Method and system for detecting and positioning unmanned aerial vehicle signal

Abstract

The invention provides an unmanned aerial vehicle signal detection and positioning method and system. The method comprises the following steps: detecting an unmanned aerial vehicle signal by using a plurality of receivers of a TDOA receiving station; when an unmanned aerial vehicle signal is detected, identifying the unmanned aerial vehicle signal, if the unmanned aerial vehicle signal is identified as a preset unmanned aerial vehicle signal, performing fragment selection on original data received by a receiver and then entering time difference operation, otherwise, directly entering time difference operation; fragment selection: performing self-correlation operation on the original data received by each receiver to obtain a self-correlation function; segmenting the autocorrelation function of each receiver into a plurality of segments according to the time slot length; effective fragments are screened from the multiple fragments through feature detection, and the effective fragments are used for participating in time difference operation. According to the technical scheme of the invention, for the preset unmanned aerial vehicle signal, useless information data is discarded through fragment selection, and effective fragments are selected, so that the calculation power is saved, the calculation efficiency is improved, and the positioning precision is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle detection, and in particular to a method and system for detecting and positioning unmanned aerial vehicle signals. BACKGROUND

[0002] With the rapid development of unmanned aerial vehicle technology, it has rapidly spread to commercial and civilian markets. The maturity of mainstream consumer drones, the simplicity of use, and the wide range of popular groups have brought convenience to users. However, the "non-compliant" use of such drones has also caused "black flight" behavior, which has brought risks and threats, and even triggered many safety problems. Not only does it pose a threat to people's lives and property, but it also threatens the safety of important low-altitude control no-fly zones and critical infrastructure.

[0003] Therefore, it is very important and urgent to effectively control unmanned aerial vehicles. The detection and positioning technology for various types of unmanned aerial vehicles is the cornerstone of unmanned aerial vehicle control, and is an important prerequisite for all control measures such as monitoring, countermeasures, and driving away of unmanned aerial vehicles. Only by accurately detecting and positioning unmanned aerial vehicles can we more effectively and more targetedly manage "anti-unmanned aerial vehicles" to protect airspace safety and personnel safety.

[0004] Current traditional means for unmanned aerial vehicle detection and positioning are mostly ground detection, such as radar detection, direction finding detection, and more advanced TDOA technology. TDOA technology for detection and positioning is a targeted breakthrough technology based on new unmanned aerial vehicle signal patterns, and is the best unmanned aerial vehicle detection and positioning method. It is also important in the field of "anti-unmanned aerial vehicles".

[0005] However, the existing TDOA technology also has some problems: (1) The mainstream consumer drones all work in the ISM frequency band, with 2.4GHz and 5.8GHz frequency bands being the main ones. The use of this frequency band by communication devices and signals is diverse, making the TDOA receiving device receive data composed of multiple signals when working in the 2.4GHz and 5.8GHz frequency bands. A large amount of the received data is invalid data, and the overall signal quality is poor. If the data received by the TDOA receiving device is directly used for time difference calculation, the correct time difference of the target unmanned aerial vehicle cannot be calculated due to the interference of various signals, thus an effective positioning cannot be given. In addition, there is considerable pressure on the data transmission and computing power of the TDOA system, which will also cause a large error between the TDOA detection and positioning result and the actual position, further affecting the accuracy of the TDOA time difference positioning algorithm and the positioning accuracy of the unmanned aerial vehicle position.

[0006] (2) When using TDOA for detection positioning, multi-station joint positioning is often needed, at least three stations are needed, and the actual erection often has a station spacing of 500m-2km, so that the interference signals received by the receivers of different stations and the occurrence time of the interference signals are different, thus the signal received by different stations of TDOA has large differences, and the signals at different times also have the problems of no effective positioning result or positioning error when calculating the time difference between the receivers. SUMMARY

[0007] The present application provides a kind of unmanned aerial vehicle signal detection positioning method and system, to solve the problems of poor positioning accuracy in prior art, cannot effectively position.

