A method, device, medium, and product for locating unmanned aerial vehicles (UAVs).

Abstract

This invention discloses a method, device, medium, and product for UAV positioning, relating to the field of UAV positioning. It first establishes n reconnaissance stations; at each reconnaissance station, it receives electromagnetic wave signals emitted by the UAV and obtains timestamped IQ signals through digital signal processing; then, at each reconnaissance station, it estimates the absolute arrival time of the signal using a time estimation method based on the Zadoff-Chu sequence; finally, it uploads the estimated absolute times from each reconnaissance station to a host computer system, where the host computer system calculates the UAV's position using the TDOA method. This invention utilizes the Zadoff-Chu sequence contained in the UAV signal frame for positioning, making the method simple and efficient; simultaneously, it leverages the unique CP sequence characteristics of the OFDM signal segment to improve the accuracy of time delay estimation, thereby improving the UAV positioning accuracy.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) positioning, and specifically to a UAV positioning method, device, medium, and product. Background Technology

[0002] The statements in this section are provided only as background information in relation to this disclosure and may not constitute prior art.

[0003] In recent years, the rapid development of UAV technology has brought many conveniences, but also new safety challenges. Accurate and reliable UAV positioning technology has become crucial for ensuring airspace safety and efficient management. Compared to traditional active radar-based positioning methods, passive positioning technology utilizes only electromagnetic radiation sources in the environment for positioning, offering advantages such as stealth, anti-interference capabilities, and low cost. It has shown great potential in the field of UAV positioning and has become a research hotspot. Passive Time Difference of Arrival (TDOA) technology, due to its simplicity and high accuracy, has become the main method used for passive UAV positioning. This method infers the relative position of the target object to each reference base station by solving a system of nonlinear hyperbolic equations based on the distance difference between each reference base station and the target object.

[0004] One of the core technologies of TDOA is time delay estimation. Even with the same data, using different time delay estimation techniques can yield significant differences in the final positioning results. Currently, time delay estimation techniques mainly employ cross-correlation, generalized cross-correlation (GCC), adaptive filtering, and maximum likelihood estimation.

[0005] Among them, the TDOA UAV positioning method based on cross-correlation estimates the time delay between signals by detecting the peak value of the correlation function of the signals. The method is simple and highly accurate, but it is easily affected by noise, multipath effect and other factors in actual engineering, resulting in large positioning errors in actual engineering.

[0006] The TDOA UAV positioning method based on the generalized cross-correlation method first calculates the cross-power spectrum of the signal, then adds different weighting functions to highlight the correlation peak according to different situations, and finally uses the generalized cross-correlation function to find the peak position and obtain the signal arrival time. This method has high complexity and is easily affected by noise, resulting in poor positioning accuracy.

[0007] The core of the TDOA UAV positioning method based on adaptive filtering is to process the received signal through an adaptive filter in order to estimate the signal arrival time. This method has high computational complexity and its performance is extremely dependent on the filter parameter settings, which may not be very applicable to practical engineering.

[0008] In summary, current time delay estimation methods all suffer from drawbacks such as high complexity and poor positioning accuracy in UAV positioning. Summary of the Invention

[0009] The purpose of this invention is to address the shortcomings of current UAV positioning methods, such as high complexity and poor accuracy, by providing a UAV positioning method, device, medium, and product that solves the aforementioned problems.

[0010] The technical solution of the present invention is as follows:

[0011] A method for locating unmanned aerial vehicles (UAVs), comprising:

[0012] Step S1: Set up n reconnaissance stations;

[0013] Step S2: Receive the electromagnetic wave signal emitted by the UAV at each reconnaissance station and obtain the IQ signal with timestamp through digital signal processing;

[0014] Step S3: Estimate the absolute time of signal arrival at each reconnaissance station using a time estimation method based on the Zadoff-Chu sequence;

[0015] Step S4: Upload the estimated absolute time of each reconnaissance station to the host computer system, and use the TDOA method in the host computer system to calculate the position of the UAV.

