A wearable portable unmanned aerial vehicle signal detection and positioning method

Abstract

The application discloses a kind of wearable portable unmanned aerial vehicle signal detection and positioning method, it is related to unmanned aerial vehicle signal field, its method includes: obtaining N groups of signal observation parameters;Based on N groups of signal observation parameters, signal observation unit sequence is constructed;According to signal observation unit sequence, obtain M effective observation units;M effective observation unit corresponding signal azimuth is generated;The device position coordinates are combined with signal azimuth, and M signal pointing rays are generated;According to the intersection relationship between M signal pointing rays, generate several candidate positioning points;In several candidate positioning points, anchor target positioning point;Target positioning point is observed source backtracking and signal merging, and unmanned aerial vehicle signal positioning record is constructed;The application can merge signal frequency, received power, effective observation unit quantity and the positioning coordinates of target positioning point, so that positioning result can correspond to specific observation source, is convenient for review, display and archive.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) signal detection, specifically a wearable portable UAV signal detection and positioning method. Background Technology

[0002] With the increasing use of low-altitude drones in various scenarios, such as patrolling key areas, temporary security, park protection, and field duties, it is often necessary to detect wireless signals such as drone remote control signals and image transmission signals, and determine their possible source locations. Compared to fixed or vehicle-mounted detection equipment, wearable portable detection equipment is more convenient to use with personnel on the move. It can continuously receive surrounding radio frequency signals at different locations and perform location analysis by combining information such as device location, device orientation, and antenna received energy.

[0003] In existing portable signal positioning methods, suspected UAV signals are usually first screened based on parameters such as signal frequency, bandwidth and receiving power. Then, the signal direction is estimated based on the received energy difference of multi-directional antennas. The receiving position and signal direction are converted into direction finding rays. The signal source position is estimated through the intersection or convergence area between multiple direction finding rays.

[0004] However, as wearable devices move with a person, factors such as body obstruction, changes in posture, environmental reflection, multipath propagation, and antenna energy fluctuations can all affect the direction finding results, causing the intersection points formed by multiple direction finding rays to be relatively dispersed. If the positioning result is determined directly based on the intersection point location, it is easy to overlook the source relationship between the intersection point and the corresponding direction finding ray, and it is also difficult to determine whether a certain intersection area maintains good consistency with the relevant direction finding rays. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for signal detection and positioning of wearable portable drones. This method inherits ray numbers from candidate positioning points, traces back the signal pointing rays within a cluster based on the inherited ray numbers, and then anchors the target positioning point using the number of intersections and the average vertical distance. This solves the technical problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for signal detection and positioning of a wearable portable drone includes the following steps: S1. Obtain N sets of signal observation parameters formed by the wearable detection device receiving suspected drone signals at N consecutive sampling times; S2. Based on N sets of signal observation parameters, construct a sequence of signal observation units corresponding to N consecutive sampling times; S3. According to the signal observation unit sequence, mark the UAV signal validity of each signal observation unit to obtain M valid observation units; S4. Based on the multi-directional antenna energy in the M effective observation units, generate the signal direction of arrival angles corresponding to the M effective observation units; S5. Combine the equipment position coordinates and signal direction angles corresponding to each effective observation unit to generate M signal pointing rays; S6. Based on the intersection relationship between the M signal pointing rays, generate several candidate positioning points; S7. Anchor the target positioning point among several candidate positioning points; S8. Perform observation source backtracking and signal merging on the target positioning point to construct a UAV signal positioning record.

[0007] In some embodiments, acquiring N sets of signal observation parameters formed by the wearable detection device receiving suspected drone signals at N consecutive sampling times includes: S1-1. Collect several sets of radio frequency signal parameters, device attitude parameters, and device position parameters received by the wearable detection device during continuous sampling. S1-2. Based on the sampling time of the radio frequency signal parameters, the radio frequency signal parameters, equipment attitude parameters, and equipment position parameters are timestamped to generate several sets of initial observation parameters. S1-3. Perform integrity verification on the equipment position coordinates, wearing direction angle, received power and multi-directional antenna energy in several sets of initial observation parameters, and perform synchronization verification on the timestamp alignment status in several sets of initial observation parameters to identify invalid observation parameters. S1-4. Remove invalid observation parameters from several sets of initial observation parameters and retain several sets of usable observation parameters; S1-5. Select N sets of available observation parameters corresponding to N consecutive sampling times from several sets of available observation parameters, and use them as N sets of signal observation parameters.

[0008] In some embodiments, a sequence of signal observation units corresponding to N consecutive sampling times is constructed, including: S2-1. Read the sampling times corresponding to N sets of signal observation parameters; S2-2. Encapsulate the signal observation parameters and sampling times corresponding to the same sampling time into a signal observation unit; S2-3. Arrange the N signal observation units according to the order of the N consecutive sampling times to generate a signal observation unit sequence.

[0009] In some embodiments, the step of marking the UAV signal validity of each signal observation unit according to the signal observation unit sequence to obtain M valid observation units includes: S3-1. Read the signal discrimination parameters in the signal observation unit; The signal discrimination parameters include frequency band discrimination parameters and intensity stability parameters. The frequency band discrimination parameters include at least the signal frequency and signal bandwidth, and the intensity stability parameters include at least the received power and envelope stability. S3-2. If the signal frequency in the frequency band discrimination parameter is within the preset UAV communication frequency band and the signal bandwidth is within the preset bandwidth range, then the signal observation unit is marked as a frequency band matching observation unit. S3-3. If the received power in the intensity stability parameter of the frequency band matching observation unit is greater than the preset power threshold and the envelope stability is greater than the preset stability threshold, then the frequency band matching observation unit is marked as a valid observation unit. S3-4. Traverse the N signal observation units until M valid observation units are marked in the signal observation unit sequence.

