Interference source position measurement method, apparatus, electronic device, and storage medium

Abstract

An interference source position measurement method, an apparatus, a device, a medium, and a program product. The method comprises: acquiring measurement data collected by at least one unmanned aerial vehicle, the measurement data at least comprising unmanned aerial vehicle position information and an interference signal sequence; and on the basis of the measurement data, determining position information of an interference source. In the method, a plurality of unmanned aerial vehicles are used for cooperative measurement, so as to achieve the effect of a virtual antenna array and realize high-precision interference source position measurement. On the basis of the flexibility in deployment and transformation of the unmanned aerial vehicles, the number of the unmanned aerial vehicles and test times are freely adjustable, thereby improving the flexibility and measurement accuracy of interference source positioning.

Description

Methods, devices, electronic equipment and storage media for measuring the location of interference sources

[0001] Cross-reference to related applications

[0002] This disclosure claims priority to Chinese Patent Application No. 202411748420.8, filed on November 29, 2024, entitled "Method, Apparatus, Electronic Device and Storage Medium for Measuring Interference Source Location", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of communication technology, and more specifically, to a method, apparatus, electronic device, storage medium, and computer program product for measuring the location of an interference source. Background Technology

[0004] Signal interference sources significantly disrupt communication quality and stability. Therefore, locating and eliminating interference sources has become a crucial issue in the communications field. Traditionally, interference source localization primarily employs two methods: field strength approximation and cross-location. The field strength approximation method tests the interference signal strength over a large area and infers the approximate location of the interference source based on this strength. While more suitable for small-scale applications, it is limited by inefficiency and accuracy issues. The cross-location method utilizes a high-gain directional antenna to locate the strongest interference signal at different locations. By repeating this process at multiple locations, the area containing the interference source can be delineated. However, the cross-location method also presents operational inconvenience due to the need for a high-gain antenna and multi-point testing. Therefore, it is necessary to develop a more efficient and user-friendly method for measuring the location of interference sources. Summary of the Invention

[0005] This disclosure provides a method, apparatus, electronic device, storage medium, and computer program product for measuring the location of an interference source.

[0006] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0007] According to one aspect of this disclosure, a method for measuring the location of an interference source is provided, the method comprising: acquiring measurement data collected by at least one unmanned aerial vehicle (UAV); the measurement data including at least: UAV location information and an interference signal sequence; and determining the location information of the interference source based on the measurement data.

[0008] In an exemplary embodiment, the measurement data corresponds to each of the drones.

[0009] In an exemplary embodiment, acquiring measurement data collected by at least one UAV includes: controlling the at least one UAV to multiple different UAV locations to perform at least one round of interference measurement on the interference source, obtaining at least one set of interference measurement information; each set of interference measurement information corresponds to each round of interference measurement; each set of interference measurement information includes the measurement data corresponding to each UAV; wherein, each round of interference measurement process includes: controlling the at least one UAV to a first UAV location corresponding to each of the UAVs; acquiring a first set of interference measurement information collected by the at least one UAV on the interference source; the first set of interference measurement information includes first measurement data corresponding to each of the UAVs; the first measurement data includes at least: first UAV location information and a first interference signal sequence.

[0010] In an exemplary embodiment, determining the location information of the interference source based on the measurement data includes: determining the time difference between each of the drones based on the interference signal sequence corresponding to each of the drones; and determining the location information of the interference source based on the drone location information and the time difference corresponding to each of the drones.

[0011] In an exemplary embodiment, determining the time difference between each of the drones based on the interference signal sequence corresponding to each of the drones includes: acquiring a second set of interference measurement information; the second set of interference measurement information includes: second measurement data corresponding to the second drone and third measurement data corresponding to the third drone; the second measurement data includes a second interference signal sequence; the third measurement data includes a third interference signal sequence; and comparing the second interference signal sequence with the third interference signal sequence to obtain the time difference between the second drone and the third drone.

[0012] In an exemplary embodiment, comparing the second interference signal sequence with the third interference signal sequence to obtain the time difference between the second UAV and the third UAV includes: performing a correlation comparison between the second interference signal sequence and the third interference signal sequence; determining the second time point and the third time point corresponding to the maximum correlation value in the second interference signal sequence and the third interference signal sequence, respectively; and obtaining the time difference between the second UAV and the third UAV based on the second time point and the third time point.

[0013] In an exemplary embodiment, determining the time difference between each of the drones based on the interference signal sequence corresponding to each of the drones further includes: establishing a time difference matrix between the drones based on the time difference between the drones.

[0014] In an exemplary embodiment, determining the location information of the interference source based on the location information and time difference of each of the drones includes: constructing a system of equations based on the location information and time difference of each of the drones; and determining the location information of the interference source by solving the system of equations.