[0008] To solve the above technical problems, the present application is realized by the following technical solutions: According to the first aspect of the present application, a kind of unmanned aerial vehicle signal detection positioning method is provided, comprising: Detecting unmanned aerial vehicle signal using multiple receivers of TDOA receiving station; After detecting the unmanned aerial vehicle signal, identifying the unmanned aerial vehicle signal, if the unmanned aerial vehicle signal is identified as a preset unmanned aerial vehicle signal, then selecting a segment of the original data received by the receiver and entering time difference operation, otherwise directly entering time difference operation; The segment selection includes: Correlation operation is carried out on the original data received by each receiver to obtain an autocorrelation function; According to the time slot length, the autocorrelation function of each receiver is segmented into multiple segments; Utilize feature detection to screen effective segments in the multiple segments, and utilize the effective segments to participate in the time difference operation.

[0009] Optionally, the utilization of feature detection to screen effective segments in the multiple segments includes: Calculate the average power of each segment, and perform clustering operation on multiple segments of each receiver according to the size of average power to form a cluster combination of each segment; For the clustering result, perform superposition enhancement operation to obtain each type of segment enhancement result; For each type of segment enhancement result of each receiver, perform cyclic prefix autocorrelation feature detection to screen out the cluster category with the smallest matching error with the feature detection template; The segment at the position of the maximum value of the cyclic prefix autocorrelation feature detection in the cluster category with the smallest matching error is taken as the effective segment.

[0010] Optionally, the superposition enhancement operation is performed on the clustering result to obtain a segment enhancement result of each cluster, and the superposition enhancement operation specifically comprises: The superposition enhancement operation is performed on all segments of each cluster based on the clustering result to obtain a superposition result, and the superposition result is subjected to secondary superposition according to the symbol length and a modulus value is calculated to obtain a segment enhancement result of each cluster.

[0011] Optionally, the cyclic prefix autocorrelation feature detection is performed on the segment enhancement result of each cluster of each receiver, and a cluster category with the minimum matching error with the feature detection template is screened out, and the cyclic prefix autocorrelation feature detection specifically comprises: The position of the maximum value of each segment enhancement result of each cluster of each receiver is calculated, and a to-be-detected sequence near the maximum value is extracted, the matching error of the to-be-detected sequence and the feature detection template is calculated, and the cluster category with the minimum matching error is screened out.

[0012] Optionally, between the screening of the cluster category with the minimum matching error with the feature detection template and the selection of the segment at the position of the maximum value of the cyclic prefix autocorrelation feature detection in the cluster category with the minimum matching error as an effective segment, the method further comprises: It is judged whether the minimum matching error is less than a first matching error threshold value, if yes, the segment at the position of the maximum value of the cyclic prefix autocorrelation feature detection in the cluster category with the minimum matching error is selected as the effective segment; if no, the receiver cannot select an effective segment, and the data received by the receiver is discarded.

[0013] Optionally, after the effective segment is selected in the segment selection of the data received by the receiver, the time difference operation comprises: For each receiver, it is judged whether the minimum matching error is less than a second matching error threshold value, the second matching error threshold value being less than the first error matching threshold value; if yes, the position of the maximum value of the cyclic prefix autocorrelation feature detection of the two receivers is used to calculate the time difference; if no, the intersection of the effective segments of the two receivers and the generalized cross-correlation function of the original data received by the receiver are used to calculate the time difference.

[0014] Optionally, the preset unmanned aerial vehicle signal is a mainstream unmanned aerial vehicle signal.

[0015] According to a second aspect of the present application, a system for detecting and positioning an unmanned aerial vehicle signal is provided, and the system comprises: A signal detection module is configured to detect the unmanned aerial vehicle signal by using a plurality of receivers of a TDOA receiving station. The signal recognition module is used to identify the drone signal after it is detected. If the drone signal is identified as a preset drone signal, the module selects segments of the raw data received by the receiver and then enters the time difference calculation module. Otherwise, the module directly enters the time difference calculation module. The segment selection module includes: The autocorrelation operation unit is used to perform autocorrelation operation on the raw data received by each of the receivers to obtain the autocorrelation function; The segmentation unit is used to segment the autocorrelation function of each receiver into multiple segments according to the time slot length; The effective segment filtering unit is used to filter effective segments from the multiple segments using feature detection, and to use the effective segments to participate in time difference calculation. The time difference calculation module is used to perform time difference calculations.