[0010] In some embodiments, generating the signal direction of arrival angles corresponding to the M effective observation units based on the multi-directional antenna energy of the M effective observation units includes: S4-1, Read the forward antenna energy, backward antenna energy, left antenna energy and right antenna energy in the effective observation unit; S4-2. Subtract the left antenna energy from the right antenna energy to obtain the lateral energy difference, and subtract the rear antenna energy from the forward antenna energy to obtain the longitudinal energy difference; S4-3. Take the lateral energy difference as the lateral direction component and the longitudinal energy difference as the longitudinal direction component, and perform four-quadrant arctangent calculation on the lateral and longitudinal direction components to obtain the relative angle of arrival. S4-4. Add the relative angle of arrival to the wearing direction angle corresponding to the effective observation unit, and normalize the addition result to a preset angle range to obtain the signal angle of arrival; S4-5. Traverse the M effective observation units and generate the signal direction angles corresponding to the M effective observation units.

[0011] In some embodiments, the step of combining the device position coordinates corresponding to each effective observation unit with the signal direction angle to generate M signal pointing rays includes: S5-1. Read the equipment position coordinates and signal direction angle corresponding to the valid observation unit; S5-2. Use the equipment location coordinates as the starting point of the ray; S5-3. Extend the ray from the starting point of the ray along the direction indicated by the signal angle to generate a signal pointing ray corresponding to the effective observation unit; S5-4. Traverse M valid observation units and generate M signal pointing rays.

[0012] In some embodiments, generating several candidate positioning points based on the intersection relationship between the M signal pointing rays includes: S6-1. Assign ray numbers to the M signal pointing rays according to the order of the effective observation units corresponding to the M signal pointing rays in the signal observation unit sequence. S6-2. Among the M signal pointing rays, select the i-th signal pointing ray and the j-th signal pointing ray whose ray numbers satisfy i < j to form a ray pair, until C ray pairs are obtained; where C = M × (M-1) / 2; S6-3. Subtract the angles of the two signals pointing to the ray from each other and take the absolute value to obtain the angle difference. S6-4. If the angle difference is greater than 180°, then the result obtained by subtracting the angle difference from 360° shall be determined as the ray angle; if the angle difference is less than or equal to 180°, then the angle difference shall be determined as the ray angle. S6-5. If the angle between the rays is greater than the preset intersection angle, calculate the intersection point of the ray pair. S6-6. If the intersection point is located in the positive extension direction of the two signal-pointing rays in the ray pair, then the intersection point is marked as a candidate positioning point, and the candidate positioning point inherits the ray number of the two signal-pointing rays in the ray pair. S6-7. Traverse the C ray pairs to generate several candidate positioning points.

[0013] In some embodiments, anchoring the target location point among a plurality of candidate location points includes: S7-1. Perform distance clustering on several candidate location points to generate at least one location point cluster; S7-2. Based on the ray numbers inherited by candidate positioning points within the positioning point cluster, deduplicatize and merge the ray numbers, and determine the signal pointing ray within the cluster corresponding to the positioning point cluster. S7-3. Taking the cluster center of the positioning point cluster as the test point, calculate the vertical distance from the test point to the line where the signal pointing ray is located within the corresponding cluster, and average the vertical distances to obtain the average vertical distance. S7-4. Add the average vertical distance to the preset reference distance to obtain the positioning constraint distance; S7-5. Count the number of intersections corresponding to each location point cluster; where the number of intersections is the number of candidate location points contained in the location point cluster. S7-6. Divide the number of intersections by the positioning constraint distance to generate the positioning reliability. S7-7. Determine the cluster center of the cluster of positioning points with the highest positioning confidence as the target positioning point.

[0014] In some embodiments, the step of tracing the source of observations and merging signals to construct a UAV signal positioning record for the target location point includes: S8-1. Determine the cluster of positioning points corresponding to the target positioning point as the target positioning point cluster; S8-2, Read the ray number inherited by each candidate positioning point within the target positioning point cluster; S8-3. Deduplicate and merge the read ray numbers to generate a set of target ray numbers; S8-4. From the M signal pointing rays, extract the signal pointing rays corresponding to the target ray number set and generate the target signal pointing ray group; S8-5. Based on the effective observation units corresponding to each signal pointing ray in the target signal pointing ray group, duplicate effective observation units are deduplicated and merged to generate a target effective observation unit group. S8-6. Extract the signal frequency and received power corresponding to each effective observation unit from the target effective observation unit group; S8-7. Based on the signal frequency, count the number of effective observation units in the target effective observation unit group, and determine the signal frequency with the largest number as the positioning signal frequency; S8-8. Based on the received power corresponding to each effective observation unit in the target effective observation unit group, extract the minimum received power and the maximum received power, and generate the received power range. S8-9. Count the number of effective observation units in the effective observation unit group of the target, and extract the positioning coordinates of the target positioning point; S8-10. Combine the positioning coordinates, the number of effective observation units, the positioning signal frequency, and the receiving power range to construct the UAV signal positioning record.