[0015] In an exemplary embodiment, constructing a set of equations based on the location information and time difference of each of the drones includes: acquiring a third set of interference measurement information; the third set of interference measurement information includes: fourth measurement data corresponding to the fourth drone and fifth measurement data corresponding to the fifth drone; the fourth measurement data includes: fourth drone location information and a fourth interference signal sequence; the fifth measurement data includes: fifth drone location information and a fifth interference signal sequence; obtaining the time difference between the fourth drone and the fifth drone based on the fourth interference signal sequence and the fifth interference signal sequence; and constructing equations based on the fourth drone location information, the fifth drone location information, and the time difference between the fourth drone and the fifth drone.

[0016] According to another aspect of this disclosure, an interference source location measuring device is provided, comprising: a measuring module configured to acquire measuring data collected by at least one unmanned aerial vehicle (UAV); the measuring data including at least: UAV location information and an interference signal sequence; and a positioning module configured to determine the location information of the interference source based on the measuring data.

[0017] According to another aspect of this disclosure, an electronic device is provided, comprising: one or more processors; and a storage device configured to store one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the interference source location measurement method as described in the above embodiments.

[0018] According to another aspect of this disclosure, a computer-readable storage medium is provided that stores a computer program, which, when executed by a processor, implements the interference source location measurement method as described in the above embodiments.

[0019] According to another aspect of this disclosure, a computer program product is provided, including a computer program / signaling, characterized in that, when the computer program / signaling is executed by a processor, it implements the interference source location measurement method as described in the above embodiments.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0022] Figure 1 shows a schematic diagram of a network architecture of a communication system applicable to an embodiment of this disclosure;

[0023] Figure 2 shows a flowchart of an interference source location measurement method according to an embodiment of the present disclosure;

[0024] Figure 3 shows a flowchart of the interference measurement process according to an embodiment of the present disclosure;

[0025] Figure 4 shows a flowchart of the interference source location determination method according to an embodiment of the present disclosure;

[0026] Figure 5 shows a flowchart of the time difference determination method according to an embodiment of the present disclosure;

[0027] Figure 6 shows a flowchart of the interference source location calculation method according to an embodiment of the present disclosure;

[0028] Figure 7 shows a schematic diagram of the structure of an interference source location measuring device according to an embodiment of the present disclosure;

[0029] Figure 8 shows a schematic diagram of the structure of an electronic device suitable for implementing exemplary embodiments of the present disclosure. Detailed Implementation

[0030] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0031] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0032] It should be noted that the ordinal numbers such as "first" and "second" mentioned in the embodiments of this disclosure are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects. Furthermore, the descriptions of "first" and "second" do not limit the objects to necessarily being different.

[0033] To address the aforementioned problems, this disclosure proposes a method for measuring the location of interference sources, applicable to various communication systems. Examples include GSM (Global System for Mobile Communications), CDMA (Code Division Multiple Access), WCDMA (Wideband Code Division Multiple Access), LTE (Long Term Evolution), LTE Frequency Division Duplex, LTE Time Division Duplex, UMTS (Universal Mobile Telecommunication System), WIMAX (Worldwide Interoperability for Microwave Access), 5G (5th generation), or future communication systems or other similar systems.

[0034] Figure 1 shows a schematic diagram of a network architecture of a communication system to which an embodiment of this disclosure applies. As shown in Figure 1, the network architecture includes UE, RAN (Radio Access Network) equipment, AMF (Access and Mobility Management Function) network elements, SMF (Session Management function) network elements, UPF (User Plane Function) network elements, PCF (Policy Control Function) network elements, NSSF (Network Slice Selection Function) network elements, NRF (Network Repository Function) network elements, NWDAF (Network Data Analytics Function) network elements, UDM (Unified Data Management) network elements, UDR (Unified Data Repository) network elements, ASF (Authentication Server Function) network elements, NEF (Network Exposure Function) network elements, AF (Application Function) network elements, and DN (Data Network) connecting to the operator's network.

[0035] The UE can be various electronic devices, deployed on land (indoor or outdoor, handheld, wearable, or vehicle-mounted); on water (e.g., ships); or in the air (e.g., airplanes, balloons, and satellites). The UE can communicate with the core network without RAN equipment, exchanging voice and / or data with RAN equipment. The UE can be a mobile phone, tablet, computer with wireless transceiver capabilities, mobile internet device, wearable device, virtual reality terminal device, augmented reality terminal device, wireless terminal in industrial control, wireless terminal in autonomous driving, wireless terminal in telemedicine, wireless terminal in smart grids, wireless terminal in transportation safety, wireless terminal in smart cities, wireless terminal in smart homes, etc. Optionally, the client applications installed in different UEs can be the same, or clients of the same type of application based on different operating systems. Depending on the terminal platform, the specific form of the application client can also differ; for example, the application client can be a mobile phone client, a computer client, etc.

