Positioning method, positioning device, mobile carrier and storage medium

Abstract

The application discloses a positioning method, a positioning device, a mobile carrier and a storage medium. The mobile carrier comprises a first antenna, a second antenna and a clock source, the first antenna and the second antenna are time-frequency synchronized through the shared clock source, and are used for receiving radio frequency signals transmitted by a radio frequency signal source. The positioning method comprises the following steps: acquiring motion parameters, a first radio frequency signal and a second radio frequency signal of the mobile carrier, the motion parameters comprising a translation parameter and a rotation parameter; processing the first radio frequency signal and the second radio frequency signal, and extracting an actual Doppler frequency difference therebetween; determining an azimuth angle of the radio frequency signal source based on the actual Doppler frequency difference and the rotation parameter; and determining the position of the radio frequency signal source based on the azimuth angle and the translation parameter. In this way, the application realizes high-precision passive positioning of the radio frequency signal source by the single mobile carrier through the double-antenna reception of the radio frequency signal and the extraction of the Doppler frequency difference, the rotation direction finding and the translation positioning, and has the advantages of flexible deployment and high positioning precision.

Description

Technical Field

[0001] This application relates to the field of positioning technology, and in particular to a positioning method, positioning device, mobile carrier, and storage medium. Background Technology

[0002] With the rapid development of the low-altitude economy and the application of unmanned aerial vehicles (UAVs), the demand for high-precision positioning of ground-based radio frequency (RF) signal sources is gradually increasing. Related technologies typically employ fixed ground monitoring stations or direction-finding systems based on antenna arrays to locate RF signal sources.

[0003] However, ground-based fixed monitoring stations require multi-station joint monitoring, resulting in high system construction and maintenance costs, and the coverage is limited by the location of the stations, making it difficult to deploy flexibly in complex environments; direction-finding systems based on antenna arrays require multiple antennas to form a specific array, resulting in a large system size that cannot be applied to compact platforms such as micro-sized UAVs. Summary of the Invention

[0004] In view of the above problems, this application provides a positioning method, positioning device, mobile carrier and storage medium, which can effectively improve the problems in related technical solutions.

[0005] This application proposes a method for locating a radio frequency (RF) signal source for a mobile carrier. The mobile carrier includes a first antenna, a second antenna, and a clock source. The first antenna and the second antenna achieve time-frequency synchronization by sharing the clock source. Both the first antenna and the second antenna are used to receive RF signals transmitted by the RF signal source. The method includes: The motion parameters of the mobile carrier, the first radio frequency signal received by the first antenna, and the second radio frequency signal received by the second antenna are acquired in real time, wherein the motion parameters include translational parameters and rotational parameters; The first radio frequency signal and the second radio frequency signal are processed to extract the actual Doppler frequency difference between the first radio frequency signal and the second radio frequency signal; Based on the actual Doppler frequency difference and the rotation parameters, the azimuth angle of the radio frequency signal source relative to the moving carrier is determined; The position of the radio frequency signal source relative to the moving carrier is determined based on the azimuth angle and the translational parameters of the moving carrier.

[0006] In some embodiments, processing the first radio frequency signal and the second radio frequency signal to extract the actual Doppler frequency difference between the first radio frequency signal and the second radio frequency signal includes: Perform a conjugate multiplication operation on the first radio frequency signal and the second radio frequency signal to obtain a conjugate multiplied signal; The actual Doppler frequency difference is obtained by performing time-frequency processing on the conjugate multiplication signal.

[0007] In some embodiments, time-frequency processing is performed on the conjugate multiplication signal to obtain the actual Doppler frequency difference, including: Perform a short-time Fourier transform on the conjugate multiplied signal to obtain the time-frequency power spectrum; The target time-frequency power spectrum is obtained by convolving and smoothing the time-frequency power spectrum along the frequency dimension using a Gaussian kernel function. The actual Doppler frequency difference is extracted based on the target time-frequency power spectrum.