[0015] This invention provides a method for signal detection and positioning of wearable portable drones, which has the following advantages: This invention, by generating candidate positioning points when signal pointing rays form ray pairs, ensures that each candidate positioning point inherits the ray numbers of the two signal pointing rays in the corresponding ray pair. This prevents the candidate positioning point from being merely an isolated spatial intersection, preserving its source correspondence with the signal pointing rays. Therefore, when anchoring a target positioning point, the signal pointing ray within the positioning point cluster can be determined by reverse calculation based on the ray numbers inherited by the candidate positioning points within that cluster.

[0016] Furthermore, when anchoring the target positioning point among several candidate positioning points, distance clustering is first performed on the candidate positioning points to generate positioning point clusters; then, the signal pointing rays within the cluster are determined based on the ray numbers inherited by the candidate positioning points within the positioning point cluster, and the average vertical distance from the cluster center to the line containing the signal pointing ray within the cluster is calculated. The average vertical distance can be used to reflect the fit between the positioning point cluster and the corresponding signal pointing ray, so that the anchoring of the target positioning point does not only depend on the number of intersections, but also considers the positional relationship between the positioning point cluster and the actual signal pointing ray.

[0017] Furthermore, the positioning constraint distance is obtained by adding the average vertical distance to the preset reference distance, and the positioning confidence level is generated by dividing the number of intersections by the positioning constraint distance. This allows positioning point clusters with a large number of intersections and a small average vertical distance to obtain higher positioning confidence. Thus, the positioning point cluster with the highest positioning confidence can be selected from multiple positioning point clusters, and its cluster center can be determined as the target positioning point, thereby reducing the impact of a single abnormal intersection, multipath reflection intersection, or attitude disturbance intersection on the positioning results.

[0018] Ultimately, when constructing UAV signal positioning records, the target signal pointing ray group and the target effective observation unit group can be traced back by the ray number inherited by the candidate positioning points within the target positioning point cluster. The signal frequency, received power, number of effective observation units, and positioning coordinates of the target positioning points in the target effective observation unit group can be merged, so that the positioning results can be mapped to specific observation sources, which is convenient for verification, display, and archiving. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a wearable portable drone signal detection and positioning method according to the present invention. Figure 2 This is a schematic diagram of the signal direction angle generation process described in this invention; Figure 3 This is a schematic diagram of the process for generating candidate positioning points according to the present invention; Figure 4 This is a schematic diagram of the process for determining the target location point according to the present invention. Detailed Implementation

[0020] 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.

[0021] Example 1: Please refer to Figure 1This invention provides a method for signal detection and positioning of a wearable portable drone, comprising the following steps: S1. Obtain N sets of signal observation parameters formed by the wearable detection device receiving suspected drone signals at N consecutive sampling times.

[0022] Wearable detection devices can be mounted on personnel's backpacks, tactical vests, shoulder straps, or handheld auxiliary supports to receive drone remote control signals from the surrounding environment while personnel are moving.

[0023] Among them, signal observation parameters refer to the set of observation parameters formed by the wearable detection device after receiving a suspected drone signal at the corresponding sampling time. These parameters may include radio frequency signal parameters, device attitude parameters, and device position parameters.

[0024] S2. Based on N sets of signal observation parameters, construct a sequence of signal observation units corresponding to N consecutive sampling times.

[0025] Each set of signal observation parameters corresponds to a sampling time. By encapsulating the signal observation parameters corresponding to the same sampling time into a signal observation unit and arranging them in the order of the sampling times, a sequence of signal observation units can be obtained.

[0026] S3. Based on the signal observation unit sequence, mark the UAV signal validity of each signal observation unit to obtain M valid observation units.

[0027] In this embodiment, the signal observation units are marked as valid in order to select suitable observation units for localization from N signal observation units. The marked valid observation units are used for the subsequent generation of signal direction angle and signal pointing ray.

[0028] S4. Based on the multi-directional antenna energy in the M effective observation units, generate the signal direction of arrival angles corresponding to the M effective observation units.

[0029] Among them, the multi-directional antenna energy is used to represent the difference in signal energy received by antennas in different directions within the same effective observation unit; the direction of arrival of the UAV signal relative to the wearable detection device can be determined by the multi-directional antenna energy, and then combined with the device orientation to generate the signal direction angle of the corresponding effective observation unit.

[0030] S5. Combine the equipment position coordinates and signal direction angles corresponding to each effective observation unit to generate M signal pointing rays.

[0031] The device position coordinates are used to determine the starting point of the ray for a valid observation, and the signal direction angle is used to determine the direction of the ray's extension for that valid observation. Thus, each valid observation unit can generate a signal-directing ray.

[0032] S6. Based on the intersection relationship between the M signal pointing rays, generate several candidate positioning points.

[0033] When wearable detection devices move with personnel, the device position coordinates corresponding to different effective observation units are different, thus forming multiple signal pointing rays with different ray origins. By determining the intersection relationship between these signal pointing rays, several candidate location points of the UAV signal source can be obtained.

[0034] S7. Among several candidate positioning points, anchor the target positioning point.

[0035] Multiple candidate positioning points may be scattered due to signal reflection, multipath propagation, equipment attitude error, or antenna energy fluctuation. In this embodiment, the target positioning point is anchored based on the concentration of the candidate positioning points and their alignment with the signal pointing ray, thereby avoiding using a single intersection point as the positioning result.