[0036] RAN equipment is a device in the network used to connect UEs to the wireless network. It can include devices in the access network that communicate with wireless terminals through one or more sectors on the air interface. UEs can access AMF network elements through RAN equipment. Specifically, when a UE accesses an AMF network element through RAN equipment, the UE can access the AMF network element through network-side equipment such as 5G and later versions of base stations (e.g., 5G NR NB) or base stations in other communication systems (e.g., eNB base stations).

[0037] The AMF network element is mainly used for mobility management and access authentication / authorization of UEs, and is also responsible for transmitting user policies between UEs and PCF network elements.

[0038] SMF network elements are mainly used for session management, UE Internet Protocol address allocation and management, selection of manageable user plane functions, policy control, or terminal points for charging function interfaces, and downlink data notification, etc.

[0039] UPF network elements can be used for packet routing and forwarding, or QoS processing of user plane data. User data can be accessed to the DN through this network element.

[0040] PCF network elements are a unified policy framework used to guide network behavior, providing policy rule information for control plane functional network elements (such as AMF and SMF network elements).

[0041] The NSSF network element is mainly used to select the appropriate network slice for the UE's services.

[0042] NRF network elements are primarily used to provide registration and discovery functions for network elements or the services they provide.

[0043] NWDAF network elements can collect data from various network functions and perform analysis and prediction.

[0044] UDM network elements are mainly used to manage UE subscription information. For example, during the authentication process, they perform authentication vector calculation, key deduction, user identifier decryption, etc. In the resynchronization process, they verify AUTS according to the corresponding algorithm and initiate a re-authentication process.

[0045] UDR network elements are mainly used to store structured data information, including subscription information, policy information, and network data or service data with standard format definitions.

[0046] The AUSF network element is mainly used for security authentication of terminal devices.

[0047] The NEF (Network Element) is located between the 5G core network and external third-party application functions, and may also have some internal application functions. It is responsible for managing the network data exposed to the outside world. All external applications that want to access the internal data of the 5G core network must go through the NEF. The NEF provides corresponding security guarantees to ensure the security of external applications to the network, and provides functions such as opening up QoS customization capabilities for external applications, subscription to mobility state events, and distribution of application function requests.

[0048] AF (Application Filter) network elements are primarily used to convey application-side requests to the network side, such as QoS requirements and user state event subscriptions. AF network elements can be third-party functional entities or application services deployed by the operator. For third-party application functional entities, authorization processing can also be handled through NEF (Network Filter) network elements when interacting with the core network. For example, a third-party application function may directly send a request to an NEF network element. The NEF network element determines whether the AF network element is authorized to send the request. If the verification is successful, the request will be forwarded to the corresponding PCF (Physical Processing Filter) network element or UDM (User Filtering Filter) network element.

[0049] It should be understood that the aforementioned network elements or functions can be network components in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform).

[0050] Figure 2 shows a flowchart of an interference source location measurement method according to an embodiment of the present disclosure. As shown in Figure 2, the interference source location measurement method may include the following steps.

[0051] In step S210, at least one set of measurement data collected by a UAV is acquired; the measurement data includes at least: UAV location information and interference signal sequence.

[0052] In related technologies, interference source localization mainly includes two methods: field strength approximation and cross-location. The field strength approximation method tests the interference signal strength over a large area and infers the approximate location of the interference source based on the signal strength. The cross-location method utilizes a high-gain directional antenna to find the direction of strongest interference signal at different locations. By repeating this process at multiple locations, the area where the interference source is located can be delineated. The former is suitable for small areas, but its testing efficiency and accuracy are limited. The latter relies on high-gain antennas and multiple test points, making testing inconvenient. Therefore, a more efficient and convenient method for measuring the location of interference sources is needed.

[0053] In this embodiment of the disclosure, at least one set of measurement data collected by a drone is acquired. This measurement data includes at least: drone location information and a sequence of interference signals.

[0054] In an exemplary embodiment, the measurement data corresponds to each individual drone. Each drone performs data measurements at a specific location, obtaining drone location information and a sequence of interference signals collected at that location. The drone then binds and stores the drone location information and the interference signal sequence to form the measurement data.

[0055] In an exemplary embodiment, the at least one UAV is controlled to multiple different UAV locations to perform at least one round of interference measurement on the interference source, thereby obtaining at least one set of interference measurement information collected by the at least one UAV on the target interference source; each set of interference measurement information corresponds to each round of interference measurement; each set of interference measurement information includes the measurement data corresponding to each UAV.