[0008] In some embodiments, determining the azimuth angle of the radio frequency signal source relative to the moving carrier based on the actual Doppler frequency difference and the rotation parameters includes: Based on the rotation parameters and multiple preset candidate azimuth angles, a theoretical Doppler frequency difference curve corresponding to the preset candidate azimuth angles is generated. The actual Doppler frequency difference is matched with each of the theoretical Doppler frequency difference curves to determine the azimuth angle of the radio frequency signal source relative to the moving carrier.

[0009] In some embodiments, matching the actual Doppler frequency difference with the theoretical Doppler frequency difference curve to determine the azimuth angle of the radio frequency signal source relative to the moving carrier includes: Each of the theoretical Doppler frequency difference curves is mapped to the target time-frequency power spectrum; The power value of the target time-frequency power spectrum is extracted along the trajectory of each of the theoretical Doppler frequency difference curves; By summing up the power values ​​corresponding to the same theoretical Doppler frequency difference curve, the cumulative energy value corresponding to each curve is obtained. The azimuth angle is the preset candidate azimuth angle corresponding to the theoretical Doppler frequency difference curve with the largest cumulative energy value.

[0010] In some embodiments, determining the position of the radio frequency signal source relative to the moving carrier based on the azimuth angle and the translational parameters includes: Using any possible position of the radio frequency signal source as a candidate position, the theoretical Doppler frequency difference corresponding to the candidate position is determined based on the candidate position and the translational parameters. The theoretical Doppler frequency difference is matched with the actual Doppler frequency difference to construct the objective function; The search space for the candidate positions is constrained by the azimuth angle; The search is performed within the search space with the objective function as the goal, and the optimal solution is determined as the position of the radio frequency signal source relative to the mobile carrier.

[0011] In some implementations, the search within the search space aimed at optimizing the objective function includes: Multiple initial solutions are selected within the search space; Each of the aforementioned initial solutions is used as the starting point for an iterative search; The solution with the best objective function value among the local optima obtained from the search is taken as the optimal solution.

[0012] This application also proposes a positioning device for a radio frequency signal source, used on a mobile carrier. The mobile carrier includes a first antenna, a second antenna, and a clock source. The first antenna and the second antenna achieve time-frequency synchronization by sharing the clock source. Both the first antenna and the second antenna are used to receive radio frequency signals transmitted by the radio frequency signal source. The device includes: The acquisition module is used to acquire in real time the motion parameters of the mobile carrier, the first radio frequency signal received by the first antenna, and the second radio frequency signal received by the second antenna, wherein the motion parameters include translational parameters and rotational parameters; The processing module is used to process the first radio frequency signal and the second radio frequency signal to extract the actual Doppler frequency difference between the first radio frequency signal and the second radio frequency signal; The determining module is used to determine the azimuth angle of the radio frequency signal source relative to the moving carrier based on the actual Doppler frequency difference and the rotation parameters; and to determine the position of the radio frequency signal source relative to the moving carrier based on the azimuth angle and the translational parameters of the moving carrier.

[0013] This application also proposes a mobile carrier, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the positioning method as described in any of the above embodiments.

[0014] This application also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the positioning method as described in any of the above embodiments.