[0016] Optionally, the effective fragment filtering unit includes: The segment clustering unit is used to calculate the average power of each segment, and to perform clustering operations on multiple segments of each receiver according to the magnitude of the average power to form a cluster combination of segments; The fragment enhancement unit is used to perform superposition enhancement operations on the clustering results to obtain the enhanced fragment results for each class. The feature detection unit is used to perform autocorrelation feature detection of the cyclic prefix for each type of segment enhancement result of each receiver, and to select the clustering category with the smallest matching error with the feature detection template. The effective fragment selection unit is used to select the fragment at the position of the maximum value of the autocorrelation feature detection of the cyclic prefix in the cluster category with the smallest matching error as an effective fragment.

[0017] Optionally, the effective segment filtering unit is further configured to: determine whether the minimum matching error is less than a first matching error threshold; if so, select the segment at the position of the maximum value of the autocorrelation feature detection of the cyclic prefix in the cluster category with the minimum matching error as an effective segment; otherwise, the receiver cannot select an effective segment and discards the data received by the receiver.

[0018] Optionally, when the time difference calculation module performs time difference calculation using the effective segments, it specifically performs the following: for each receiver, it determines whether the minimum matching error is less than a second matching error threshold, where the second matching error threshold is less than the first error matching threshold; if so, it uses the autocorrelation feature of the cyclic prefixes of the two receivers to detect the location of the maximum value to calculate the time difference; otherwise, it uses the intersection of the effective segments of the two receivers and the generalized cross-correlation function of the original data received by the receivers to calculate the time difference.

[0019] The UAV signal detection and positioning method and system provided by this invention can be used for mainstream UAVs that are widely used, have large amounts of data, and are complex. The received data is first segmented before time difference calculation is performed. The segment selection includes autocorrelation, segmentation, and feature detection. By segment selection, useless information data is discarded and effective segments are selected. Data that can accurately participate in time difference positioning can be selected for time difference calculation, which can save computing power, improve computing efficiency, and improve positioning accuracy, thus effectively improving the performance of UAV control.

[0020] In one alternative embodiment of the present invention, effective segments are selected by segment clustering, segment superposition enhancement, and then feature detection, resulting in more accurate and effective data selection.

[0021] In one optional embodiment of the present invention, a two-stage superposition enhancement method is adopted for segment superposition enhancement, which first uses the time slot period and then the symbol period. This avoids the problem of cumulative symbol period offset caused by the presence of an extended cyclic prefix in the seven OFDM symbols in one time slot.

[0022] In one optional embodiment of the present invention, a first matching error threshold is used as the segment detection threshold during the effective segment screening, which can further filter out some invalid data and further save computing power.

[0023] In an optional embodiment of the present invention, the signal quality is divided by using a second matching error threshold as a high confidence threshold during time difference calculation. Different time difference calculation methods are selected for different signal qualities, which further reduces the transmission and calculation pressure of the system and further improves the accuracy of time difference calculation. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0025] Figure 1 This is a flowchart of a method for detecting and locating drone signals according to an embodiment of the present invention; Figure 2 A schematic diagram of the composition of OFDM symbols within a time slot; Figure 3 This is a flowchart of a method for detecting and locating UAV signals according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a time difference calculation method according to an embodiment of the present invention; Figure 5This is a schematic diagram of a drone signal detection and positioning system according to an embodiment of the present invention; Figure 6 This is a schematic diagram of a drone signal detection and positioning system according to an embodiment of the present invention; Explanation of reference numerals in the attached figures: 11-Signal detection module; 12-Signal recognition module; 13-Segment selection module; 131 - Autocorrelation computation unit; 132-Segmentation Unit; 133 - Effective Fragment Filtering Unit; 1331 - Fragment Clustering Department; 1332 - Fragment Enhancement Section; 1333 - Feature Detection Department; 1334 - Effective Fragment Screening Department; 14-Time Difference Calculation Module. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] In the description of this invention, it should be understood that the terms "upper part", "lower part", "upper end", "lower end", "lower surface", "upper surface", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention.

[0028] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0029] In the description of this invention, "a plurality of" means multiple, such as two, three, four, etc., unless otherwise explicitly specified.