[0056] In an exemplary embodiment, two or more drones carrying jamming receivers measure the target jamming source at different locations, acquiring at least one set of jamming measurement information. Each drone performs measurements in each round, and the measurement results from all drones are combined to form a set of jamming measurement information. By changing the drones' positions and performing multiple rounds of measurements, multiple sets of jamming measurement information can be obtained.

[0057] In this embodiment of the disclosure, each set of interference measurement information includes measurement data corresponding to each UAV. The measurement data corresponding to each UAV includes at least: UAV position information and an interference signal sequence. The UAV position information is the spatial coordinate information of the corresponding UAV during measurement. The interference signal sequence is the signal sequence of the target interference source received by the corresponding UAV during measurement. It should be noted that the interference signal sequence is the signal sequence of the target interference source received within a corresponding acquisition period. The acquisition time length can be preset. In addition, the interference signal sequence also includes time information corresponding to the signal sequence to determine the time point corresponding to each signal. The UAV position information and the interference signal sequence are in a corresponding relationship. That is, the UAV position information is the location of the UAV when the interference signal sequence is acquired.

[0058] In an exemplary embodiment, the multiple drones perform interference source measurements within a preset measurement range around the target interference source.

[0059] In step S220, the location information of the interference source is determined based on the measurement data.

[0060] In this embodiment, the interference measurement information includes measurement data collected by multiple drones through multiple rounds. Furthermore, the measurement data corresponding to each drone includes corresponding drone position information and interference signal sequences. By changing the positions of the drones, the target interference source can be measured from multiple different locations. By analyzing the at least one set of interference measurement information, the location information of the target interference source can be determined. This location information is the spatial coordinate information of the target interference source.

[0061] The interference source location measurement method provided in this disclosure obtains multiple sets of interference measurement information by controlling multiple drones to change positions and performing multiple rounds of measurements on the target interference source. This interference measurement information includes the drone position information and interference signal sequence corresponding to each drone. Based on these multiple sets of interference measurement information, the location information of the target interference source is determined. This method utilizes multiple drones for collaborative measurement, achieving the effect of a virtual antenna array and realizing high-precision interference source location measurement. Based on the flexibility of drone deployment and changes, the number of drones and the number of tests can be freely adjusted, improving the flexibility and measurement accuracy of interference source location.

[0062] As described in step S210, this disclosure utilizes the convenience of UAVs changing positions to control at least one UAV to change positions to multiple different UAV positions (each UAV corresponding to a different position) during the interference measurement process, so as to perform multiple rounds of interference measurement on the target interference source and obtain multiple sets of interference measurement information. Each set of interference measurement information corresponds to each round of interference measurement, that is, to a set of positions for each UAV.

[0063] Figure 3 shows a flowchart of the interference measurement process according to an embodiment of the present disclosure. As shown in Figure 3, each round of interference measurement process may include the following steps.

[0064] In step S310, the at least one UAV is controlled to move to the first UAV position corresponding to each of the UAVs.

[0065] In this embodiment of the disclosure, during each round of interference measurement, the multiple drones are controlled to change positions to a first drone position corresponding to each drone. This first drone position can be any location within the measurement range. In one round of interference measurement, each drone has an independent first drone position corresponding to it. After reaching the position, the drone records this position information as its corresponding drone position information.

[0066] In an exemplary embodiment, during the first round of interference measurement, each drone records its location information as follows: [x 11 ,y 11 ,z 11 ],[x 12 ,y12 ,z 12 ],…,[x 1N ,y 1N ,z 1N Where x, y, and z are the spatial coordinates of the UAV; the first subscript indicates the round of interference measurement; and the second subscript indicates the UAV's serial number.

[0067] In step S320, a first set of interference measurement information collected by the at least one UAV on the target interference source is obtained; the first set of interference measurement information includes first measurement data corresponding to each of the UAVs; the first measurement data includes at least: first UAV position information and first interference signal sequence.

[0068] In this embodiment of the disclosure, after each UAV reaches the designated first UAV position, each UAV synchronously collects a first set of interference measurement information from the target interference source. As mentioned above, this first set of interference measurement information includes first measurement data corresponding to each UAV. This first measurement data includes at least: the first UAV position information and a first interference signal sequence.

[0069] In an exemplary embodiment, each drone receives unified control commands and simultaneously measures the target interference source.

[0070] In an exemplary embodiment, the location information of the first drone is the location of the first drone corresponding to that drone. For example, the location information of the first drone corresponding to each drone is: [x 11 ,y 11 ,z 11 ],[x 12 ,y 12 ,z 12 ],…,[x 1N ,y 1N ,z 1N The drone uses BeiDou / GPS for positioning and synchronous recording.

[0071] In an exemplary embodiment, each drone synchronously measures the target interference source to obtain a corresponding first interference signal sequence. For example, the first interference signal sequence corresponding to each drone is: R 11 (t),R 12 (t),…,R 1N (t). The first subscript indicates the round of interference measurement; the second subscript indicates the serial number of the UAV.