[0016] Furthermore, the time estimation method based on the Zadoff-Chu sequence in step S3 includes:

[0017] Step S31: Use the multi-template cross-correlation matching method to determine whether the Zadoff-Chu sequence exists. If it exists, proceed to step S32; otherwise, terminate the process.

[0018] Step S32: If the Zadoff-Chu sequence exists, the absolute arrival time of the signal is obtained using the CP sequence blind matching method.

[0019] Furthermore, the multi-template cross-correlation matching method in step S31 includes:

[0020] Step S311: Generate a local LFM signal with a frequency consistent with the scanning parameters of the reconnaissance station;

[0021] Step S312: Generate a multi-mode LFM signal composed of multiple local LFM signals with a frequency difference of 9 MHz, covering a total range of 99MHz;

[0022] Step S313: Perform time-domain cross-correlation between the IQ signal and the multi-mode LFM signal;

[0023] Step S314: If the cross-correlation result shows a cross-correlation peak exceeding a preset threshold, then the Zadoff-Chu sequence is determined to exist; otherwise, the process is terminated.

[0024] Further, step S313 includes:

[0025] make For IQ signals, For multi-template LFM signals, the time-domain cross-correlation function between the two is... The calculation is as follows:

[0026]

[0027] in:

[0028] express After Fourier transform, the conjugate is obtained.

[0029] Further, step S314 includes:

[0030] like If the condition is met, the Zadoff-Chu sequence is determined to exist; otherwise, the process is terminated.

[0031] Further, the CP sequence blind matching method in step S32 includes:

[0032] Step S321: Set the length of the cyclic prefix (CP) sequence of the OFDM signal to... The length of the symbol body is ;in, , ;

[0033] Step S322: Set initial value , Two CP sequence signal segments were obtained through the IQ signal, namely... and ;in, The starting sampling point number; This means that in the IQ signal, starting from the initial sampling point, a length of... The sub-signal segment; In the IQ signal, this means that from the initial sampling point onwards... Starting with length, take the length as... The sub-signal segment;

[0034] Step S323: Calculation and Pearson correlation value ;

[0035] Step S324: Iterate through all distinct... and Pearson correlation value under the given value ;

[0036] Step S325: Take the maximum value value corresponding The optimal CP sequence length for the OFDM signal. The optimal symbol length for an OFDM signal;

[0037] Step S326: Based on the optimal CP sequence length and optimal symbol body length Two CP sequence signal segments were obtained through the IQ signal, namely... and ;in, This means that in the IQ signal, starting from the initial sampling point, a length of... The sub-signal segment; In the IQ signal, this means that from the initial sampling point onwards... Starting with length, take the length as... The sub-signal segment;

[0038] Step S327: Calculation and The autocorrelation is used to estimate the time delay, which is the absolute time of signal arrival.

[0039] Furthermore, in step S1, n ≥ 3.

[0040] The present invention also proposes an electronic device, comprising:

[0041] At least one processor; and a memory communicatively connected to said at least one processor;

[0042] The memory stores instructions that can be executed by the at least one processor. By executing the instructions stored in the memory, the at least one processor performs a UAV positioning method as described above.

[0043] The present invention also proposes a computer-readable storage medium for storing instructions that, when executed, enable the implementation of a UAV positioning method as described above.

[0044] The present invention also proposes a computer program product, which, when executed by a processor, implements the above-described UAV positioning method.

[0045] Compared with existing technologies, the advantages of this invention are:

[0046] This invention utilizes the ZC sequence contained in the UAV signal frame for positioning, which is simple and efficient. At the same time, it utilizes the unique CP sequence characteristics of the OFDM signal segment to improve the time delay estimation accuracy, thereby improving the UAV positioning accuracy. Attached Figure Description

[0047] Figure 1 Here is a flowchart of a drone positioning method;

[0048] Figure 2 This is a detailed flowchart of step S3 in an embodiment of the present invention;

[0049] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention; Detailed Implementation

[0050] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0051] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0052] Example 1

