems, passive radar has advantages such as simplified system structure, strong concealment, and low deployment costs, and shows promising application prospects in the field of low-altitude target monitoring.

[0006] In summary, the existing technology has at least the following technical problems: Existing drone monitoring technologies suffer from several problems, including difficulty in large-scale deployment in urban low-altitude airspace, significant influence of environmental factors on monitoring methods, insufficient continuous and stable detection capability for low-altitude, slow-speed, and small-sized drone swarms, and a lack of technical mechanisms for high-precision joint estimation of azimuth and distance for drone swarm targets. Summary of the Invention

[0007] The purpose of this invention is to provide a drone swarm azimuth and distance monitoring system based on broadcast signal reflection, in order to solve the technical problems of existing drone monitoring technology, such as difficulty in large-scale deployment in urban low-altitude airspace, significant influence of environmental factors on monitoring methods, insufficient continuous and stable detection capability for low-altitude, slow-speed, small-sized drone swarms, and lack of a high-precision joint azimuth and distance estimation mechanism for drone swarm targets.

[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 location and distance monitoring system based on broadcast signal reflection, comprising: an illumination source selection unit for selecting an FM radio station covering the monitoring area as a non-cooperative illumination source; a reference receiving channel, including a first antenna and a first receiving link, for receiving the direct broadcast signal from the FM radio station and outputting a reference baseband signal; a monitoring receiving channel, including a second antenna and a second receiving link, for receiving a monitoring signal containing the echo reflected by the drone swarm and outputting a monitoring baseband signal; a frequency conversion acquisition unit for down-converting the received signals of the reference receiving channel and the monitoring receiving channel under the action of the same local oscillator, and synchronously sampling the two down-converted signals to obtain the reference baseband signal and the monitoring baseband signal; and a filtering estimation unit for performing cross-correlation and / or matched filtering on the reference baseband signal and the monitoring baseband signal. The system is configured to: extract the time delay difference between the two targets and convert the time delay difference into a bistatic distance difference; establish a bistatic positioning model based on the transmitter position of the FM radio station, the reference receiving channel position, and the monitoring receiving channel position, and generate a set of candidate solutions for the target position in each pointing sector of the second antenna; calculate the residual between the predicted bistatic distance difference and the bistatic distance difference for each candidate solution; select the candidate solution with the smallest residual that satisfies the preset altitude range constraint and / or velocity continuity constraint as the target position solution, and output the azimuth and distance of the UAV swarm target accordingly, while also outputting the residual and / or confidence level; and detect and track multiple targets in the time-delay-Doppler domain, and perform data association and trajectory tracking in combination with the residual and / or the confidence level, outputting a list of UAV swarm targets and trajectory information.

[0010] In one embodiment, the frequency conversion acquisition unit includes: a local oscillator, a mixer and intermediate frequency link respectively connected to the first receiving link and the second receiving link, and a dual-channel synchronous sampling module; wherein, the dual-channel synchronous sampling module is a software-defined radio dual-channel synchronous sampling module or an equivalent implementation, used to ensure time alignment of the two sampling paths.

[0011] In one embodiment, the UAV swarm orientation and distance monitoring system further includes a signal synchronization and phase drift compensation unit; the signal synchronization and phase drift compensation unit includes providing a unified reference clock to the same local oscillator and the dual-channel synchronous sampling module to ensure the synchronization of the reference baseband signal and the monitoring baseband signal on the time axis.

[0012] In one embodiment, the second antenna is a directional antenna or a directional array antenna, and the second antenna is configured to cover multiple azimuth sectors by mechanical scanning or electronic scanning; and the UAV swarm azimuth and distance monitoring system is configured to: for each azimuth sector, at least the filtering estimation unit generates a corresponding delay-Doppler two-dimensional detection statistic, and / or generates a distance-azimuth detection result.

[0013] In one embodiment, the UAV swarm orientation and distance monitoring system further includes a reference coherence correction module; the reference coherence correction module constructs weighted coefficients based on the reference baseband signal within a preset sliding time window, and applies the weighted coefficients to the monitoring baseband signal to achieve phase correction and / or amplitude normalization processing; wherein, the phase component of the weighted coefficients is used to cancel common phase drift, and the amplitude component of the weighted coefficients is used to cancel common gain drift.

[0014] In one embodiment, the reference coherence correction module is further configured to: within the sliding time window, perform coherent fixed component estimation based on the reference baseband signal corrected by the weighted coefficients and the monitoring baseband signal corrected by the weighted coefficients to obtain estimated signals of the direct wave leakage component and / or stable multipath component coherent with the reference baseband signal in the monitoring baseband signal, and cancel the estimated signals from the monitoring baseband signal to suppress the influence of direct leakage and stable multipath clutter on UAV swarm echo detection; wherein, the coherent fixed component estimation includes one or more estimation methods based on least squares fitting, projection elimination, or adaptive filtering.

[0015] In one embodiment, the filtering estimation unit further includes a Doppler processing subunit, which performs Doppler spectrum estimation on the monitoring baseband signal to form a time-delay-Doppler two-dimensional detection statistic, and uses a Doppler threshold and / or a dynamic-static separation strategy to suppress false alarms introduced by static scatterers.