[0072] In this embodiment, the interference measurement round is completed by recording the first drone position information and the first interference signal sequence corresponding to each drone. After completing this round of interference measurement, the aforementioned multi-round interference measurement process of S310 and S320 is repeated until sufficient measurement data is obtained. It should be noted that the interference source position measurement method provided in this disclosure can improve the accuracy of finally determining the position information of the target interference source by increasing the number of drones and the number of measurement rounds. Those skilled in the art can determine the number of drones used and the number of measurement rounds according to the actual accuracy requirements, and this disclosure does not limit the number.

[0073] The interference source location measurement method provided in this disclosure utilizes multiple drones for collaborative measurement. Based on the flexibility of drone deployment and transformation, the number of drones and the number of tests can be freely adjusted to obtain multiple sets of interference measurement information at low cost.

[0074] Figure 4 shows a flowchart of the interference source location determination method according to an embodiment of the present disclosure. As shown in Figure 4, the aforementioned step S220 may include the following steps.

[0075] In step S410, the time difference between each of the drones is determined based on the interference signal sequence corresponding to each of the drones.

[0076] In this embodiment of the disclosure, the time difference between each UAV receiving the target interference source signal can be calculated and determined based on the corresponding interference measurement information collected by each UAV in each interference measurement round.

[0077] In this embodiment of the disclosure, any two interference signal sequences corresponding to UAVs can be selected in any interference measurement round. By comparing the two interference signal sequences and comparing the time points corresponding to the same interference signals, the time difference between the two UAVs in that round can be determined.

[0078] By repeating the above comparison and calculation process, the time difference between any two drones in each round can be determined. Based on the time differences between the drones, a time difference matrix can be established. For example,

[0079] In this context, the superscript k indicates the round of interference measurement; the first and second subscripts indicate the serial numbers of the two UAVs corresponding to the time difference.

[0080] In step S420, the location information of the target interference source is determined based on the location information of each UAV and the time difference corresponding to each UAV.

[0081] In this embodiment of the disclosure, a mathematical model of the distance between the drone and the target interference source can be established based on the drone position information corresponding to each drone in each interference measurement round. By using the mathematical models of the distances between different drones and the target interference source, and the time difference between corresponding two drones, the position information of the target interference source can be determined.

[0082] The interference source location measurement method provided in this disclosure compares the interference signal sequences measured by two drones to obtain the time difference between the two drones. By combining this time difference with the corresponding location information of the drones, the location information of the target interference source can be determined.

[0083] Figure 5 shows a flowchart of the time difference determination method according to an embodiment of the present disclosure. As shown in Figure 5, the aforementioned step S410 may include the following steps.

[0084] In step S510, a second set of interference measurement information is acquired; the second set of interference measurement information includes: second measurement data corresponding to the second UAV and third measurement data corresponding to the third UAV; the second measurement data includes a second interference signal sequence; the third measurement data includes a third interference signal sequence.

[0085] In this embodiment of the disclosure, a second set of interference measurement information is acquired. This second set of interference measurement information is any one of at least one set of interference measurement information, corresponding to a certain interference measurement round. As mentioned above, this second set of interference measurement information includes measurement data corresponding to each UAV. This includes second measurement data corresponding to the second UAV and third measurement data corresponding to the third UAV. The second and third UAVs are any two of at least one UAV. The second measurement data includes a second interference signal sequence collected by the second UAV. The third measurement data includes a third interference signal sequence collected by the third UAV.

[0086] In step S520, the second interference signal sequence is compared with the third interference signal sequence to obtain the time difference between the second UAV and the third UAV.

[0087] In this embodiment, the second interference signal sequence collected by the second UAV is compared with the third interference signal sequence collected by the third UAV to determine the corresponding signal sequence characteristics of the interference signal sequences collected by the two UAVs. Based on the corresponding signal sequence characteristics between the two interference signal sequences, the corresponding time points are determined, and the time difference between the two UAVs in that round can be calculated.

[0088] In an exemplary embodiment, as shown in FIG5, the comparison process between the interference signal sequences may include the following steps.

[0089] In step S520a, the second interference signal sequence and the third interference signal sequence are compared for correlation.

[0090] In this embodiment of the disclosure, the second interference signal sequence and the third interference signal sequence are compared for correlation. This correlation comparison can be performed by comparing the correlation values ​​between the two interference signal sequences through correlation calculation.

[0091] In step S520b, the second time point and the third time point corresponding to the maximum correlation value in the second interference signal sequence and the third interference signal sequence are determined respectively.