[0030] In the description of this invention, unless otherwise explicitly specified and limited, the term "connection" and other such terms should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral connection; it can refer to a mechanical connection, an electrical connection, or a connection that allows communication between the components; it can refer to a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0031] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0032] In one embodiment, a method for detecting and locating UAV signals is provided; please refer to [reference needed]. Figure 1 It includes: S11: Detect drone signals using multiple receivers at the TDOA receiving station.

[0033] A typical TDOA receiving station consists of 3 to M receivers used to receive in-phase quadrature (IQ) signals and form an IQ sequence according to the set sampling rate Fs and sequence length N.

[0034] S12: When a drone signal is detected, the drone signal is identified. If the drone signal is identified as a preset drone signal, the raw data received by the receiver is segmented and then the time difference calculation is performed. Otherwise, the time difference calculation is performed directly.

[0035] In practice, the preset drone signal can be a mainstream drone signal, such as the image transmission signal of DJI, Tongdao, and other drones.

[0036] Assuming there are M receivers, the IQ sequence received by the M receivers is defined as follows: .

[0037] Currently, mainstream drones such as DJI and Autel generally use OFDM modulation technology for image transmission. OFDM is a multi-carrier modulation method. When a signal is transmitted through multiple channels, it is unavoidable that different symbols arrive at the receiving end at different times due to the different channel lengths, causing inter-carrier interference—this is the inter-carrier interference caused by multipath effect. This can further lead to data loss and bit errors. Therefore, to effectively cancel and solve the inter-carrier interference and bit error rate caused by multipath channels, OFDM technology can insert guard time between OFDM symbols, and the guard time can be greater than the multipath delay spread to cancel it out. In practical applications, the signal at the end of an OFDM symbol is moved to the beginning of the symbol, and a cyclic spread signal is sent within the guard time. This is the cyclic prefix characteristic of OFDM signals. Cyclic prefixes are divided into regular cyclic prefixes and extended cyclic prefixes. The OFDM signal in each time slot is the same, exhibiting time slot periodicity and symbol periodicity overall. A typical 0.5ms time slot contains 7 OFDM symbols, with a symbol length usually of 66.7 µs. Six of these OFDM symbols use a regular cyclic prefix of 4.69 µs, and one symbol uses an extended cyclic prefix of 5.21 µs. Figure 2 As shown. Time slot length Number of OFDM symbols per time slot ; Regular cyclic prefix length Valid symbol length Total symbol length .

[0038] Specifically, segment selection includes: S21: Perform autocorrelation calculations on the raw data received by each receiver to obtain the autocorrelation function. .

[0039] Autocorrelation function The formula is as follows: (1.1) (1.2) in, m is a constant, referring to the m-th receiver number, and n is a constant, referring to the n-th sequence.

[0040] S22: Based on the time slot length Nslot, the autocorrelation function of each receiver is segmented into multiple segments, and the number of segments is... .

[0041] S23: Select valid segments from multiple segments and use the valid segments to participate in time difference calculation.

[0042] S13: Time difference calculation.

[0043] When a preset drone signal is detected, the time difference calculation involves performing a generalized cross-correlation (GCC) calculation on the selected valid segments and the original IQ data to obtain a cross-correlation function, and then calculating the time difference between the two devices. When a non-preset drone signal is detected, the time difference calculation involves performing a generalized cross-correlation (GCC) calculation on the original IQ data to obtain a cross-correlation function, and then calculating the time difference between the two devices.

[0044] In one implementation method, please refer to Figure 3 S23 filters valid segments from multiple segments, specifically including: S231: Calculate the average power of each segment The J segments from each receiver are clustered according to their average power, forming cluster combinations of segments. Assuming they are clustered into G classes, the following results are obtained. ,in ( ) represents the set of indices for this type of segment, and g is a constant representing the g-th cluster index.

[0045] Average power ; where j is a constant, representing the index of the j-th segment.

[0046] In practice, the clustering algorithm can be the kmeans clustering algorithm.

[0047] S232: For the clustering results of each receiver, all segments in each cluster are superimposed to enhance the results. In this embodiment, the subsequent fragment overlay result The sign length is then double-stacked, and the modulus is calculated. .