[0015] The positioning method, positioning device, mobile carrier, and storage medium proposed in this application receive radio frequency signals through dual antennas sharing a common clock source and utilize conjugate multiplication to eliminate common frequency offsets, achieving accurate extraction of Doppler frequency differences. Then, by combining rotational motion direction finding with translational motion positioning, precise positioning of the radio frequency signal source is achieved without relying on large antenna arrays or multi-machine collaborative methods, reducing system architecture and deployment costs. Furthermore, this method completes all signal processing in the digital domain, eliminating the need for additional hardware. Thus, this application can improve the positioning accuracy and deployment flexibility of mobile carriers for radio frequency signal sources, reduce hardware complexity, and provide an efficient and reliable technical solution for radio frequency signal source detection and positioning tasks in various scenarios such as low-altitude UAV inspection, low-altitude logistics, and aerial communication base stations.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a method for locating a radio frequency signal source according to certain embodiments of this application; Figure 2 This is a schematic diagram of a positioning device for a radio frequency signal source according to certain embodiments of this application; Figure 3 This is a schematic diagram of a drone radio frequency signal source positioning scenario according to certain embodiments of this application; Figure 4 This is a flowchart of a method for locating a radio frequency signal source according to certain embodiments of this application; Figure 5 This is a comparison chart of measured and theoretical values ​​of Doppler frequency difference for certain embodiments of the UAV under horizontal translation conditions. Figure 6 This is a comparison chart of measured and theoretical values ​​of Doppler frequency difference for certain embodiments of the UAV under stationary rotation conditions. Figures 7-8 Flowchart of a method for locating a radio frequency signal source according to certain embodiments of this application; Figure 9 This is a schematic diagram of the coordinate system of the moving carrier and the azimuth angle of the signal source in some embodiments of this application; Figure 10 This is a flowchart of a method for locating a radio frequency signal source according to certain embodiments of this application; Figure 11 This is a schematic diagram of the angular power spectrum and peak detection of a drone rotating in place according to certain embodiments of this application; Figures 12-15 Flowchart of a method for locating a radio frequency signal source according to certain embodiments of this application; Figure 16 This is a schematic diagram of the translational flight path and signal source localization results of a UAV in some embodiments of this application.

[0018] Explanation of icon numbers Mobile carrier 1000, positioning device 100, acquisition module 10, processing module 20, determination module 30, first antenna 200, second antenna 300. Detailed Implementation

[0019] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0020] Please see Figure 1 This application proposes a method for locating a radio frequency (RF) signal source for a mobile carrier. The mobile carrier includes a first antenna, a second antenna, and a clock source. The first antenna and the second antenna are synchronized in time and frequency by sharing a clock source. Both the first antenna and the second antenna are used to receive RF signals transmitted by the RF signal source. The method includes: 01. Real-time acquisition of motion parameters of the mobile carrier, first radio frequency signal received by the first antenna, and second radio frequency signal received by the second antenna, wherein the motion parameters include translational parameters and rotational parameters; 02. Process the first radio frequency signal and the second radio frequency signal to extract the actual Doppler frequency difference between the first radio frequency signal and the second radio frequency signal; 03. Based on the actual Doppler frequency difference and rotation parameters, determine the azimuth angle of the radio frequency signal source relative to the moving carrier; 04. Based on the azimuth angle and the translational parameters of the moving vehicle, determine the position of the radio frequency signal source relative to the moving vehicle.

[0021] Please see Figure 2 This application also proposes a positioning device 100 for a radio frequency signal source, used on a mobile carrier 1000. The mobile carrier 1000 includes a first antenna 200, a second antenna 300, and a clock source. The first antenna 200 and the second antenna 300 achieve time-frequency synchronization by sharing a clock source. Both the first antenna 200 and the second antenna 300 are used to receive radio frequency signals transmitted by the radio frequency signal source. The positioning device 100 includes an acquisition module 10, a processing module 20, and a determination module 30.

[0022] Step 01 can be implemented by the acquisition module 10, step 02 can be implemented by the processing module 20, and steps 03 and 04 can be implemented by the determination module 30.

[0023] Alternatively, the acquisition module 10 is used to acquire in real time the motion parameters of the mobile carrier 1000, the first radio frequency signal received by the first antenna 200, and the second radio frequency signal received by the second antenna 300, wherein the motion parameters include translational parameters and rotational parameters; the processing module 20 is used to process the first radio frequency signal and the second radio frequency signal to extract the actual Doppler frequency difference between the first radio frequency signal and the second radio frequency signal; the determination module 30 is used to determine the azimuth angle of the radio frequency signal source relative to the mobile carrier 1000 based on the actual Doppler frequency difference and the rotational parameters; and to determine the position of the radio frequency signal source relative to the mobile carrier 1000 based on the azimuth angle and the translational parameters of the mobile carrier 1000.