[0092] In this embodiment of the disclosure, based on the correlation comparison in step S520a, the maximum correlation values ​​in the second and third interference signal sequences are determined respectively. This maximum correlation value means that the correlation between these two signal sequences is the highest. Based on this, it can be considered that the signal sequences are the same signals emitted by the target interference source and collected by the two drones respectively. The time points corresponding to the signal sequences with the maximum correlation value are determined as the second time point and the third time point, respectively.

[0093] In step S520c, the time difference between the second UAV and the third UAV is obtained based on the second time point and the third time point.

[0094] In this embodiment of the disclosure, the time difference between the second drone and the third drone can be calculated based on the second time point and the third time point. Where k represents the round of interference measurement; i and j represent the sequence numbers of the two corresponding UAVs. This time difference signifies the time difference between when the two UAVs receive the same signal emitted by the target interference source.

[0095] The interference source location measurement method provided in this embodiment uses correlation comparison of interference signal sequences measured by two UAVs to determine the signal sequence characteristics corresponding to the collected interference signal sequence, and then obtains the time difference between the two UAVs.

[0096] Figure 6 shows a flowchart of the interference source location calculation method according to an embodiment of the present disclosure. As shown in Figure 6, the aforementioned step S420 may include the following steps.

[0097] In step S610, a set of equations is constructed based on the location information and time difference of each UAV.

[0098] In this embodiment of the disclosure, the calculated time difference represents the time difference between when the two drones receive the same signal emitted by the target interference source. This time difference is caused by the distance difference between the locations of the two drones and the target interference source. Since radio signals travel at near the speed of light in space, this distance difference is directly proportional to the time difference.

[0099] In this embodiment of the disclosure, a system of equations is constructed based on the above relationships. Specifically, this system of equations can be constructed in the following manner.

[0100] S610a, acquire the third set of interference measurement information; the third set of interference measurement information includes: fourth measurement data corresponding to the fourth UAV and fifth measurement data corresponding to the fifth UAV; the fourth measurement data includes: fourth UAV position information and fourth interference signal sequence; the fifth measurement data includes: fifth UAV position information and fifth interference signal sequence.

[0101] S610b, the time difference between the fourth UAV and the fifth UAV is obtained based on the fourth interference signal sequence and the fifth interference signal sequence.

[0102] S610c, Based on the location information of the fourth UAV, the location information of the fifth UAV, and the time difference between the fourth UAV and the fifth UAV, an equation is constructed.

[0103] The third set of interference measurement information comprises any one of at least one set of interference measurement information, corresponding to a specific interference measurement round. This third set of interference measurement information includes: fourth measurement data corresponding to the fourth UAV and fifth measurement data corresponding to the fifth UAV. The fourth measurement data includes: the position information of the fourth UAV and a fourth interference signal sequence. The fifth measurement data includes: the position information of the fifth UAV and a fifth interference signal sequence. The fourth and fifth UAVs are any two of at least one UAV.

[0104] The time difference between the fourth and fifth UAVs can be calculated using the aforementioned method; the specific calculation process will not be elaborated here.

[0105] As mentioned earlier, since the distance difference is directly proportional to the time difference, an equation is constructed between the fourth and fifth UAVs.

[0106] Where k represents the round of interference measurement; i and j represent the serial numbers of the two corresponding UAVs; [x0, y0, z0] represents the position coordinates of the target interference source; [x ki ,y ki ,z ki[x] represents the position coordinates of drone i; kj ,y kj ,z kj [] represents the position coordinates of drone j; Let i represent the time difference between drones i and j; C represents the speed of light.

[0107] Using the above method, several similar equations can be constructed based on multiple interference measurement rounds and between any two UAVs, thus forming a set of equations.

[0108] In step S620, the location information of the target interference source is determined by solving the system of equations.

[0109] In this embodiment of the disclosure, the location information [x0, y0, z0] of the target interference source can be determined by solving the above-mentioned system of equations. It should be noted that the accuracy of the target interference source location information obtained by the measurement can be improved by increasing the number of UAVs and the number of test rounds.

[0110] The interference source location measurement method provided in this disclosure utilizes the mathematical relationship between the time difference between two UAVs and the distance difference between the two UAVs and the target interference source to construct multiple equations, forming a system of equations. Solving this system of equations determines the location information of the target interference source. Due to the flexibility of UAV deployment and transformation, multiple sets of interference measurement information can be easily obtained, thereby constructing more equations. Therefore, this method can improve the accuracy of the target interference source location information obtained by increasing the number of UAVs and the number of test rounds.

[0111] In some embodiments, two drones are used to measure three rounds, as an example.

[0112] 1) Two drones were used. The first measurement showed their deployment coordinates as [x...]. 11 ,y 11 ,z 11 ] and [x 12 ,y 12 ,z 12 The received interference signal sequences are R 11 (t) and R 12 (t);

[0113] 2) Perform correlation calculations on the interference signal sequences received by the two UAVs to obtain the time difference between the arrival of the interference source signal on the two UAVs.