[0053] Current passive positioning systems for unmanned aerial vehicles (UAVs) generally employ the Time Delay Allocation (TDOA) system, the core step of which is "estimating the time difference of arrival (TDOA) of signals received from the same UAV by various reconnaissance stations." In this system, the time delay estimation technique directly determines the final positioning accuracy. Currently, three common methods are used:

[0054] (1) Direct cross-correlation method - directly calculate the cross-correlation function of the two received signals, and use the peak value to correspond to the time delay;

[0055] (2) Generalized cross-correlation method - first, the signal is weighted in the frequency domain, and then the cross-correlation is calculated;

[0056] (3) Adaptive filtering method - using an adaptive filter to dynamically track the time delay parameter.

[0057] However, the aforementioned methods all exhibit drawbacks such as high complexity and poor positioning accuracy in actual UAV usage environments. Therefore, this embodiment proposes a new time-of-arrival measurement procedure based on the UAV signal frame structure and utilizing the inherent Zadoff-Chu sequence and cyclic prefix sequence within the frame. By processing this signal segment, the UAV can be located. This method is simple and has high positioning accuracy, thereby reducing complexity and improving precision.

[0058] In this embodiment, for details, please refer to... Figure 1 A method for locating unmanned aerial vehicles (UAVs) specifically includes the following steps:

[0059] Step S1: Set up n reconnaissance stations;

[0060] In this embodiment, specifically, n≥3 in step S1;

[0061] Step S2: Receive the electromagnetic wave signal emitted by the UAV at each reconnaissance station and obtain the IQ signal with timestamp through digital signal processing;

[0062] Step S3: Estimate the absolute time of signal arrival at each reconnaissance station using a time estimation method based on the Zadoff-Chu sequence;

[0063] Step S4: Upload the estimated absolute time of each reconnaissance station to the host computer system, and use the TDOA method in the host computer system to calculate the position of the UAV.

[0064] In this embodiment, for details, please refer to... Figure 2 The time estimation method based on the Zadoff-Chu sequence in step S3 includes:

[0065] Step S31: Use the multi-template cross-correlation matching method to determine whether the Zadoff-Chu sequence exists. If it exists, proceed to step S32; otherwise, terminate the process.

[0066] Step S32: If the Zadoff-Chu sequence exists, the absolute arrival time of the signal is obtained using the CP sequence blind matching method.

[0067] In this embodiment, for details, please refer to... Figure 2 The multi-template cross-correlation matching method in step S31 includes:

[0068] Step S311: Generate a local LFM signal with a frequency consistent with the scanning parameters of the reconnaissance station;

[0069] Step S312: Generate a multi-template LFM signal composed of multiple local LFM signals with a frequency difference of 9 MHz, covering a total range of 99 MHz; that is, use multiple local LFM signals to generate a multi-template LFM signal, where the frequency difference between adjacent templates is 9 MHz, for a total of 99 MHz;

[0070] Step S313: Perform time-domain cross-correlation between the IQ signal and the multi-mode LFM signal;

[0071] Step S314: If the cross-correlation result shows a cross-correlation peak exceeding a preset threshold, then the Zadoff-Chu sequence is determined to exist; otherwise, the process is terminated.

[0072] In this embodiment, specifically, step S313 includes:

[0073] make For IQ signals, For multi-template LFM signals, the time-domain cross-correlation function between the two is... The calculation is as follows:

[0074]

[0075] in:

[0076] express After Fourier transform, the conjugate is obtained.

[0077] In this embodiment, specifically, step S314 includes:

[0078] like If the Zadoff-Chu sequence exists, the process is terminated; otherwise, the process is terminated. If yes, it means the input signal segment contains drone signals, continue to the next step; otherwise, terminate the process.