[0016] In one embodiment, the detection and tracking unit employs a super-resolution parameter estimation algorithm to separate multiple peaks in the time-delay-Doppler domain, thereby improving the range resolution and / or velocity resolution of multiple targets within the UAV swarm; the super-resolution parameter estimation algorithm includes MUSIC, ESPRIT, sparse reconstruction, or a combination thereof.

[0017] In one embodiment, the detection and tracking unit includes a data association module and a filter bank; the data association module is used to perform track initiation, track maintenance and track termination, and employs JPDA, MHT or a combination thereof; the filter bank employs one or more of Kalman filtering, extended Kalman filtering, unscented Kalman filtering or particle filtering to output a smooth track of the UAV swarm target.

[0018] In one embodiment, the UAV swarm location and distance monitoring system further includes a visualization and remote communication unit; the visualization and remote communication unit is used to display the location, distance and trajectory of the UAV swarm target on the airspace situation interface and output alarm information, and communicate with the monitoring center through a wired network or wireless network to realize remote monitoring and data playback.

[0019] The beneficial effects of this invention are as follows: (1) Passive detection architecture based on broadcast signals is suitable for large-scale, low-cost deployment. This technical solution utilizes widely covered and frequency-stable FM radio stations as a non-cooperative illumination source, eliminating the need for self-emitted electromagnetic signals. This avoids the high power consumption, high cost, and spectrum occupation issues of traditional active radar systems, resulting in a relatively simplified system structure and significantly reduced deployment costs. Because FM radio has a natural coverage advantage in urban low-altitude airspace, this solution can build a monitoring network without constructing additional transmission infrastructure, making it particularly suitable for large-scale deployment in urban low-altitude airspace, thus solving the problem of the difficulty in large-scale deployment of existing active radar systems.

[0020] (2) Combining bistatic distance difference measurement with sector candidate solution constraints improves the positioning accuracy of low-altitude weak targets. This technical solution extracts the time delay difference by cross-correlation or matched filtering of the reference baseband signal and the monitoring baseband signal, and converts it into a bistatic distance difference to measure the target's distance difference. Based on this, and combined with the pointing sector constraint of the second antenna, a candidate solution set is constructed, and position optimization is performed by minimizing the residual. This allows target positioning to not rely on a single measurement, but to achieve joint calculation of azimuth and range under geometric model constraints. Through the dual constraint mechanism of "candidate solution generation + residual optimization," the positioning stability and accuracy of low-altitude, slow-speed, small-sized UAV targets are effectively improved, overcoming the positioning instability problem caused by relying solely on a single time delay or azimuth estimation in existing passive detection schemes.

[0021] (3) Delayed-Doppler domain detection and swarm target tracking mechanism to enhance the continuous detection capability of UAV swarms. This technical solution detects multiple targets in the time-delay-Doppler domain and combines residuals and / or confidence levels for data association and trajectory tracking, enabling multi-target detection and trajectory management of UAV swarms. By associating and filtering continuous frame data of multiple targets, it effectively suppresses instantaneous false alarms and target jumps, improving the continuous and stable detection capability of UAV swarms and meeting the needs of low-altitude traffic supervision and swarm target monitoring.

[0022] (4) Coherent correction and fixed component cancellation mechanism to improve anti-interference capability in complex urban environments. This technical solution incorporates a reference coherent correction module, constructing weighted coefficients within a sliding time window to perform phase and amplitude correction on the monitored baseband signal, thereby eliminating common phase drift and gain drift. Furthermore, a coherent fixed component estimation and cancellation mechanism suppresses direct wave leakage components and stable multipath components. These mechanisms significantly reduce the overwhelming effect of direct leakage and stable multipath clutter generated by reflections from urban buildings on the UAV echo signal, improving the detectability of weak targets in complex electromagnetic environments and enhancing the system's robustness in urban low-altitude environments.

[0023] (5) It has both engineering feasibility and scalability. This technical solution ensures synchronization between the reference channel and the monitoring channel on the time axis by using the same local oscillator and a unified reference clock, thereby reducing time delay estimation errors. At the same time, the system has a modular structure, which facilitates integration with visualization platforms and remote monitoring systems. It is suitable for various application scenarios such as electronic fence construction, low-altitude traffic monitoring, and protection of critical areas.

[0024] In summary, this technical solution, while ensuring system concealment and low cost, achieves high-precision joint estimation of azimuth and distance for low-altitude UAV swarms and continuous tracking, significantly improving the manageability and controllability of urban low-altitude airspace. 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 location and distance monitoring system of the present invention; Figure 2 This is the second schematic diagram of the structural composition of the UAV swarm orientation and distance monitoring system of the present invention; Figure 3 This is the third schematic diagram of the structural composition of the UAV swarm orientation and distance monitoring system of the present invention; Figure 4 This is the fourth schematic diagram of the structural composition of the UAV swarm orientation and distance monitoring system of the present invention; Figure 5 This is the fifth schematic diagram of the structural composition of the UAV swarm orientation and distance monitoring system of the present invention; Figure 6 This is the sixth schematic diagram of the structural composition of the UAV swarm orientation and distance monitoring system of the present invention; Figure 7This is a schematic diagram of the structural composition of the UAV swarm orientation and distance monitoring system according to the second embodiment of the present invention.