[0048] , The formula is as follows: (1.3) (1.4) In the above embodiments, a two-stage superposition method was used for coherent superposition, first the time slot period and then the symbol period. During the first coherent superposition, the clustered data (i.e., the signal) was segmented according to the time slot period, and these time slot segments were then aligned and superimposed. This involved directly superimposing the time slot periods for calculation, thereby leveraging the time slot periodicity to enhance the signal. However, considering the periodicity of OFDM and the symbol distribution, a time slot contains seven OFDM symbols, including one symbol with an extended cyclic prefix and six symbols with cyclic prefixes. The length of the extended cyclic prefix is ​​greater than the length of the cyclic prefix. This results in the accumulated time slot segments being fully aligned and starting from the same point after the first coherent superposition. However, the symbols within each time slot are unevenly distributed due to the presence of a long extended cyclic prefix symbol, leading to a shift in the accumulated symbol period after coherent superposition for each time slot segment. In other words, the symbols within each time slot are not aligned. By performing a second coherent superposition of symbol periods, symbol period alignment is achieved. For example, by aligning symbols according to the deviation value between the extended cyclic prefix and the cyclic prefix during superposition, not only can time slots be aligned and superimposed, but symbols within time slots can also be aligned and superimposed. This avoids the cumulative symbol period offset problem caused by one extended cyclic prefix in seven OFDM symbols within one time slot, and can improve the overall system's ability to detect segments of UAV signals.

[0049] S233: For each segment enhancement result of each receiver, perform autocorrelation feature detection of the cyclic prefix, and select the clustering category with the smallest matching error with the feature detection template of the cyclic prefix.

[0050] Specifically, design a feature detection template for cyclic prefixes. ,right Zero-mean normalization is obtained Segment enhancement results for each receiver Calculate the location of its maximum value. Then extract the sequence to be tested near the maximum value. ,right Zero-mean normalization is obtained Then calculate the sequence to be tested. With feature detection template Matching error between .

[0051] , , , , , The formulas are as follows: (1.5) (1.6) (1.7) (1.8) (1.9) (1.10) in, , .

[0052] S234: In the cluster category with the smallest matching error, the segment at the location of the maximum value of the autocorrelation feature detection of the cyclic prefix is ​​taken as the valid segment.

[0053] In each receiver, assuming the clustering category with the smallest matching error is the th cluster... The class, where w is a constant representing the w-th cluster index, has a matching error of . The position of the maximum value of the autocorrelation graph feature detection of its cyclic prefix is The set of fragment numbers for this class is .

[0054] In one implementation, after selecting the cluster category with the smallest matching error to the feature detection template, a fragment detection threshold is also set: a first matching error threshold. The minimum matching error is compared with the first matching error threshold. The comparison is performed, and only the smallest matching error is less than the first matching error threshold, i.e. Only when the autocorrelation feature of the cyclic prefix reaches its maximum value is the segment considered a valid segment; otherwise, the segment is considered invalid. If the receiver cannot select a valid segment, it is considered that the data of the station is invalid and is discarded from the system, thus saving computing power and improving computing efficiency.

[0055] In one embodiment, after selecting valid segments from the data received by the receiver, a high confidence threshold is set during time difference calculation: a second matching error threshold. The second matching error threshold is less than the first error matching threshold. The signal quality of valid segments is further assessed by using a high confidence threshold. When the signal quality is relatively high, the time difference can be calculated directly by detecting the position of the maximum value of the autocorrelation feature of the cyclic prefix, as shown in formula (1.11).

[0056] If not, that is When the signal quality is relatively low, using the autocorrelation feature of the cyclic prefix to detect the maximum value location to calculate the time difference results in a large error. Instead, effective segments of clustering and the original IQ data can be used for time difference calculation. Specifically, based on the clustering of receiver f... Where f represents the sequence number of the f-th receiver, and the clustering with other receivers that detected valid segments. Calculate the intersection to obtain As shown in formula (1.12), the cross-correlation function is obtained by using the segment index of the intersection to select the original IQ data for generalized cross-correlation (GCC) calculation. The formula is shown in (1.13); finally, the time difference between the two devices is calculated. The formula is shown in (1.14).

[0057] (1.11) (1.12) (1.13) (1.14) in, .