[0079] In this embodiment, for details, please refer to [reference needed]. Figure 2 The CP sequence blind matching method in step S32 includes:

[0080] Step S321: Set the length of the cyclic prefix (CP) sequence of the OFDM signal to... The length of the symbol body is ;in, , ;

[0081] Step S322: Set initial value , Two CP sequence signal segments were obtained through the IQ signal, namely... and ;in, The starting sampling point number; This means that in the IQ signal, starting from the initial sampling point, a length of... The sub-signal segment; In the IQ signal, this means that from the initial sampling point onwards... Starting with length, take the length as... The sub-signal segment;

[0082] Step S323: Calculation and Pearson correlation value ;

[0083] Step S324: Iterate through all distinct... and Pearson correlation value under the given value ;

[0084] Step S325: Take the maximum value value corresponding The optimal CP sequence length for the OFDM signal. The optimal symbol length for an OFDM signal;

[0085] Step S326: Based on the optimal CP sequence length and optimal symbol body length Two CP sequence signal segments were obtained through the IQ signal, namely... and ;

[0086] in, This means that in the IQ signal, starting from the initial sampling point, a length of... The sub-signal segment; In the IQ signal, this means that from the initial sampling point onwards... Starting with length, take the length as... The sub-signal segment;

[0087] Step S327: Calculation and The autocorrelation is used to estimate the time delay, which is the absolute time of signal arrival.

[0088] Based on the same technical concept, embodiments of the present invention also provide an electronic device that can implement the UAV positioning method flow provided in the above embodiments of the present invention. In one embodiment, the electronic device may be a server, a terminal device, or other electronic devices. Figure 3 As shown, the electronic device may include:

[0089] At least one processor and a memory connected to the at least one processor. In this embodiment of the invention, the specific connection medium between the processor and the memory is not limited. Figure 3 The example used is the connection between the processor and memory via a bus. The bus... Figure 3 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. Buses can be divided into address buses, data buses, control buses, etc., but for ease of representation, [the specific bus type is not shown here]. Figure 3 The processor is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, a processor can also be called a controller; there are no restrictions on the name.

[0090] In this embodiment of the invention, the memory stores instructions executable by at least one processor. By executing the instructions stored in the memory, the at least one processor can perform a UAV positioning method as described above. The processor can implement... Figure 3 The functions of each module in the device shown.

[0091] The processor is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory and calling data stored in memory, it can monitor the device's various functions and process data, thereby enabling overall monitoring of the device.

[0092] In an alternative design, the processor may include one or more processing units. The processor may integrate an application processor and a modem processor, wherein the application processor primarily handles the operating system, user interface, and applications, while the modem processor primarily handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. In some embodiments, the processor and memory may be implemented on the same chip; in some embodiments, they may also be implemented separately on separate chips.

[0093] The processor can be a general-purpose processor, such as a CPU, digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the UAV positioning method disclosed in the embodiments of this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0094] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic memory, magnetic disk, optical disk, etc. Memory is any other medium capable of carrying or storing desired program code in the form of instructions or data structures, and accessible by a computer, but is not limited thereto. In embodiments of the present invention, memory can also be a circuit or any other device capable of implementing storage functions, used to store program instructions and / or data.

[0095] By designing and programming the processor, the code corresponding to the UAV positioning method described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute the steps of the method described in the foregoing embodiments during runtime. How to design and program the processor is a technique well-known to those skilled in the art and will not be elaborated upon here.

[0096] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a UAV positioning method described above.

[0097] In some alternative embodiments, the present invention also provides that various aspects of the UAV positioning method can also be implemented in the form of a program product, which includes program code that, when the program product is run on a device, causes the control device to perform the steps in the UAV positioning method according to various exemplary embodiments of the present invention described above.

[0098] It should be noted that although several units or sub-units of the apparatus have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the invention, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units. Furthermore, although the operation of the method of the invention is described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0099] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0100] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a server, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0101] Program code for performing the operations of this invention can be written using any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed 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.

[0102] 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).

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] In addition, in some embodiments, a computer program product is also proposed, which, when executed by a processor, implements the above-described UAV positioning method.