[0027] The reference numerals in the attached figures are as follows: 1. Unmanned aerial vehicle (UAV) swarm location and distance monitoring system; 2. Irradiation source selection unit; 3. Reference receiving channel; 31. First antenna; 32. First receiving link; 4. Monitoring and receiving channel; 41. Second antenna; 42. Second receiving link; 5. Variable frequency acquisition unit; 51. Same local oscillator; 52. Mixer and intermediate frequency link; 53. Dual-channel synchronous sampling module; 6. Filtering estimation unit; 61. Doppler processing subunit; 7. Azimuth and distance calculation unit; 8. Detection and tracking unit; 81. Data association module; 82. Filter bank; 91. Signal synchronization and phase drift compensation unit; 92. Reference coherence correction module; 93. Visualization and remote communication unit. 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] This specific implementation provides a UAV swarm azimuth and range monitoring system based on broadcast signal reflection. The system includes an illumination source selection unit, a reference receiving channel, a monitoring receiving channel, a frequency conversion acquisition unit, a filtering estimation unit, an azimuth and range calculation unit, and a detection and tracking unit. The system selects an FM radio station as a non-cooperative illumination source, acquires the direct signal through the reference channel, and acquires the reflected signal of the UAV swarm through the monitoring channel. Under the action of the same local oscillator, it completes dual-channel down-conversion and synchronous sampling. The system extracts the time delay difference through cross-correlation and converts it into bistatic distance difference. Under the constraint of antenna pointing sector, candidate solutions are generated and residual optimization is performed to achieve joint azimuth and range calculation. Multi-target detection and tracking are performed in the time-delay-Doppler domain. It is suitable for continuous monitoring of UAV swarms in urban low-altitude airspace. It effectively solves the technical problems of existing UAV monitoring technologies, such as difficulty in large-scale deployment in urban low-altitude airspace, significant influence of environmental factors on monitoring methods, insufficient continuous and stable detection capability for low-altitude, slow-speed, and small-sized UAV swarms, and lack of a high-precision joint azimuth and range estimation mechanism for UAV swarm targets.

[0030] The first implementation of a drone swarm location and distance monitoring system, for example Figure 1As shown, the system includes an illumination source selection unit 2 for selecting an FM radio station covering the monitoring area as a non-cooperative illumination source; a reference receiving channel 3, including a first antenna 31 and a first receiving link 32, for receiving direct broadcast signals from the FM radio station and outputting a reference baseband signal; a monitoring receiving channel 4, including a second antenna 41 and a second receiving link 42, for receiving monitoring signals including echoes reflected from a drone swarm and outputting a monitoring baseband signal; a frequency conversion acquisition unit 5 for down-converting the received signals from the reference receiving channel 3 and the monitoring receiving channel 4 under the same local oscillator, and synchronously sampling the two down-converted signals to obtain the reference baseband signal and the monitoring baseband signal; and a filtering estimation unit 6 for performing cross-correlation and / or matched filtering on the reference baseband signal and the monitoring baseband signal to extract the time delay difference Δτ between them, and then estimating the Δτ. τ is converted to the bistatic distance difference ΔR = c·Δτ, where c is the electromagnetic wave propagation speed; and the azimuth distance calculation unit 7 is used to establish a bistatic positioning model based on the transmitter position of the FM radio station, the position of the reference receiving channel 3 and the position of the monitoring receiving channel 4, and generate a set of candidate solutions for the target position in each pointing sector of the second antenna 41; calculate the residual between the predicted bistatic distance difference and the bistatic distance difference ΔR for each candidate solution; select the candidate solution with the smallest residual that satisfies the preset altitude range constraint and / or velocity continuity constraint as the target position solution, and output the azimuth angle θ and distance R of the UAV swarm target accordingly, and output the residual and / or confidence level; the detection and tracking unit 8 is used to detect multiple targets in the time-delay-Doppler domain, and perform data association and trajectory tracking by combining the residual and / or confidence level, and output the list of UAV swarm targets and track information.

[0031] Specifically, compared with existing UAV monitoring technologies, this technical solution addresses the challenges of large-scale deployment in urban low-altitude airspace, significant susceptibility to environmental factors, insufficient continuous and stable detection capabilities for low-altitude, slow-moving, and small-sized UAV swarms, and the lack of a high-precision joint estimation mechanism for UAV swarm targets. It proposes a UAV swarm azimuth and distance monitoring system based on broadcast signal reflection1, offering several advantages: The passive detection architecture based on broadcast signals is suitable for large-scale, low-cost deployment; this solution utilizes widely covered and frequency-stable FM radio stations as non-cooperative illumination sources, eliminating the need for self-emission of electromagnetic signals and avoiding the high power consumption, high cost, and spectrum occupancy issues of traditional active radar systems, resulting in a relatively simplified system structure and significantly reduced deployment costs. Furthermore, due to the natural coverage advantage of FM radio in urban low-altitude airspace, this solution enables the construction of a monitoring network without additional transmission infrastructure, making it particularly suitable for large-scale deployment in urban low-altitude airspace, thus solving the problem of the difficulty in large-scale deployment of existing active radar systems.