[0036] S8. Perform observation source backtracking and signal merging on the target positioning point to construct a UAV signal positioning record.

[0037] In this embodiment, the UAV signal positioning record is used to represent the location of the UAV signal source and its corresponding signal information detected by the wearable detection device. It may include positioning coordinates, number of effective observation units, positioning signal frequency, and receiving power range.

[0038] This embodiment is based on the signal observation parameters generated by the wearable detection device during continuous sampling. First, effective observation units are marked. Then, signal direction angles and signal pointing rays are generated based on the effective observation units. The target positioning point is anchored through the intersection of multiple signal pointing rays. Therefore, portable positioning of the UAV signal source location can be achieved without relying on a vehicle-mounted platform or fixed base station array.

[0039] Example 2: See Figures 2 to 4 This embodiment, based on Embodiment 1, further explains the specific implementation methods of each step. The specific implementation methods of each step are described below.

[0040] In this embodiment, step S1 includes: S1-1. Collect several sets of radio frequency signal parameters, device attitude parameters, and device position parameters received by the wearable detection device during continuous sampling.

[0041] S1-2. Based on the sampling time of the radio frequency signal parameters, the radio frequency signal parameters, equipment attitude parameters, and equipment position parameters are timestamped to generate several sets of initial observation parameters.

[0042] S1-3. Perform integrity verification on the equipment position coordinates, wearing direction angle, received power and multi-directional antenna energy in several sets of initial observation parameters, and perform synchronization verification on the timestamp alignment status in several sets of initial observation parameters to identify invalid observation parameters.

[0043] The integrity check is used to determine whether the initial observation parameters simultaneously include the device location coordinates, wearable orientation angle, received power, and multi-directional antenna energy. If any one of these is empty, missing, or cannot be read, the set of initial observation parameters is identified as invalid.

[0044] Synchronization verification is used to determine whether the timestamps of the radio frequency signal parameters, device attitude parameters, and device position parameters in the same set of initial observation parameters are within the preset synchronization tolerance range. If the time difference between the timestamp of any parameter and the sampling time of the radio frequency signal parameter is greater than the preset synchronization tolerance, the set of initial observation parameters is identified as invalid observation parameters.

[0045] S1-4. Remove invalid observation parameters from several sets of initial observation parameters and retain several sets of usable observation parameters.

[0046] Available observation parameters refer to the initial observation parameters that have passed integrity and synchronization checks. Since available observation parameters simultaneously include the parameters required for signal reception, device orientation, and device location, they can be used to construct signal observation units.

[0047] S1-5. Select N sets of available observation parameters corresponding to N consecutive sampling times from several sets of available observation parameters, and use them as N sets of signal observation parameters.

[0048] Here, N sets of signal observation parameters correspond to the signal reception status of the wearable detection device during a continuous sampling process. By selecting the available observation parameters corresponding to N consecutive sampling times, the mixed use of discrete observation parameters from different time periods can be avoided.

[0049] This embodiment ensures that the N sets of signal observation parameters simultaneously meet the requirements of parameter integrity, timestamp alignment, and continuous sampling by performing integrity checks, synchronization checks, and selecting N sets of continuously available observation parameters. This reduces the impact of missing parameters, time-misaligned parameters, and discontinuous sampling parameters on the signal observation parameters.

[0050] In this embodiment, step S2 includes: S2-1. Read the sampling times corresponding to the N sets of signal observation parameters.

[0051] S2-2. Encapsulate the signal observation parameters and sampling times corresponding to the same sampling time into a signal observation unit.

[0052] The signal observation unit is used to store signal observation parameters formed at the same sampling time, enabling the RF signal, device attitude, and device position at the same sampling time to be processed as a single observation object. The signal observation unit can retain parameter fields such as signal frequency, signal bandwidth, received power, envelope stability, multi-directional antenna energy, device position coordinates, and wearable orientation angle.

[0053] S2-3. Arrange the N signal observation units according to the order of the N consecutive sampling times to generate a signal observation unit sequence. This sequence represents the signal observation changes of the wearable detection device during continuous sampling.

[0054] This embodiment encapsulates the signal observation parameters and sampling times corresponding to the same sampling time into a signal observation unit, and arranges them into a signal observation unit sequence according to the sampling time, so that the N sets of signal observation parameters are transformed from scattered parameters into a signal observation unit sequence organized according to the sampling time.

[0055] In this embodiment, step S3 includes: S3-1. Read the signal discrimination parameters in the signal observation unit; wherein, the signal discrimination parameters include frequency band discrimination parameters and intensity stability parameters, the frequency band discrimination parameters include at least the signal frequency and signal bandwidth, and the intensity stability parameters include at least the received power and envelope stability.

[0056] Among them, the frequency band discrimination parameter is used to determine whether the radio frequency signal in the signal observation unit conforms to the frequency band characteristics of the UAV communication signal, and the strength stability parameter is used to determine whether the radio frequency signal has the receiving strength and short-time stability to participate in positioning.

[0057] S3-2. If the signal frequency in the frequency band discrimination parameter is within the preset UAV communication frequency band and the signal bandwidth is within the preset bandwidth range, then the signal observation unit is marked as a frequency band matching observation unit.

[0058] S3-3. If the received power in the intensity stability parameter of the frequency band matching observation unit is greater than the preset power threshold and the envelope stability is greater than the preset stability threshold, then the frequency band matching observation unit is marked as a valid observation unit.