[0024] This application also proposes a mobile carrier 1000, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the aforementioned method for locating the radio frequency signal source. That is, the processor can acquire in real time the motion parameters of the mobile carrier 1000, the first radio frequency signal received by the first antenna 200, and the second radio frequency signal received by the second antenna 300. The motion parameters include translational parameters and rotational parameters. The processor processes the first and second radio frequency signals to extract the actual Doppler frequency difference between them. Based on the actual Doppler frequency difference and the rotational parameters, the processor determines the azimuth angle of the radio frequency signal source relative to the mobile carrier 1000. Based on the azimuth angle and the translational parameters of the mobile carrier 1000, the processor determines the position of the radio frequency signal source relative to the mobile carrier 1000.

[0025] In the radio frequency signal source positioning method, positioning device 100, and mobile carrier 1000 of this application embodiment, radio frequency signals are received by two antennas on the mobile carrier 1000 that share a common clock source. The dual signals are multiplied by conjugate to eliminate common frequency offset and extract Doppler frequency difference. Direction finding is performed by the rotational motion of the mobile carrier 1000 and positioning is performed by translational motion. This achieves high-precision passive positioning of ground radio frequency signal sources by a lightweight hardware architecture that relies only on a single mobile carrier 1000 and dual antennas. From the principle level, it avoids the dependence on large antenna arrays and the time-frequency synchronization hardware cost required for multi-device collaboration. It solves the technical problem that traditional positioning schemes cannot meet the requirements of low cost, lightweight and high-precision positioning. It effectively reduces system load and deployment cost and improves the flexibility and accuracy of radio frequency signal source positioning in complex outdoor environments.

[0026] In some examples, the positioning device 100 may be part of the mobile carrier 1000. Alternatively, the mobile carrier 1000 may include the positioning device 100. As hardware, the positioning device 100 may be a standalone component or added to the mobile carrier 1000 as an additional peripheral element. The positioning device 100 may also be integrated into the mobile carrier 1000; for example, it may be integrated into a processor. As software, the code segment corresponding to the positioning device 100 may be stored in memory and executed by the processor to implement the aforementioned functions. Alternatively, the positioning device 100 may include a computer program, or the aforementioned computer program may include the positioning device 100.

[0027] The mobile carrier 1000 can be a mobile phone, tablet computer, computer (personal computer, laptop computer), UAV ground station, handheld spectrum monitor, or other similar device. This application embodiment can be described using a UAV ground station as an example of the mobile carrier 1000; that is, the radio frequency signal source positioning method and positioning device 100 are applied to, but not limited to, UAV ground stations. The positioning device 100 can be hardware or software pre-installed on the UAV ground station, and can execute the aforementioned positioning method when started and run on the UAV ground station. For example, the positioning device 100 can be a low-level software code segment of the UAV ground station or part of the operating system, or it can be an application running on the UAV ground station.

[0028] It is worth noting that the mobile carrier 1000 refers to a mobile platform capable of mounting at least two antennas, including but not limited to multi-rotor drones, fixed-wing drones, unmanned surface vessels, and other platforms with autonomous or remote-controlled movement capabilities. In some embodiments of this application, the mobile carrier 1000 is selected as a multi-rotor drone. The multi-rotor drone possesses flexible maneuverability such as vertical take-off and landing, hovering, low-speed translation, and rotation around an axis, enabling it to be quickly deployed in complex environments and execute diverse movement modes. Two antennas are mounted on the drone to receive radio frequency signals emitted by a radio frequency signal source.

[0029] The first antenna 200 and the second antenna 300 are two spatially separated receiving antennas, fixedly mounted on the mobile carrier 1000 and maintained at a preset distance, used to receive radio frequency signals transmitted by a radio frequency signal source. A clock source provides the same reference clock for both antennas, ensuring time-frequency synchronization between the two receiving channels, meaning the common frequency offset components in the two received signals are identical. Through this shared clock source design, the frequency and time deviations between the two radio frequency receiving links are naturally eliminated at the hardware level, eliminating the need for additional synchronization and calibration hardware.