[0114] 3) Repeat steps 1) and 2) at different drone measurement locations, performing a total of 3 measurements to obtain the results.

[0115] 4) Based on the position of the UAV and the time difference of signal arrival for each measurement, the following set of equations is obtained:

[0116] 5) Solve the equations to obtain the target interference source location coordinates [x0, y0, z0].

[0117] Based on the same inventive concept, this disclosure provides an interference source location measuring device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the method embodiments described above, the implementation of this interference source location measuring device can be referenced in the implementation of the method embodiments described above, and repeated details will not be elaborated further.

[0118] Figure 7 shows a schematic diagram of an interference source location measuring device according to an embodiment of the present disclosure. As shown in Figure 7, the interference source location measuring device 700 may include a measuring module 710 and a positioning module 720.

[0119] The measurement module 710 is configured to acquire at least one set of measurement data collected by a UAV; the measurement data includes at least: UAV location information and interference signal sequence.

[0120] The positioning module 720 is configured to determine the location information of the interference source based on the measurement data.

[0121] In an exemplary embodiment, the measurement data corresponds to each of the drones.

[0122] In an exemplary embodiment, the measurement module 710 is further configured to control the at least one UAV to multiple different UAV locations to perform at least one round of interference measurement on the interference source, obtaining at least one set of interference measurement information; each set of interference measurement information corresponds to each round of interference measurement; each set of interference measurement information includes the measurement data corresponding to each UAV; wherein, each round of interference measurement process includes: controlling the at least one UAV to a first UAV location corresponding to each of the UAVs; acquiring a first set of interference measurement information collected by the at least one UAV on the interference source; the first set of interference measurement information includes first measurement data corresponding to each of the UAVs; the first measurement data includes at least: first UAV location information and a first interference signal sequence.

[0123] In an exemplary embodiment, the positioning module 720 includes a time difference calculation module 721 and a position calculation module 722; the time difference calculation module 721 is configured to determine the time difference between each of the drones based on the interference signal sequence corresponding to each of the drones; the position calculation module 722 is configured to determine the position information of the target interference source based on the drone position information and the time difference corresponding to each of the drones.

[0124] In an exemplary embodiment, the time difference calculation module 721 is further configured to acquire a second set of interference measurement information; the second set of interference measurement information includes: second measurement data corresponding to the second UAV and third measurement data corresponding to the third UAV; the second measurement data includes a second interference signal sequence; the third measurement data includes a third interference signal sequence; the second interference signal sequence and the third interference signal sequence are compared to obtain the time difference between the second UAV and the third UAV.

[0125] In an exemplary embodiment, the time difference calculation module 721 is further configured to perform a correlation comparison between the second interference signal sequence and the third interference signal sequence; determine the second time point and the third time point corresponding to the maximum correlation value in the second interference signal sequence and the third interference signal sequence, respectively; and obtain the time difference between the second UAV and the third UAV based on the second time point and the third time point.

[0126] In an exemplary embodiment, the time difference calculation module 721 is further configured to establish a time difference matrix between the various drones based on the time difference between the various drones.

[0127] In an exemplary embodiment, the position calculation module 722 is further configured to construct a set of equations based on the position information and time difference of each of the drones; and to determine the position information of the target interference source by solving the set of equations.

[0128] In an exemplary embodiment, the position calculation module 722 is further configured to acquire a third set of interference measurement information; the third set of interference measurement information includes: fourth measurement data corresponding to the fourth UAV and fifth measurement data corresponding to the fifth UAV; the fourth measurement data includes: fourth UAV position information and a fourth interference signal sequence; the fifth measurement data includes: fifth UAV position information and a fifth interference signal sequence; the time difference between the fourth UAV and the fifth UAV is obtained based on the fourth interference signal sequence and the fifth interference signal sequence; and an equation is constructed based on the fourth UAV position information, the fifth UAV position information, and the time difference between the fourth UAV and the fifth UAV.

[0129] Figure 8 shows a schematic diagram of the structure of an electronic device suitable for implementing exemplary embodiments of the present disclosure. An electronic device 800 according to this embodiment of the present disclosure will now be described with reference to Figure 8. The electronic device 800 shown in Figure 8 is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0130] As shown in Figure 8, the electronic device 800 is presented in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, a bus 830 connecting different system components (including storage unit 820 and processing unit 810), and a display unit 840.

[0131] Storage unit 820 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 8201 and / or cache memory 8202, and may further include a read-only memory (ROM) 8203.

[0132] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0133] Bus 830 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0134] Electronic device 800 can also communicate with one or more external devices 870 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 800, and / or with any device that enables electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 850. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0135] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored.