[0059] In this embodiment, observation units that are obviously not part of the UAV communication signal are first screened out by frequency band discrimination parameters, and then observation units with weak signals or large short-term fluctuations are screened out by intensity stability parameters, so that the marked effective observation units can be used as the basis for calculating the signal direction angle and signal pointing ray.

[0060] S3-4. Traverse the N signal observation units until M valid observation units are marked in the signal observation unit sequence.

[0061] This embodiment uses frequency band discrimination parameters and strength stability parameters to classify and label signal observation units, thereby excluding observation units with mismatched frequency bands, insufficient received strength, or large short-term fluctuations, thus obtaining more effective observation units that are more suitable for characterizing UAV signals.

[0062] In this embodiment, step S4 includes: S4-1. Read the forward antenna energy, backward antenna energy, left antenna energy, and right antenna energy from the valid observation unit.

[0063] S4-2. Subtract the left antenna energy from the right antenna energy to obtain the lateral energy difference, and subtract the rear antenna energy from the forward antenna energy to obtain the longitudinal energy difference.

[0064] Among them, the lateral energy difference is used to represent the energy bias of the UAV signal in the left-right direction, and the longitudinal energy difference is used to represent the energy bias of the UAV signal in the front-back direction.

[0065] S4-3. Take the lateral energy difference as the lateral direction component and the longitudinal energy difference as the longitudinal direction component, and perform four-quadrant arctangent calculation on the lateral and longitudinal direction components to obtain the relative angle of arrival.

[0066] In this embodiment, the formula for calculating the relative angle of arrival is: ; in, Indicates the relative angle of arrival; Indicates the lateral energy difference; Indicates the longitudinal energy difference; This represents the arctangent function in the four quadrants.

[0067] Specifically, the lateral energy difference represents the degree of signal deflection in the left-right direction, and the longitudinal energy difference represents the degree of signal deflection in the front-back direction. By treating these as lateral and longitudinal components respectively, a two-dimensional orientation relationship of the signal relative to the wearable detection device can be formed. Using the four-quadrant arctangent function for calculation, it is possible to distinguish between different quadrants where the signal is deflected forward, backward, left, or right, depending on whether the lateral and longitudinal components are positive or negative, thus obtaining the relative orientation angle relative to the wearable detection device's own orientation.

[0068] S4-4. Add the relative angle of arrival to the wearing direction angle corresponding to the effective observation unit, and normalize the addition result to a preset angle range to obtain the signal angle of arrival.

[0069] It should be noted that the relative angle of arrival represents the direction of the drone signal relative to the orientation of the wearable detection device itself, while the wearable orientation angle represents the orientation of the wearable detection device in the map coordinate system. Adding the two together converts the signal direction relative to the device's orientation into the signal angle of arrival in the map coordinate system. The preset angle range can be 0° to 360°, or -180° to 180°.

[0070] S4-5. Traverse the M effective observation units and generate the signal direction angles corresponding to the M effective observation units.

[0071] This embodiment determines the relative direction of arrival (ROA) by measuring the antenna energy difference in the front-back and left-right directions, and obtains the signal ROA by combining it with the wear direction angle, so that the multi-directional antenna energy in the effective observation unit can be converted into a direction angle expression under a unified coordinate system.

[0072] In this embodiment, step S5 includes: S5-1. Read the equipment position coordinates and signal direction angle corresponding to the valid observation unit.

[0073] S5-2. Use the equipment location coordinates as the starting point of the ray.

[0074] Among them, the device position coordinates represent the position of the wearable detection device at the corresponding sampling time, and therefore can be used as the starting point of the ray corresponding to this signal observation.

[0075] S5-3. Extend the ray from the starting point of the ray along the direction indicated by the signal direction angle to generate a signal pointing ray corresponding to the effective observation unit.

[0076] The signal direction of arrival (STO) indicates the direction of the UAV signal relative to the wearable detection device in the map coordinate system. Starting from the device's location coordinates and extending positively along the direction indicated by the STO, the signal pointing ray corresponding to that effective observation unit can be obtained.

[0077] S5-4. Traverse M valid observation units and generate M signal pointing rays.

[0078] In this embodiment, the device position coordinates are used as the starting point of the ray, and the ray extends from the starting point along the direction indicated by the signal direction angle, so that the device position coordinates and the signal direction angle in the effective observation unit are combined into a corresponding signal pointing ray.

[0079] In this embodiment, step S6 includes: S6-1. Assign ray numbers to the M signal pointing rays according to the order of the effective observation units corresponding to the M signal pointing rays in the signal observation unit sequence.

[0080] S6-2. Among the M signal pointing rays, select the i-th signal pointing ray and the j-th signal pointing ray whose ray numbers satisfy i < j to form a ray pair, until C ray pairs are obtained; where C = M × (M-1) / 2.

[0081] By limiting i < j, any two different signals pointing to rays can form a ray pair only once, avoiding the repeated calculation of the same ray pair.

[0082] S6-3. Subtract the angles of the two signals pointing to the ray from each other and take the absolute value to obtain the angle difference. S6-4. If the angle difference is greater than 180°, then the result obtained by subtracting the angle difference from 360° shall be determined as the ray angle; if the angle difference is less than or equal to 180°, then the angle difference shall be determined as the ray angle.