[0030] Radio frequency (RF) signal sources refer to various devices capable of actively emitting RF signals, including but not limited to cellular communication base stations, Wi-Fi routers, communication terminals, and illegal RF interference sources. In low-altitude application scenarios, RF signal sources are typically ground-based fixed or slowly moving transmitting devices with unknown locations, requiring passive detection and positioning via a mobile carrier 1000. The RF signal emitted by the RF signal source propagates in space and is captured by the receiving antenna on the mobile carrier 1000. The Doppler frequency of this RF signal is affected by the relative motion between the RF signal source and the mobile carrier 1000.

[0031] The Doppler frequency difference refers to the difference in Doppler frequencies between the signals transmitted by the same radio frequency source received by the first antenna 200 and the second antenna 300. This difference is determined by the difference in motion states of the two antennas due to their different positions in space. Specifically, when the moving carrier 1000 moves, the two antennas have different spatial positions and instantaneous velocity vectors, resulting in a difference in the Doppler frequencies of the two signals received by the first antenna 200 and the second antenna 300. The actual Doppler frequency difference is the true frequency difference measurement value extracted from the two actually received radio frequency signals.

[0032] Rotation parameters refer to quantitative parameters describing the rotational motion of the mobile carrier 1000. In some embodiments of this application, rotation parameters include, but are not limited to, rotational angular velocity, the distance between the first antenna and the second antenna, and the wavelengths of the first and second radio frequency signals. Specifically, the rotational angular velocity is the angular velocity of the mobile carrier 1000 rotating about its vertical axis, which can be acquired by an inertial measurement unit mounted on the mobile carrier 1000; the distance between the first and second antennas is the fixed installation distance between the two antennas on the mobile carrier 1000, determined by the hardware structure of the mobile carrier 1000; and the wavelength of the radio frequency signal is the carrier wavelength of the signal transmitted by the radio frequency signal source, determined by the carrier frequency of the radio frequency signal. In some embodiments of this application, the rotation parameters, together with the Doppler frequency difference, are used to determine the azimuth angle of the radio frequency signal source relative to the mobile carrier 1000.

[0033] Translational parameters refer to quantitative parameters describing the translational motion of the mobile carrier 1000, including but not limited to three-dimensional position coordinates, translational velocity vector, and acceleration. These translational parameters can be measured in real time by a differential positioning module mounted on the mobile carrier 1000 and are used to characterize the translational state of the mobile carrier 1000 in space. In one embodiment of this application, the translational parameters, together with the azimuth angle, are used to determine the position of the radio frequency signal source relative to the mobile carrier 1000.

[0034] Azimuth angle refers to the directional angle of a radio frequency signal source relative to a moving carrier 1000 on a horizontal plane. It is usually taken with true north as the reference zero degree, and its value ranges from 0° to 360°. In three-dimensional space, azimuth angle describes the horizontal direction of the radio frequency signal source and is an intermediate quantity for direction finding during continuous rotational motion and positioning during translational motion.

[0035] Location refers to the coordinates of a radio frequency signal source in three-dimensional space, including longitude, latitude, altitude, or three-dimensional coordinates in a geostationary coordinate system. Determining the location of the radio frequency signal source is the ultimate output target of a localization method.

[0036] Please see Figure 3 In one embodiment, the mobile carrier 1000 is a multi-rotor drone flying in low-altitude airspace, with the radio frequency signal source located on the ground. The drone is equipped with two spatially separated receiving antennas, namely a first antenna 200 and a second antenna 300, which share a common clock source for time-frequency synchronization. After takeoff, the acquisition module 10 acquires the drone's motion parameters in real time, including rotation and translation parameters. Simultaneously, it acquires the first radio frequency signal received by the first antenna 200 and the second radio frequency signal received by the second antenna 300. The processing module 20 processes the two radio frequency signals, extracting the actual Doppler frequency difference between them. Then, the determination module 30 first determines the azimuth angle of the radio frequency signal source relative to the drone based on the actual Doppler frequency difference and rotation parameters, and then determines the specific coordinate position of the radio frequency signal source relative to the drone based on the determined azimuth angle and the drone's translation parameters.