[0032] This technical solution improves the positioning accuracy of low-altitude, small targets by combining bistatic distance difference measurement with sector candidate solution constraints. It extracts the time delay difference Δτ by cross-correlation or matched filtering of the reference baseband signal and the monitoring baseband signal, and converts it into a bistatic distance difference ΔR to measure the target's distance difference. Based on this, and combined with the pointing sector constraint of the second antenna 41, a candidate solution set is constructed, and position optimization is performed by minimizing the residuals. This allows target positioning to not rely on a single measurement quantity, but rather achieve joint calculation of azimuth and range under geometric model constraints. Through the dual constraint mechanism of "candidate solution generation + residual optimization," the positioning stability and accuracy of low-altitude, slow-speed, small-sized UAV targets are effectively improved, overcoming the positioning instability problem caused by relying solely on a single time delay or azimuth estimation in existing passive detection schemes.

[0033] A time-delayed Doppler domain detection and swarm target tracking mechanism enhances the continuous detection capability of UAV swarms. This technical solution detects multiple targets in the time-delayed Doppler domain and combines residuals and / or confidence levels for data association and trajectory tracking, achieving multi-target detection and trajectory management for UAV swarms. By associating and filtering continuous frame data of multiple targets, it effectively suppresses instantaneous false alarms and target jumps, improving the continuous and stable detection capability of UAV swarms and meeting the needs of low-altitude traffic supervision and swarm target monitoring.

[0034] Coherent correction and fixed component cancellation mechanisms enhance anti-interference capabilities in complex urban environments. This technical solution employs a reference coherent correction module 92, which constructs weighted coefficients within a sliding time window to perform phase and amplitude correction on the monitored baseband signal, eliminating common phase drift and gain drift. Furthermore, a coherent fixed component estimation and cancellation mechanism suppresses direct wave leakage components and stable multipath components. These mechanisms significantly reduce the submersion effect of direct leakage and stable multipath clutter generated by reflections from urban buildings on the UAV echo signal, improving the detectability of weak targets in complex electromagnetic environments and enhancing the system's robustness in urban low-altitude environments.

[0035] It combines engineering feasibility and scalability; the design of the same local oscillator and unified reference clock ensures the synchronization of the reference channel and the monitoring channel on the time axis, reducing the time delay estimation error; at the same time, the system structure is modular, which is easy to integrate with the visualization platform and remote monitoring system, and is suitable for a variety of application scenarios such as electronic fence construction, low-altitude traffic supervision and critical area protection.

[0036] In summary, this technical solution, while ensuring system concealment and low cost, achieves high-precision joint estimation of azimuth and distance for low-altitude UAV swarms and continuous tracking, significantly improving the manageability and controllability of urban low-altitude airspace.

[0037] As one alternative implementation method: The azimuth-distance joint calculation of the aforementioned azimuth-distance calculation unit 7 is as follows: The system first obtains the location of the FM radio station, the illumination source. S Position of reference receiving channel 3 R Position of monitoring receiving channel 4 M (These can be latitude and longitude + elevation or ECEF coordinates), and the reference baseband signal is output by reference receiving channel 3. Monitoring and receiving channel 4 outputs monitoring baseband signal .

[0038] When applied, the filter estimation unit 6 uses the reference baseband signal. With monitoring baseband signal Perform cross-correlation / matched filtering to obtain the time delay difference Δτ corresponding to the peak value, and then calculate the bistatic distance difference: ΔR = c·Δτ; where c is the electromagnetic wave propagation speed. To improve stability, the time delay difference Δτ within multiple consecutive sliding time windows can be robustly estimated using median / weighted average.

[0039] Regarding the generation of the aforementioned candidate solution set, the second antenna 41 operates in a certain directional sector. At this time, the sector's azimuth is used as the azimuth observation constraint. The system can generate the candidate solution set Ω using at least one of the following methods: Method A, Height-Layered Grid Method: Within a preset height range Several height levels are set inside. (For example, one layer every 5–20m), and within the pointing sector, along the ray direction, at a horizontal distance from the target. r Perform discrete sampling (e.g., 5–50m per step) to generate candidate points: ; in, For direction The horizontal unit vector, It is a vertical unit vector.

[0040] Method B, Sector Ray + Equal Distance Curve Intersection Method: This method involves intersecting several ray directions within a sector. As candidate orientations, construct points along each ray. And use the equal distance difference constraint to filter those that satisfy: The points are taken as the candidate solution set.

[0041] Method C, Particle Sampling / Random Search Method: Randomly sample several particle points within the sector and height range as candidate solutions, and then use residual functions to iteratively converge (such as particle swarm optimization / stochastic gradient descent) to improve robustness in multi-objective and multi-peak scenarios.