[0083] Among them, the ray angle represents the angle between the smaller side of the two signal rays, avoiding the mistaken perception of the smaller angle as a larger angle difference due to the use of a circle representing the angle from 0° to 360°.

[0084] S6-5. If the angle between the rays is greater than the preset intersection angle, calculate the intersection point of the ray pair.

[0085] S6-6. If the intersection point is located in the positive extension direction of the two signal-pointing rays in the ray pair, then the intersection point is marked as a candidate positioning point, and the candidate positioning point inherits the ray number of the two signal-pointing rays in the ray pair.

[0086] The forward extension direction refers to the direction extending from the ray's origin along the direction indicated by the signal's direction of travel. If the intersection point is located in the reverse extension direction of any signal-pointing ray, then the intersection point is inconsistent with the signal direction determination of the corresponding valid observation unit and is not considered a candidate location point.

[0087] S6-7. Traverse the C ray pairs to generate several candidate positioning points.

[0088] This embodiment performs ray numbering, ray pair construction, ray angle determination, and forward extension direction determination on M signal pointing rays, so that the intersection point of ray pairs that meet the intersection conditions is retained as a candidate positioning point, and the candidate positioning point can inherit the corresponding ray number.

[0089] In this embodiment, step S7 includes: S7-1. Perform distance clustering on several candidate location points to generate at least one location point cluster.

[0090] Among them, the location point cluster represents the location area formed by multiple candidate location points clustering in close proximity.

[0091] S7-2. Based on the ray numbers inherited by candidate positioning points within the positioning point cluster, deduplicate and merge the ray numbers, and determine the signal pointing ray within the cluster corresponding to the positioning point cluster.

[0092] Since candidate locators inherit the ray number from the corresponding ray pair during generation, the intra-cluster signal pointing rays that participate in forming the locator cluster can be determined based on the ray number inherited by the candidate locators within the locator cluster.

[0093] S7-3. Taking the cluster center of the positioning point cluster as the test point, calculate the vertical distance from the test point to the line where the signal pointing ray is located within the corresponding cluster, and average the vertical distances to obtain the average vertical distance.

[0094] In this context, the cluster center of the location point cluster represents the concentrated location of multiple candidate location points within the cluster. The signal pointing rays within the cluster represent the individual signal pointing rays that participate in forming the location point cluster.

[0095] When calculating the average vertical distance, the cluster center of the location point cluster is first taken as the point to be measured; then the vertical distance from the point to be measured to the line where the signal pointing ray in each cluster is located is calculated; finally, the average vertical distance of the multiple vertical distances is calculated to obtain the average vertical distance corresponding to the location point cluster.

[0096] Specifically, the perpendicular distance from the point to be measured to the line containing the signal pointing ray within the cluster is: ; in, It represents the perpendicular distance from the cluster center of the g-th location point cluster to the straight line containing the signal pointing ray within the h-th cluster; and This represents the coordinates of the cluster center of the g-th location point cluster; and This represents the coordinates of the starting point of the signal-pointing ray within the h-th cluster; This represents the signal direction angle corresponding to the signal pointing ray within the h-th cluster.

[0097] The vertical distance is obtained by projecting the coordinate offset of the cluster center relative to the ray origin onto a direction perpendicular to the angle from which the signal originates. Specifically, and Used to indicate the offset of the cluster center relative to the ray origin in the horizontal and vertical directions; and This is used to represent the vertical direction component determined by the angle of the signal. Combining the above offset with the vertical direction component yields the deviation of the cluster center relative to the line where the signal's pointing ray lies; taking the absolute value of this deviation gives the corresponding vertical distance.

[0098] The average vertical distance is used to represent the proximity between the cluster center of a cluster of location points and the signal pointing rays within the cluster. The smaller the average vertical distance, the closer the cluster center is to the common intersection of multiple signal pointing rays within the cluster.

[0099] S7-4. Add the average vertical distance to the preset reference distance to obtain the positioning constraint distance.

[0100] The preset reference distance is used to avoid the positioning reliability being overemphasized when the average vertical distance is too small.

[0101] S7-5. Count the number of intersections corresponding to each location point cluster; where the number of intersections is the number of candidate location points contained in the location point cluster.

[0102] S7-6. Divide the number of intersections by the positioning constraint distance to generate the positioning reliability.

[0103] S7-7. Determine the cluster center of the cluster of positioning points with the highest positioning confidence as the target positioning point.

[0104] This embodiment calculates the positioning confidence by performing distance clustering on candidate positioning points and combining the signal pointing rays within the cluster, average vertical distance, positioning constraint distance, and number of intersections, so that the cluster center of the positioning point cluster with the highest positioning confidence is determined as the target positioning point.

[0105] In this embodiment, step S8 includes: S8-1. Determine the cluster of positioning points corresponding to the target positioning point as the target positioning point cluster.

[0106] The target location point is determined by the cluster center of the location point cluster with the highest location confidence, thus the target location point cluster corresponding to the target location point can be determined.

[0107] S8-2, Read the ray number inherited by each candidate positioning point within the target positioning point cluster.

[0108] It should be noted that candidate positioning points are formed by the intersection of ray pairs, and each candidate positioning point inherits the ray numbers of the two signal pointing rays in the corresponding ray pair during its generation. Therefore, it is possible to trace back the signal pointing rays that participated in the formation of the target positioning point cluster by using the candidate positioning points within the target positioning point cluster.

[0109] S8-3. Deduplicate and merge the read ray numbers to generate a set of target ray numbers.