[0106] The embodiments described above merely illustrate specific implementation methods of this application, and while the descriptions are detailed and specific, they should not be construed as limiting the scope of protection of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the technical solution of this application, and these modifications and improvements all fall within the scope of protection of this application.

[0107] This background section is provided to generally present the context of the invention. The work of the currently named inventors, the work to the extent described in this background section, and aspects of this section that did not constitute prior art at the time of application are neither expressly nor impliedly acknowledged as prior art to the invention.

Claims

1. A method for locating unmanned aerial vehicles (UAVs), characterized in that, include: Step S1: Set up n reconnaissance stations; Step S2: Receive the electromagnetic wave signal emitted by the UAV at each reconnaissance station and obtain the IQ signal with timestamp through digital signal processing; Step S3: Estimate the absolute time of signal arrival at each reconnaissance station using a time estimation method based on the Zadoff-Chu sequence; Step S4: Upload the estimated absolute time of each reconnaissance station to the host computer system, and use the TDOA method in the host computer system to calculate the position of the UAV; The time estimation method based on the Zadoff-Chu sequence in step S3 includes: Step S31: Use the multi-template cross-correlation matching method to determine whether the Zadoff-Chu sequence exists. If it exists, proceed to step S32; otherwise, terminate the process. Step S32: If the Zadoff-Chu sequence exists, the absolute arrival time of the signal is obtained using the CP sequence blind matching method; The multi-template cross-correlation matching method in step S31 includes: Step S311: Generate a local LFM signal with a frequency consistent with the scanning parameters of the reconnaissance station; Step S312: Generate a multi-mode LFM signal composed of multiple local LFM signals with a frequency difference of 9 MHz, covering a total range of 99 MHz; Step S313: Perform time-domain cross-correlation between the IQ signal and the multi-mode LFM signal; Step S314: If the cross-correlation result shows a cross-correlation peak exceeding a preset threshold, then the Zadoff-Chu sequence is determined to exist; otherwise, the process is terminated.

2. The UAV positioning method according to claim 1, characterized in that, Step S313 includes: make For IQ signals, For multi-template LFM signals, the time-domain cross-correlation function between the two is... The calculation is as follows: in: express After Fourier transform, the conjugate is obtained.

3. The UAV positioning method according to claim 2, characterized in that, Step S314 includes: like If the condition is met, the Zadoff-Chu sequence is determined to exist; otherwise, the process is terminated.

4. The UAV positioning method according to claim 3, characterized in that, The CP sequence blind matching method in step S32 includes: Step S321: Set the length of the cyclic prefix (CP) sequence of the OFDM signal to... The length of the symbol body is ;in, , ; Step S322: Set initial value , Two CP sequence signal segments were obtained through the IQ signal, namely... and ;in, The starting sampling point number; This means that in the IQ signal, starting from the initial sampling point, a length of... The sub-signal segment; In the IQ signal, this means that from the initial sampling point onwards... Starting with length, take the length as... The sub-signal segment; Step S323: Calculate and Pearson correlation value ; Step S324: Iterate through all distinct... and Pearson correlation value under the given value ; Step S325: Take the maximum value value corresponding The optimal CP sequence length for the OFDM signal. The optimal symbol length for an OFDM signal; Step S326: Based on the optimal CP sequence length and optimal symbol body length Two CP sequence signal segments were obtained through the IQ signal, namely... and ;in, This means that in the IQ signal, starting from the initial sampling point, a length of... The sub-signal segment; In the IQ signal, this means that from the initial sampling point onwards... Starting with length, take the length as... The sub-signal segment; Step S327: Calculation and The autocorrelation is used to estimate the time delay, which is the absolute time of signal arrival.

5. The UAV positioning method according to claim 1, characterized in that, In step S1, n ≥ 3.

6. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the at least one processor executes the instructions stored in the memory to perform a UAV positioning method as described in any one of claims 1-5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store instructions that, when executed, enable the UAV positioning method as described in any one of claims 1-5.

8. A computer program product, characterized in that, When the computer program is executed by the processor, it implements a drone positioning method according to any one of claims 1-5.

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