[0037] Thus, with the radio frequency signal source positioning method proposed in this application, the operator only needs to control the mobile carrier 1000 to take off, hover, and perform rotation and translation flight to achieve the positioning of the radio frequency signal source. There is no need to rely on large antenna arrays or multi-aircraft coordination. This solves the problems of high system deployment cost, large hardware load, and difficulty in flexible deployment in traditional positioning schemes, and improves the efficiency and accuracy of radio frequency signal source positioning in complex outdoor environments.

[0038] Please see Figure 4 In some implementations, step 02 includes: 021, Perform conjugate multiplication on the first radio frequency signal and the second radio frequency signal to obtain the conjugate multiplied signal; 022, perform time-frequency processing on the conjugate multiplication signal to obtain the actual Doppler frequency difference.

[0039] Please combine further Figure 2In some embodiments, steps 021-022 can be implemented by processing module 20. Alternatively, processing module 20 can perform conjugate multiplication on the first radio frequency signal and the second radio frequency signal to obtain a conjugate multiplied signal; and perform time-frequency processing on the conjugate multiplied signal to obtain the actual Doppler frequency difference.

[0040] In some implementations, the processor can perform a conjugate multiplication operation on the first radio frequency signal and the second radio frequency signal to obtain a conjugate multiplied signal; and perform time-frequency processing on the conjugate multiplied signal to obtain the actual Doppler frequency difference.

[0041] It's worth noting that conjugate multiplication refers to multiplying the conjugates of the first and second radio frequency (RF) signals. For signals, conjugate multiplication extracts the frequency difference information between the two signals. Since the first antenna 200 and the second antenna 300 share the same clock source, the first and second RF signals contain identical common frequency offset components. After conjugate multiplication, these common frequency offset components cancel each other out. Because the common frequency offset is eliminated, the instantaneous frequency of the multiplied signal is ultimately only related to the Doppler frequency difference between the two signals.

[0042] Time-frequency processing refers to the time-frequency analysis of conjugate multiplied signals to extract the characteristics of signal frequency changes over time. The output of time-frequency processing can reflect the value of signal frequency at different times. This output can determine the Doppler frequency difference. That is, by performing time-frequency processing on the conjugate multiplied signals in this step, the actual Doppler frequency difference between the first radio frequency signal and the second radio frequency signal received by the mobile carrier 1000 during actual operation can be obtained.

[0043] In one embodiment, the mobile carrier 1000 is a multi-rotor drone equipped with two spatially separated receiving antennas that share the same clock source.

[0044] During the drone's flight, the first antenna and the second antenna respectively receive radio frequency signals emitted by the radio frequency signal source, denoted as the first radio frequency signal. Second radio frequency signal Both signals contain a common frequency offset component between the transmitter and receiver. Since the two receiving channels share the same clock source, this common frequency offset component is identical in both signals.

[0045] Processing module 20 acquires two radio frequency signals and Then, perform the conjugate multiplication calculation, that is, calculate:

[0046] in, Indicates the second radio frequency signal . conjugate.

[0047] After conjugate multiplication, the common frequency offset components in the two signals are canceled out, and the conjugate multiplied signals... The instantaneous frequency contains only the Doppler frequency difference between the two signals, that is:

[0048] Next, the processing module 20 performs conjugate multiplication on the signals. Perform time-frequency processing to extract the conjugate multiplication signal. The change of the instantaneous frequency over time is the actual Doppler frequency difference.

[0049] Please see Figure 5 and Figure 6 , Figure 5 and Figure 6 The figures show a comparison between the measured curves and the theoretical true values ​​of the dual-channel Doppler frequency difference over time for UAVs in horizontal translational motion and stationary rotational motion. Figure 5 and Figure 6 The dashed line in the figure represents the true value of the Doppler frequency difference calculated using the actual flight path of the UAV, the real coordinates of the signal source, and the theoretical model. The comparison results in the figure show that the measured Doppler frequency difference matches the theoretical true value well under both translation and rotation motion modes, demonstrating that the Doppler frequency difference estimation algorithm proposed in this application has excellent extraction accuracy.