[0042] Regarding the above residual calculation and optimization, for each candidate solution Calculate the predicted bibase distance difference: ; Define residual:

[0043] Among the candidate solutions that satisfy the constraints, the one with the smallest residual is selected as the location solution:

[0044] Output: Azimuth (Or use the center angle / maximum response angle of the scanned sector); Target distance (Slope distance) or its horizontal distance; Confidence level / quality factor:

[0045] in, This is an empirical scale parameter.

[0046] Specifically, the constraints are: Height range constraints: It can be set according to the application scenario (such as the urban low-altitude monitoring height zone), such as 0–300m, 0–500m or 30–200m, etc.

[0047] Velocity continuity constraint: Solving position for consecutive frames ,Require ( T For frame period, (Measurements can be in the range of 10–50 m / s). If the value exceeds this range, the signal will be downweighted or removed to reduce false alarm jumps.

[0048] Sector consistency constraint: The azimuth of the candidate solution must fall within the current sector or its neighboring sectors to be consistent with the directional antenna scanning strategy.

[0049] Regarding the specific structure and working principle of the aforementioned frequency conversion acquisition unit 5, this implementation is as follows: Figure 2 As shown, the frequency conversion acquisition unit 5 includes: the same local oscillator 51, a mixer and intermediate frequency link 52 connected to the first receiving link 32 and the second receiving link 42 respectively, and a dual-channel synchronous sampling module 53; wherein, the dual-channel synchronous sampling module 53 is a software defined radio (SDR) dual-channel synchronous sampling module 53 or an equivalent implementation, used to ensure the time alignment of the two sampling channels.

[0050] Furthermore, such as Figure 3As shown, the UAV swarm orientation and distance monitoring system 1 also includes a signal synchronization and phase drift compensation unit 91; the signal synchronization and phase drift compensation unit 91 includes providing a unified reference clock to the same local oscillator 51 and the dual-channel synchronous sampling module 53 to ensure the synchronization of the reference baseband signal and the monitoring baseband signal on the time axis.

[0051] When applied, the frequency conversion acquisition unit 5 performs unified down-conversion on the radio frequency signals of the reference receiving channel 3 and the monitoring receiving channel 4 through the same local oscillator 51, so that the two signals have a consistent frequency reference in the frequency domain; the dual-channel synchronous sampling module 53 completes synchronous digital sampling under the control of a unified sampling clock, thereby ensuring strict alignment of the reference baseband signal and the monitoring baseband signal on the time axis.

[0052] Furthermore, the signal synchronization and phase drift compensation unit 91 provides a unified reference clock to the same local oscillator 51 and the dual-channel synchronous sampling module 53, so that the system can maintain frequency and time consistency during long-term operation and avoid delay estimation errors introduced by sampling clock drift.

[0053] The aforementioned structure works in synergy with the subsequent filtering estimation unit 6 and azimuth distance calculation unit 7: if sampling is not synchronized, the Δτ estimation will drift over time, directly affecting the accuracy of the bistatic distance difference ΔR; by unifying the reference clock and synchronizing sampling, the Δτ estimation error is significantly reduced, the distance difference measurement accuracy is improved, and stable input data is provided for minimizing the residual of subsequent candidate solutions. This solves the problem of unstable positioning caused by dual-channel time drift in existing passive monitoring systems.

[0054] A unified reference clock can be a temperature-compensated crystal oscillator (TCXO), an oven-controlled crystal oscillator (OCXO), or a GPS-locked clock (GPSDO). In distributed deployment scenarios, multi-site synchronization can be achieved through an external 10MHz reference signal or a timestamp alignment mechanism. Dual-channel sampling can employ a shared ADC architecture or a multi-ADC shared clock architecture.

[0055] In practice, the sampling synchronization error is preferably controlled at the sub-sampling period level to ensure that the Δτ measurement error is less than the system distance resolution requirement; if there is a slight deviation, it can be further corrected by digital resampling or phase compensation algorithm.

[0056] Regarding the specific configuration of the aforementioned monitoring and receiving channel 4 and the working principle of the filtering and estimation unit 6, this implementation is as follows: Figure 1As shown, the second antenna 41 is a directional antenna or a directional array antenna, and the second antenna 41 is configured to cover multiple azimuth sectors by mechanical scanning or electronic scanning; and the UAV swarm azimuth and distance monitoring system 1 is configured to: for each azimuth sector, at least the filter estimation unit 6 generates the corresponding delay-Doppler two-dimensional detection statistics, and / or generates the distance-azimuth detection results.

[0057] In application, the second antenna 41 sequentially covers multiple azimuth sectors via mechanical or electronic scanning, enabling the system to spatially partition the monitoring area. Within each sector, the filtering estimation unit 6 performs time-delay-Doppler processing on the monitoring baseband signal within the corresponding time window, generating two-dimensional detection statistics or range-azimuth detection results. This sectorization processing method works in conjunction with the azimuth-range calculation unit 7: sector direction information serves as a geometric constraint input to the candidate solution generation process, thereby narrowing the solution space and improving the efficiency and stability of residual selection; it solves the problem of significant positioning ambiguity in traditional passive radar without clear azimuth constraints, and improves the spatial resolution capability of low-altitude UAV targets.