[0058] In one embodiment, a system for detecting and locating drone signals is also provided; please refer to [reference needed]. Figure 5 It includes: Signal detection module 11 is used to detect UAV signals using multiple receivers at the TDOA receiving station; The signal recognition module 12 is used to identify the drone signal after it is detected. If the drone signal is identified as a preset drone signal, the module selects segments of the raw data received by the receiver and then enters the time difference calculation module. Otherwise, it directly enters the time difference calculation module. Segment selection module 13, which includes: The autocorrelation operation unit 131 is used to perform autocorrelation operation on the raw data received by each receiver to obtain the autocorrelation function; Segmentation unit 132 is used to segment the autocorrelation function of each receiver into multiple segments according to the time slot length; The effective segment filtering unit 133 is used to filter effective segments from multiple segments and use the effective segments to participate in time difference calculation; Time difference calculation module 14 is used to perform time difference calculation.

[0059] In one implementation method, please refer to Figure 6 The effective fragment filtering unit 133 includes: The segment clustering unit 1331 is used to calculate the average power of each segment, and to perform clustering operations on multiple segments of each receiver according to the magnitude of the average power to form a cluster combination of segments; The fragment enhancement unit 1332 is used to perform superposition enhancement operations on the clustering results to obtain the fragment enhancement results for each class; The feature detection unit 1333 is used to perform autocorrelation feature detection of the cyclic prefix for each type of segment enhancement result of each receiver, and to select the cluster category with the smallest matching error with the feature detection template. The effective fragment selection unit 1334 is used to select the fragment at the position of the maximum value of the autocorrelation feature detection of the cyclic prefix in the cluster category with the smallest matching error as the effective fragment.

[0060] In one embodiment, the effective segment selection unit is further configured to: determine whether the minimum matching error is less than a first matching error threshold; if so, select the segment at the position of the maximum value of the autocorrelation feature detection of the cyclic prefix in the cluster category with the minimum matching error as an effective segment; otherwise, the receiver cannot select an effective segment and discards the data received by the receiver.

[0061] In one embodiment, when the time difference calculation module performs time difference calculation using valid segments, it specifically performs the following: for each receiver, it determines whether the minimum matching error is less than a second matching error threshold, and the second matching error threshold is less than a first matching error threshold; if so, it uses the autocorrelation feature of the cyclic prefixes of the two receivers to detect the location of the maximum value to calculate the time difference; otherwise, it uses the intersection of the valid segments of the two receivers and the generalized cross-correlation function of the original data received by the receivers to calculate the time difference.

[0062] In the description of this specification, the references to terms such as "an embodiment," "an example," "a specific implementation process," and "an example" indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting and locating unmanned aerial vehicle (UAV) signals, characterized in that, include: Multiple receivers at the TDOA receiving station are used to detect drone signals; Once the drone signal is detected, the drone signal is identified. If the drone signal is identified as a preset drone signal, the raw data received by the receiver is segmented and then the time difference calculation is performed. Otherwise, the time difference calculation is performed directly. The segment selection includes: An autocorrelation function is obtained by performing an autocorrelation operation on the raw data received by each of the receivers. Based on the time slot length, the autocorrelation function of each receiver is segmented into multiple segments; Valid segments are selected from the multiple segments using feature detection, and these valid segments are then used in the time difference calculation.

2. The detection and positioning method according to claim 1, characterized in that, The method of using feature detection to filter valid segments from the multiple segments includes: Calculate the average power of each segment, and perform clustering operations on the multiple segments of each receiver according to the magnitude of the average power to form a cluster combination of segments; Based on the clustering results, a superposition enhancement operation is performed to obtain the enhancement result for each segment; For each segment enhancement result of each receiver, autocorrelation feature detection of the cyclic prefix is ​​performed to select the clustering category with the smallest matching error with the feature detection template; The segment containing the maximum value of the autocorrelation feature of the cyclic prefix in the cluster category with the smallest matching error is taken as the valid segment.

3. The detection and positioning method according to claim 2, characterized in that, The step of performing a superposition enhancement operation on the clustering results to obtain the enhancement result for each segment specifically includes: For the clustering results, all segments of each class are superimposed to enhance the results. The superimposed results are then superimposed twice according to the symbol length and the modulus is calculated to obtain the enhanced results of each class of segments.