[0050] Thus, by employing conjugate multiplication, the common offset components in the two signals are completely canceled out, eliminating the impact of carrier frequency drift at the transmitting end and local oscillator frequency deviation at the receiving end on Doppler frequency difference estimation. Furthermore, this signal processing method requires no additional hardware and can be performed by the mobile carrier 1000 itself, effectively reducing hardware costs and system complexity.

[0051] Please see Figure 7 In some implementations, step 022 includes: 0221, Perform a short-time Fourier transform on the conjugate multiplied signals to obtain the time-frequency power spectrum; 0222, the target time-frequency power spectrum is obtained by convolving and smoothing the time-frequency power spectrum along the frequency dimension using a Gaussian kernel function; 0223, extract the actual Doppler frequency difference based on the target time-frequency power spectrum.

[0052] Please combine further Figure 2In some implementations, steps 0221-0223 can be implemented by processing module 20. Alternatively, processing module 20 can perform a short-time Fourier transform on the conjugate multiplied signal to obtain the time-frequency power spectrum; perform convolution smoothing on the time-frequency power spectrum along the frequency dimension using a Gaussian kernel function to obtain the target time-frequency power spectrum; and extract the actual Doppler frequency difference based on the target time-frequency power spectrum.

[0053] In some implementations, the processor can perform a short-time Fourier transform on the conjugate multiplied signal to obtain the time-frequency power spectrum; perform convolution smoothing on the time-frequency power spectrum along the frequency dimension using a Gaussian kernel function to obtain the target time-frequency power spectrum; and extract the actual Doppler frequency difference based on the target time-frequency power spectrum.

[0054] It's worth noting that the Short-Time Fourier Transform (SFT) is a signal processing method that applies a time window to a signal and performs a Fourier transform. By sliding the time window across the signal, information about how the signal frequency changes over time can be obtained. The time-frequency power spectrum is the output of the SFT, with time on the horizontal axis and frequency on the vertical axis. The value at each point represents the signal energy at that frequency at that moment.

[0055] The Gaussian kernel function is a kernel function that exhibits a Gaussian distribution. Its mathematical form is:

[0056] in, This is an adjustable parameter used to control the effective support width of the kernel function. The larger the value, the wider the effective support width of the kernel function, the stronger the smoothing effect on adjacent frequency components, and the better it is for suppressing random spectral peaks caused by noise. The smaller the value, the more concentrated the kernel function is near the center frequency, and the higher the frequency resolution.

[0057] In discrete convolution operations, normalized standard deviation can be used. , To achieve the frequency resolution of the short-time Fourier transform, adjustable parameters in the continuous domain are implemented. Mapped to the discrete frequency index domain. Furthermore, since actual convolution operations cannot perform convolution over an infinite range across the entire frequency axis, the kernel function needs to be truncated. The truncated half-width M can be... ,based on The principle setting can effectively ensure that the energy coverage of the kernel function is approximately full coverage.

[0058] Convolution smoothing refers to the filtering process of convolving a Gaussian kernel function with the time-frequency power spectrum along the frequency dimension. Through convolution smoothing, the high-energy trajectories of the real signal are enhanced, while isolated spectral peaks caused by noise and interference are suppressed. The target time-frequency power spectrum refers to the time-frequency power spectrum output after Gaussian kernel smoothing. Compared to the original time-frequency power spectrum, the high-energy trajectory of the real signal in the target time-frequency power spectrum will be enhanced, while isolated peaks caused by noise and interference will be suppressed.

[0059] In one embodiment, the mobile carrier 1000 is a multi-rotor drone, and during the rotation of the drone, the processing module 20 acquires the conjugate multiplication signal. Then, regarding the signal Perform a short-time Fourier transform: First, the conjugate multiplication signal... Frame-by-frame windowing is performed. Let the length of each analysis window be L, and the sliding step size between adjacent analysis windows be H. Then the short-time Fourier transform within the q-th time window can be expressed as:

[0060] in, For window functions, For the frequency index, the corresponding power spectrum is:

[0061] Thus, the time-frequency power spectrum was obtained.