[0058] The directional array antenna can adopt a phased array, digital beamforming, or multi-channel array structure; the sector width can be set to 5°–30° according to the monitoring accuracy requirements.

[0059] In electronic scanning mode, multiple virtual beams are generated in parallel using a digital beamforming algorithm to improve scanning efficiency; in mechanical scanning mode, the scanning cycle is set to match the length of the delay-Doppler processing window.

[0060] Regarding the specific structure and working principle of the aforementioned filter estimation unit 6, this embodiment, for example... Figure 4 As shown, the filtering estimation unit 6 also includes a Doppler processing subunit 61, which is used to perform Doppler spectrum estimation on the monitoring baseband signal, form a time-delay-Doppler two-dimensional detection statistic, and use the Doppler threshold and / or dynamic-static separation strategy to suppress false alarms introduced by static scatterers.

[0061] In application, the filtering estimation unit 6 first performs cross-correlation or matched filtering on the reference baseband signal and the monitoring baseband signal to extract Δτ; subsequently, the Doppler processing subunit 61 performs frequency shift estimation on the monitoring baseband signal to form a delay-Doppler two-dimensional statistic. By setting a Doppler threshold and a dynamic-static separation strategy, the zero Doppler component caused by ground buildings or fixed scatterers is suppressed, thereby highlighting the echoes of low-speed or slightly moving targets. This processing flow works in conjunction with the detection and tracking unit 8: the two-dimensional statistic serves as the basic input for group target detection, and the Doppler information provides velocity observations for subsequent track filtering. This improves the detectability of low-altitude, slow-speed UAVs in complex backgrounds.

[0062] Among them, Doppler estimation can be performed using Fast Fourier Transform (FFT), sliding window spectral estimation, or adaptive frequency estimation methods; the threshold can be achieved using a constant false alarm rate (CFAR) strategy.

[0063] The time window length of the delay-Doppler two-dimensional matrix can be set according to the typical speed range of the UAV; for example, the window length is matched with the maximum expected Doppler frequency shift.

[0064] Regarding the working principle of the detection and tracking unit 8, the detection and tracking unit 8 adopts a super-resolution parameter estimation algorithm to separate multiple peaks in the delay-Doppler domain in order to improve the range resolution and / or velocity resolution of multiple targets in the UAV swarm. The super-resolution parameter estimation algorithm includes MUSIC (Multiple Signal Classification), ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques), sparse reconstruction, or a combination thereof.

[0065] In application, the detection and tracking unit 8 separates multiple peaks in the delay-Doppler domain and employs MUSIC, ESPRIT, or sparse reconstruction algorithms to improve the resolution between adjacent targets. This super-resolution processing enhances the multi-target discrimination capability in dense UAV swarm flight scenarios. In conjunction with the residual output of the azimuth and distance calculation unit 7, it avoids mutual interference between different targets in the solution space and solves the target aliasing problem caused by insufficient resolution of traditional FFT in dense swarm scenarios.

[0066] The super-resolution algorithm can be implemented offline or in real-time depending on computing resources, and can also be accelerated using GPUs. Noise subspace estimation can be performed before peak separation to improve algorithm stability.

[0067] Regarding the specific structure and working principle of the aforementioned detection and tracking unit 8, this embodiment, for example... Figure 5 As shown, the detection and tracking unit 8 includes a data association module 81 and a filter bank 82. The data association module 81 is used to perform track initiation, track maintenance and track termination, and adopts JPDA (Joint Probabilistic Data Association), MHT (Multiple Hypothesis Tracking) or a combination thereof. The filter bank 82 adopts one or more of Kalman filtering, extended Kalman filtering, unscented Kalman filtering or particle filtering to output a smooth track of the UAV swarm target.

[0068] In application, the data association module 81 performs threshold screening of candidate location solutions based on residuals or confidence levels, and performs JPDA or MHT association in conjunction with Doppler information; the filter bank 82 performs smooth estimation of target states in consecutive frames. The residuals / confidence levels serve as association weights, allowing positioning quality to directly participate in track determination, improving the stability of continuous tracking of group targets; and solving the problems of track jumps and false alarms / mis-associations in low signal-to-noise ratio environments.

[0069] The filter can be selected as a uniform velocity or uniform acceleration model according to the motion model, and height state variables can be added. The residual threshold can be set according to the statistical distribution of system error.

[0070] Regarding the visualization monitoring principle of the aforementioned UAV swarm location and distance monitoring system 1 for UAV swarms, this implementation is as follows: Figure 6 As shown, the UAV swarm location and distance monitoring system 1 also includes a visualization and remote communication unit 93; the visualization and remote communication unit 93 is used to display the location, distance and trajectory of the UAV swarm target on the airspace situation interface and output alarm information, and communicate with the monitoring center through a wired network or wireless network to realize remote monitoring and data playback.