[0110] Among them, deduplication and merging are used to avoid the same signal pointing ray being counted repeatedly because it participates in multiple candidate positioning points.

[0111] S8-4. Among the M signal pointing rays, extract the signal pointing rays corresponding to the target ray number set to generate the target signal pointing ray group.

[0112] S8-5. Based on the effective observation units corresponding to each signal pointing ray in the target signal pointing ray group, duplicate effective observation units are deduplicated and merged to generate a target effective observation unit group.

[0113] Each signal pointing ray is generated from the device position coordinates and signal direction angle corresponding to an effective observation unit. Therefore, the corresponding effective observation unit can be obtained by tracing back from the target signal pointing ray group. The target effective observation unit group is used to represent the observation sources involved in the construction of the target positioning point.

[0114] S8-6. Extract the signal frequency and received power corresponding to each effective observation unit from the target effective observation unit group.

[0115] S8-7. Count the number of effective observation units in the target effective observation unit group according to the signal frequency, and determine the signal frequency with the largest number as the positioning signal frequency.

[0116] S8-8. Based on the received power corresponding to each effective observation unit in the target effective observation unit group, extract the minimum received power and the maximum received power, and generate the received power range.

[0117] S8-9. Count the number of effective observation units in the effective observation unit group of the target, and extract the positioning coordinates of the target positioning point.

[0118] S8-10. Combine the positioning coordinates, the number of effective observation units, the positioning signal frequency, and the receiving power range to construct the UAV signal positioning record.

[0119] This embodiment uses the ray number inherited by candidate positioning points within the target positioning point cluster to trace back the target signal pointing ray group and the target effective observation unit group, and combines the positioning coordinates, the number of effective observation units, the positioning signal frequency, and the receiving power range to enable the UAV signal positioning record to simultaneously include the position result of the target positioning point and the corresponding signal information.

[0120] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any equivalent substitutions, simple modifications, or combinations of the above technical solutions made under the technical concept of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for signal detection and positioning of a wearable portable unmanned aerial vehicle, characterized in that, include: S1. Obtain N sets of signal observation parameters formed by the wearable detection device receiving suspected drone signals at N consecutive sampling times; S2. Based on N sets of signal observation parameters, construct a sequence of signal observation units corresponding to N consecutive sampling times; S3. According to the signal observation unit sequence, mark the UAV signal validity of each signal observation unit to obtain M valid observation units; S4. Based on the multi-directional antenna energy in the M effective observation units, generate the signal direction of arrival angles corresponding to the M effective observation units; S5. Combine the equipment position coordinates and signal direction angles corresponding to each effective observation unit to generate M signal pointing rays; S6. Based on the intersection relationship between the M signal pointing rays, generate several candidate positioning points; S7. Anchor the target positioning point among several candidate positioning points; S8. Perform observation source backtracking and signal merging on the target positioning point to construct a UAV signal positioning record.

2. The method for signal detection and positioning of a wearable portable drone according to claim 1, characterized in that, The acquisition of N sets of signal observation parameters formed by the wearable detection device receiving suspected drone signals at N consecutive sampling times includes: S1-1. Collect several sets of radio frequency signal parameters, device attitude parameters, and device position parameters received by the wearable detection device during continuous sampling. S1-2. Based on the sampling time of the radio frequency signal parameters, the radio frequency signal parameters, equipment attitude parameters, and equipment position parameters are timestamped to generate several sets of initial observation parameters. S1-3. Perform integrity verification on the equipment position coordinates, wearing direction angle, received power and multi-directional antenna energy in several sets of initial observation parameters, and perform synchronization verification on the timestamp alignment status in several sets of initial observation parameters to identify invalid observation parameters. S1-4. Remove invalid observation parameters from several sets of initial observation parameters and retain several sets of usable observation parameters; S1-5. Select N sets of available observation parameters corresponding to N consecutive sampling times from several sets of available observation parameters, and use them as N sets of signal observation parameters.

3. The method for signal detection and positioning of a wearable portable drone according to claim 2, characterized in that, The construction of a signal observation unit sequence corresponding to N consecutive sampling times based on N sets of signal observation parameters includes: S2-1. Read the sampling times corresponding to N sets of signal observation parameters; S2-2. Encapsulate the signal observation parameters and sampling times corresponding to the same sampling time into a signal observation unit; S2-3. Arrange the N signal observation units according to the order of the N consecutive sampling times to generate a signal observation unit sequence.

4. The wearable portable drone signal detection and positioning method according to claim 3, characterized in that, The step involves marking the UAV signal validity of each signal observation unit according to the signal observation unit sequence, resulting in M ​​valid observation units, including: S3-1. Read the signal discrimination parameters in the signal observation unit; The signal discrimination parameters include frequency band discrimination parameters and intensity stability parameters. The frequency band discrimination parameters include at least the signal frequency and signal bandwidth, and the intensity stability parameters include at least the received power and envelope stability. S3-2. If the signal frequency in the frequency band discrimination parameter is within the preset UAV communication frequency band and the signal bandwidth is within the preset bandwidth range, then the signal observation unit is marked as a frequency band matching observation unit. S3-3. If the received power in the intensity stability parameter of the frequency band matching observation unit is greater than the preset power threshold and the envelope stability is greater than the preset stability threshold, then the frequency band matching observation unit is marked as a valid observation unit. S3-4. Traverse the N signal observation units until M valid observation units are marked in the signal observation unit sequence.