[0136] In some possible implementations, various aspects of the present invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention.

[0137] According to embodiments of the present invention, a program product for implementing the above-described method may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with a signaling execution system, apparatus, or device.

[0138] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0139] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with a signaling execution system, apparatus, or device.

[0140] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0141] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0142] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0143] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0144] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several signaling instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the method according to the embodiments of this disclosure. Industrial applicability

[0145] This disclosure applies to the field of communication technology and is used to solve the problem of accurate location of interference sources in related technologies. By using multiple UAVs to conduct collaborative measurements, the effect of a virtual antenna array is achieved, realizing high-precision interference source location measurement. Based on the flexibility of UAV deployment and transformation, the number of UAVs and the number of tests can be freely adjusted, improving the flexibility and measurement accuracy of interference source location.

[0146] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

[0147] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for measuring the location of an interference source, the method comprising: Acquire at least one set of measurement data collected by a drone; The measurement data includes at least: UAV location information and interference signal sequence; Based on the measurement data, the location information of the interference source is determined.

2. The method according to claim 1, characterized in that, The measurement data corresponds to each of the aforementioned drones.

3. The method according to claim 2, wherein, The acquisition of at least one set of measurement data collected by a drone includes: The at least one UAV is controlled to multiple different UAV locations to perform at least one round of interference measurement on the interference source, obtaining at least one set of interference measurement information; each set of interference measurement information corresponds to each round of interference measurement; each set of interference measurement information includes the measurement data corresponding to each UAV. The interference measurement process for each round includes: Control the at least one drone to the position of the first drone corresponding to each of the drones; The first set of interference measurement information collected by the at least one UAV from the interference source is obtained; the first set of interference measurement information includes first measurement data corresponding to each of the UAVs; the first measurement data includes at least: the position information of the first UAV and the first interference signal sequence.

4. The method according to claim 2, wherein, Determining the location information of the interference source based on the measurement data includes: Based on the interference signal sequence corresponding to each of the UAVs, determine the time difference between each interference signal sequence; The location information of the interference source is determined based on the location information of each drone and the time difference.

5. The method according to claim 4, wherein, Determining the time difference between each interference signal sequence based on the interference signal sequence corresponding to each of the UAVs includes: Acquire a second set of interference measurement information; the second set of interference measurement information includes: second measurement data corresponding to the second UAV and third measurement data corresponding to the third UAV; the second measurement data includes a second interference signal sequence; the third measurement data includes a third interference signal sequence; The second interference signal sequence is compared with the third interference signal sequence to obtain the time difference between the second interference signal sequence and the third interference signal sequence.

6. The method according to claim 5, wherein, The step of comparing the second interference signal sequence with the third interference signal sequence to obtain the time difference between the second interference signal sequence and the third interference signal sequence includes: The second interference signal sequence is compared with the third interference signal sequence in terms of correlation. Determine the second and third time points corresponding to the maximum correlation values ​​in the second and third interference signal sequences, respectively; Based on the second and third time points, the time difference between the second and third interference signal sequences is obtained.

7. The method according to claim 4, wherein, The step of determining the time difference between each interference signal sequence based on the interference signal sequence corresponding to each of the UAVs further includes: A time difference matrix is ​​established between each of the interference signal sequences based on the time difference between each of the interference signal sequences.

8. The method according to claim 4, wherein, The step of determining the location information of the interference source based on the location information and time difference of each of the drones includes: Based on the location information and time difference of each drone, a system of equations is constructed. The location information of the interference source can be determined by solving the system of equations.

9. The method according to claim 8, wherein, The step of constructing a system of equations based on the corresponding drone position information and time difference of each drone includes: Acquire a third set of interference measurement information; the third set of interference measurement information includes: fourth measurement data corresponding to the fourth UAV and fifth measurement data corresponding to the fifth UAV; the fourth measurement data includes: the position information of the fourth UAV and the fourth interference signal sequence; the fifth measurement data includes: the position information of the fifth UAV and the fifth interference signal sequence; The time difference between the fourth interference signal sequence and the fifth interference signal sequence is obtained based on the fourth interference signal sequence and the fifth interference signal sequence. Based on the location information of the fourth UAV, the location information of the fifth UAV, and the time difference between the fourth and fifth interference signal sequences, an equation is constructed.

10. An interference source location measuring device, comprising: The measurement module is configured to acquire at least one set of measurement data collected by a drone; The measurement data includes at least: UAV location information and interference signal sequence; The positioning module is configured to determine the location information of the interference source based on the measurement data.

11. An electronic device, comprising: One or more processors; A storage device configured to store one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 9.

12. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 9.

13. A computer program product comprising a computer program / signaling that, when executed by a processor, implements the method as described in any one of claims 1 to 9.

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