[0071] When applied, the visualization unit overlays azimuth, distance, and flight path information onto the electronic map or airspace situation map, and combines alarm logic to issue alerts for illegal intrusions or abnormal trajectories; the remote communication unit enables real-time data uploading and historical data playback; this module works in conjunction with the detection and tracking unit 8 to realize electronic fence and low-altitude traffic monitoring functions.

[0072] The communication methods can include 5G, fiber optics, or private networks, thereby supporting multi-site integrated display, historical trajectory tracing, and statistical analysis.

[0073] The second implementation of the drone swarm location and distance monitoring system is as follows: Figure 7 As shown, the difference between this embodiment and the first embodiment is that the UAV swarm orientation and distance monitoring system 1 further includes a reference coherence correction module 92; the reference coherence correction module 92 constructs a weighted coefficient based on the reference baseband signal within a preset sliding time window, and applies the weighted coefficient to the monitoring baseband signal 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.

[0074] Furthermore, the reference coherence correction module 92 is also configured to: within a sliding time window, perform coherent fixed component estimation based on the reference baseband signal corrected by the weighted coefficients and the monitoring baseband signal corrected by the weighted coefficients, to obtain estimated signals of the direct wave leakage component and / or stable multipath component coherent with the reference baseband signal in the monitoring baseband signal, and cancel the estimated signals from the monitoring baseband signal to suppress the influence of direct leakage and stable multipath clutter on the UAV swarm echo detection; wherein, the coherent fixed component estimation includes one or more estimation methods based on least squares fitting, projection elimination or adaptive filtering.

[0075] Regarding the feasible parameter range for the aforementioned reference coherence correction + coherent fixed component estimation / cancellation, the reference coherence correction module 92 uses a sliding time window to perform common drift suppression and direct leakage / stable multipath cancellation on the reference baseband signal and the monitoring baseband signal, specifically including: (1) Sliding window and update cycle: Sliding window length The timeframe can be 20ms–500ms (e.g., 50ms, 100ms, 200ms) to balance drift tracking and computational load. Update cycle The timeframe can be 5ms–100ms, with less than 5ms being preferred. (For example, the adjusted coefficients are updated every 10ms).

[0076] (2) Construction of weighted coefficients Calculate the weighted coefficients within the window. This aligns the reference channel and the monitoring channel in a common phase / gain. One of the following equivalent implementations can be used: Normalized coefficients based on complex correlation: ,in The inner product of the window; or Decomposed into phase components With amplitude components These are used to offset common phase drift and gain drift, respectively.

[0077] The baseband signal is calibrated as follows: .

[0078] (3) Estimation and cancellation of coherent fixed components Monitor baseband signal within the window Intermediate and reference baseband signals The coherent fixed components are estimated to obtain the estimated signal. And offset: ; in It can be obtained by least squares fitting, projection cancellation, or adaptive filtering (such as LMS / RLS). The canceled signal Then, it is fed into delayed-Doppler processing and multi-target detection, thereby significantly reducing the submersion effect of direct leakage and stable multipath on the weak echo of the UAV swarm.

[0079] In application, the reference coherent correction module 92 constructs weighted coefficients within a sliding time window to perform phase and amplitude correction on the monitored baseband signal to eliminate common drift. Subsequently, coherent fixed component estimation is performed on the corrected signal to obtain the direct wave leakage component and stable multipath component, which are then canceled out. This module works in conjunction with the filtering estimation unit 6 to reduce the strong clutter smothering effect and improve the signal-to-noise ratio of weak UAV echoes. At the same time, it improves the peak contrast of the delay-Doppler two-dimensional statistics, thereby solving the problem of direct leakage and stable reflection masking weak targets in complex urban multipath environments and significantly improving the system robustness.

[0080] The sliding window length can be set to 20ms–500ms; the estimation method can be least squares, adaptive filtering or projection elimination; the dynamic update period can be set; the weighted coefficients can be decomposed into phase components and amplitude components for separate processing; and the fixed components can be estimated and then canceled by signal subtraction or projection elimination.

[0081] 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 system for monitoring the location and distance of a swarm of unmanned aerial vehicles (UAVs) based on broadcast signal reflection, characterized in that, It includes an illumination source selection unit for selecting FM radio stations covering the monitoring area as non-cooperative illumination sources; A reference receiving channel, including a first antenna and a first receiving link, is used to receive the direct broadcast signal from the FM radio station and output a reference baseband signal. The monitoring receiving channel includes a second antenna and a second receiving link to receive monitoring signals containing echoes reflected from the UAV swarm and output monitoring baseband signals; The frequency conversion acquisition unit is used to down-convert the received signals of the reference receiving channel and the monitoring receiving channel under the action of the same local oscillator, and to synchronously sample the two signals after down-conversion to obtain the reference baseband signal and the monitoring baseband signal. The filtering estimation unit is used to perform cross-correlation and / or matched filtering on the reference baseband signal and the monitoring baseband signal to extract the time delay difference between the two and convert the time delay difference into a bistatic distance difference. The azimuth and distance calculation unit is used to establish a bistatic positioning model based on the transmitter position of the FM radio station, the reference receiving channel position, and the monitoring receiving channel position, and in each pointing sector of the second antenna: generate a set of candidate solutions for the target position; calculate the residual between the predicted bistatic distance difference and the bistatic distance difference for each candidate solution; select the candidate solution with the smallest residual that satisfies the preset altitude range constraint and / or velocity continuity constraint as the target position solution, and output the azimuth and distance of the UAV swarm target accordingly, while also outputting the residual and / or confidence level; The detection and tracking unit is used to detect multiple targets in the time-delay-Doppler domain, and perform data association and trajectory tracking by combining the residuals and / or the confidence scores, and output the list of targets and track information of the UAV swarm.