5. The method for signal detection and positioning of a wearable portable drone according to claim 3, characterized in that, The generation of the signal direction of arrival angles corresponding to the M effective observation units based on the multi-directional antenna energy of the M effective observation units includes: S4-1, Read the forward antenna energy, backward antenna energy, left antenna energy and right antenna energy in the effective observation unit; S4-2. Subtract the left antenna energy from the right antenna energy to obtain the lateral energy difference, and subtract the rear antenna energy from the forward antenna energy to obtain the longitudinal energy difference; S4-3. Take the lateral energy difference as the lateral direction component and the longitudinal energy difference as the longitudinal direction component, and perform four-quadrant arctangent calculation on the lateral and longitudinal direction components to obtain the relative angle of arrival. S4-4. Add the relative angle of arrival to the wearing direction angle corresponding to the effective observation unit, and normalize the addition result to a preset angle range to obtain the signal angle of arrival; S4-5. Traverse the M effective observation units and generate the signal direction angles corresponding to the M effective observation units.

6. The method for signal detection and positioning of a wearable portable drone according to claim 1, characterized in that, The step of combining the device position coordinates and signal direction angles corresponding to each effective observation unit to generate M signal pointing rays includes: S5-1. Read the equipment position coordinates and signal direction angle corresponding to the valid observation unit; S5-2. Use the equipment location coordinates as the starting point of the ray; S5-3. Extend the ray from the starting point of the ray along the direction indicated by the signal angle to generate a signal pointing ray corresponding to the effective observation unit; S5-4. Traverse M valid observation units and generate M signal pointing rays.

7. The method for signal detection and positioning of a wearable portable drone according to claim 1, characterized in that, The step of generating several candidate positioning points based on the intersection relationship between the M signal pointing rays includes: S6-1. Assign ray numbers to the M signal pointing rays according to the order of the effective observation units corresponding to the M signal pointing rays in the signal observation unit sequence. S6-2. Among the M signal pointing rays, select the i-th signal pointing ray and the j-th signal pointing ray whose ray numbers satisfy i < j to form a ray pair, until C ray pairs are obtained; where C = M × (M-1) / 2; S6-3. Subtract the angles of the two signals pointing to the ray from each other and take the absolute value to obtain the angle difference. S6-4. If the angle difference is greater than 180°, then the result obtained by subtracting the angle difference from 360° shall be determined as the ray angle; if the angle difference is less than or equal to 180°, then the angle difference shall be determined as the ray angle. S6-5. If the angle between the rays is greater than the preset intersection angle, calculate the intersection point of the ray pair. S6-6. If the intersection point is located in the positive extension direction of the two signal-pointing rays in the ray pair, then the intersection point is marked as a candidate positioning point, and the candidate positioning point inherits the ray number of the two signal-pointing rays in the ray pair. S6-7. Traverse the C ray pairs to generate several candidate positioning points.

8. The method for signal detection and positioning of a wearable portable drone according to claim 7, characterized in that, The step of anchoring the target positioning point among several candidate positioning points includes: S7-1. Perform distance clustering on several candidate location points to generate at least one location point cluster; S7-2. Based on the ray numbers inherited by candidate positioning points within the positioning point cluster, deduplicatize and merge the ray numbers, and determine the signal pointing ray within the cluster corresponding to the positioning point cluster. S7-3. Taking the cluster center of the positioning point cluster as the test point, calculate the vertical distance from the test point to the line where the signal pointing ray is located within the corresponding cluster, and average the vertical distances to obtain the average vertical distance. S7-4. Add the average vertical distance to the preset reference distance to obtain the positioning constraint distance; S7-5. Count the number of intersections corresponding to each location point cluster; where the number of intersections is the number of candidate location points contained in the location point cluster. S7-6. Divide the number of intersections by the positioning constraint distance to generate the positioning reliability. S7-7. Determine the cluster center of the cluster of positioning points with the highest positioning confidence as the target positioning point.

9. A method for signal detection and positioning of a wearable portable drone according to claim 8, characterized in that, The process of tracing the source of observations and merging signals at the target location point to construct a UAV signal location record includes: S8-1. Determine the cluster of positioning points corresponding to the target positioning point as the target positioning point cluster; S8-2, Read the ray number inherited by each candidate positioning point within the target positioning point cluster; S8-3. Deduplicate and merge the read ray numbers to generate a set of target ray numbers; S8-4. From the M signal pointing rays, extract the signal pointing rays corresponding to the target ray number set and generate the target signal pointing ray group; S8-5. Based on the effective observation units corresponding to each signal pointing ray in the target signal pointing ray group, duplicate effective observation units are deduplicated and merged to generate a target effective observation unit group. S8-6. Extract the signal frequency and received power corresponding to each effective observation unit from the target effective observation unit group; S8-7. Based on the signal frequency, count the number of effective observation units in the target effective observation unit group, and determine the signal frequency with the largest number as the positioning signal frequency; S8-8. Based on the received power corresponding to each effective observation unit in the target effective observation unit group, extract the minimum received power and the maximum received power, and generate the received power range. S8-9. Count the number of effective observation units in the effective observation unit group of the target, and extract the positioning coordinates of the target positioning point; S8-10. Combine the positioning coordinates, the number of effective observation units, the positioning signal frequency, and the receiving power range to construct the UAV signal positioning record.

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