4. The detection and positioning method according to claim 2, characterized in that, For each segment enhancement result of each receiver, autocorrelation feature detection of the cyclic prefix is ​​performed to select the clustering category with the smallest matching error with the feature detection template, specifically including: For each segment enhancement result of each receiver, the location of its maximum value is calculated, and then the detection sequence near the maximum value is extracted. The matching error between the detection sequence and the feature detection template is calculated, and the clustering category with the smallest matching error is selected.

5. The detection and positioning method according to claim 2, characterized in that, Between selecting the cluster category with the smallest matching error with the feature detection template and selecting the segment at the position of the maximum value of the autocorrelation feature detection of the cyclic prefix in the cluster category with the smallest matching error as the valid segment, the method further includes: If the minimum matching error is less than the first matching error threshold, the segment at the location of the maximum value of the autocorrelation feature of the cyclic prefix in the cluster category with the minimum matching error is selected as the valid segment; otherwise, the receiver cannot select a valid segment and discards the data received by the receiver.

6. The detection and positioning method according to claim 5, characterized in that, After selecting valid segments from the data received by the receiver, the time difference calculation includes: For each receiver, it is determined whether the minimum matching error is less than a second matching error threshold, which is less than the first error matching threshold. If so, the time difference is calculated by detecting the location of the maximum value using the autocorrelation feature of the cyclic prefixes of the two receivers. Otherwise, the time difference is calculated by using the intersection of the effective segments of the two receivers and the generalized cross-correlation function of the original data received by the receivers.

7. The detection and positioning method according to any one of claims 1 to 6, characterized in that, The preset drone signal is a mainstream drone signal.

8. A system for detecting and locating unmanned aerial vehicle (UAV) signals, characterized in that, include: The signal detection module is used to detect UAV signals using multiple receivers at the TDOA receiving station; The signal recognition module is used to identify the drone signal after it is detected. If the drone signal is identified as a preset drone signal, the module selects segments of the raw data received by the receiver and then enters the time difference calculation module. Otherwise, the module directly enters the time difference calculation module. The segment selection module includes: The autocorrelation operation unit is used to perform autocorrelation operation on the raw data received by each of the receivers to obtain the autocorrelation function; The segmentation unit is used to segment the autocorrelation function of each receiver into multiple segments according to the time slot length; The effective segment filtering unit is used to filter effective segments from the multiple segments using feature detection, and to use the effective segments to participate in time difference calculation. The time difference calculation module is used to perform time difference calculations.

9. The detection and positioning system according to claim 8, characterized in that, The effective fragment filtering unit includes: The segment clustering unit is used to calculate the average power of each segment, and to perform clustering operations on multiple segments of each receiver according to the magnitude of the average power to form a cluster combination of segments; The fragment enhancement unit is used to perform superposition enhancement operations on the clustering results to obtain the enhanced fragment results for each class. The feature detection unit is used to perform autocorrelation feature detection of the cyclic prefix for each type of segment enhancement result of each receiver, and to select the clustering category with the smallest matching error with the feature detection template. The effective fragment selection unit is used to select the fragment at the position of the maximum value of the autocorrelation feature detection of the cyclic prefix in the cluster category with the smallest matching error as an effective fragment.

10. The detection and positioning system according to claim 9, characterized in that, The effective segment selection unit is further configured to: determine whether the minimum matching error is less than a first matching error threshold; if so, select the segment at the position of the maximum value of the autocorrelation feature detection of the cyclic prefix in the cluster category with the minimum matching error as an effective segment; otherwise, the receiver cannot select an effective segment and discards the data received by the receiver.

11. The detection and positioning system according to claim 10, characterized in that, When the time difference calculation module is used to perform time difference calculation using the effective segment, it is specifically used to: for each receiver, determine whether the minimum matching error is less than a second matching error threshold, wherein the second matching error threshold is less than the first error matching threshold; If so, the time difference is calculated by detecting the location of the maximum value using the autocorrelation feature of the cyclic prefixes of the two receivers; otherwise, the time difference is calculated by using the intersection of the effective segments of the two receivers and the generalized cross-correlation function of the original data received by the receivers.

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