2. The UAV swarm location and distance monitoring system according to claim 1, characterized in that, The frequency conversion acquisition unit includes: a local oscillator, a mixer and intermediate frequency link respectively connected to the first receiving link and the second receiving link, and a dual-channel synchronous sampling module; The dual-channel synchronous sampling module is a software-defined radio dual-channel synchronous sampling module or an equivalent implementation, used to ensure time alignment of the two sampling paths.

3. The unmanned aerial vehicle (UAV) swarm location and distance monitoring system according to claim 2, characterized in that, The unmanned aerial vehicle swarm orientation and distance monitoring system also includes a signal synchronization and phase drift compensation unit; The signal synchronization and phase drift compensation unit includes providing a unified reference clock to the same local oscillator and the dual-channel synchronous sampling module to ensure the synchronization of the reference baseband signal and the monitoring baseband signal on the time axis.

4. The unmanned aerial vehicle (UAV) swarm location and distance monitoring system according to claim 1, characterized in that, The second antenna is a directional antenna or a directional array antenna, and the second antenna is configured to cover multiple azimuth sectors by mechanical scanning or electronic scanning; Furthermore, the UAV swarm orientation and distance monitoring system is configured such that, for each orientation sector, at least the filtering estimation unit generates a corresponding delay-Doppler two-dimensional detection statistic and / or generates a distance-or orientation detection result.

5. The unmanned aerial vehicle (UAV) swarm location and distance monitoring system according to claim 1, characterized in that, The unmanned aerial vehicle swarm orientation and distance monitoring system also includes a reference coherence correction module; Within a preset sliding time window, the reference coherence correction module constructs weighted coefficients based on the reference baseband signal and applies these weighted coefficients to the monitoring baseband signal to achieve phase correction and / or amplitude normalization. The phase component of the weighted coefficients is used to cancel common phase drift, and the amplitude component of the weighted coefficients is used to cancel common gain drift.

6. The unmanned aerial vehicle (UAV) swarm location and distance monitoring system according to claim 5, characterized in that, The reference coherence correction module is further configured to: within the sliding time window, perform coherent fixed component estimation based on the reference baseband signal corrected by the weighted coefficient and the monitoring baseband signal corrected by the weighted coefficient to obtain estimated signals of the direct wave leakage component and / or stable multipath component coherent with the reference baseband signal in the monitoring baseband signal, and cancel the estimated signals from the monitoring baseband signal to suppress the influence of direct leakage and stable multipath clutter on the echo detection of UAV swarm; The coherent fixed component estimation includes one or more estimation methods based on least squares fitting, projection elimination, or adaptive filtering.

7. The unmanned aerial vehicle (UAV) swarm location and distance monitoring system according to claim 1, characterized in that, The filtering estimation unit further includes a Doppler processing subunit, which performs Doppler spectrum estimation on the monitoring baseband signal to form a time-delay-Doppler two-dimensional detection statistic, and uses a Doppler threshold and / or a dynamic-static separation strategy to suppress false alarms introduced by static scatterers.

8. The unmanned aerial vehicle (UAV) swarm location and distance monitoring system according to claim 1, characterized in that, The detection and tracking unit employs a super-resolution parameter estimation algorithm to separate multiple peaks in the time-delay-Doppler domain, thereby improving the range resolution and / or velocity resolution of multiple targets within the UAV swarm. The super-resolution parameter estimation algorithm includes MUSIC, ESPRIT, sparse reconstruction, or a combination thereof.

9. The unmanned aerial vehicle (UAV) swarm location and distance monitoring system according to claim 1, characterized in that, The detection and tracking unit includes a data association module and a filter bank; The data association module is used to perform track initiation, track maintenance and track termination, and adopts JPDA, MHT or a combination thereof; The filter bank employs one or more of Kalman filtering, extended Kalman filtering, unscented Kalman filtering, or particle filtering to output a smooth trajectory for the UAV swarm target.

10. The unmanned aerial vehicle (UAV) swarm location and distance monitoring system according to claim 1, characterized in that, The drone swarm location and distance monitoring system also includes a visualization and remote communication unit; The visualization and remote communication unit is used to display the location, distance, and trajectory of the UAV swarm target on the airspace situation interface and output alarm information. It also communicates with the monitoring center via wired or wireless network to achieve remote monitoring and data playback.

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