[0062] Next, processing module 20 performs convolution smoothing on the time-frequency power spectrum along the frequency dimension using a Gaussian kernel function. The discrete form of the Gaussian kernel function is as follows:

[0063] in, , is the normalized standard deviation. Given the frequency resolution of the short-time Fourier transform, the target time-frequency power spectrum... Represented as:

[0064] in, , where is the truncated half-width of the kernel function.

[0065] Processing module 20 in the target time-frequency power spectrum Based on this, the frequency position corresponding to the maximum power value is searched along the frequency direction within each time window, and the peak frequency is used as the estimated value of the actual Doppler frequency difference at that moment. The peak frequencies at all moments are arranged in chronological order, which is the actual Doppler frequency difference observation sequence.

[0066] Thus, by using a Gaussian kernel function to convolve and smooth the time-frequency power spectrum along the frequency dimension, the frequency components with high signal-to-noise ratio are enhanced, while noise and isolated spectral peaks are suppressed, improving the accuracy and stability of Doppler frequency difference extraction under low signal-to-noise ratio conditions; simultaneously The parameters are adjustable and can be adjusted according to actual needs based on environmental complexity, balancing frequency resolution in open scenes and noise resistance in complex scenes.

[0067] Please see Figure 8 In some implementations, step 03 includes: 031. Based on the rotation parameters and multiple preset candidate azimuth angles, generate a theoretical Doppler frequency difference curve corresponding to the preset candidate azimuth angles; 032. Match the actual Doppler frequency difference with each theoretical Doppler frequency difference curve to determine the azimuth angle of the radio frequency signal source relative to the moving carrier 1000.

[0068] Please combine further Figure 2 In some embodiments, steps 031 and 032 can be implemented by the determining module 30. Alternatively, the determining module 30 can generate a theoretical Doppler frequency difference curve corresponding to a preset candidate azimuth angle based on rotation parameters and multiple preset candidate azimuth angles; and match the actual Doppler frequency difference with each theoretical Doppler frequency difference curve to determine the azimuth angle of the radio frequency signal source relative to the moving carrier 1000.

[0069] In some implementations, the processor can generate a theoretical Doppler frequency difference curve corresponding to a preset candidate azimuth angle based on rotation parameters and multiple preset candidate azimuth angles; and match the actual Doppler frequency difference with each theoretical Doppler frequency difference curve to determine the azimuth angle of the radio frequency signal source relative to the moving carrier 1000.

[0070] It is worth noting that the preset candidate azimuth angles refer to multiple hypothetical direction angles selected within the range of 0° to 360° according to a preset step size. Each preset candidate azimuth angle represents a hypothesis that an RF signal source might be in that direction. The preset step size is set according to the accuracy requirements; the smaller the step size, the more preset candidate azimuth angles there are, i.e., the higher the angular resolution.

[0071] The theoretical Doppler frequency difference curve refers to the curve showing the change of the Doppler frequency difference over time, calculated based on rotation parameters and a theoretical Doppler frequency difference model, for a given preset candidate azimuth angle. This curve reflects the trend of the Doppler frequency difference as the moving vehicle rotates 100° if the radio frequency signal source is in the candidate direction. Different preset candidate azimuth angles correspond to different phases of the theoretical Doppler frequency difference curves; this difference can be used to match measured data to determine the true azimuth angle.

[0072] Matching refers to the process of comparing the actual Doppler frequency difference variation sequence with the theoretical Doppler frequency difference curves corresponding to each preset candidate azimuth angle. The actual Doppler frequency difference variation sequence is connected in chronological order to form a measured curve. By comparing the measured curve with each theoretical curve one by one, the candidate azimuth angle corresponding to the theoretical curve with the highest matching degree is the true azimuth angle of the radio frequency signal source.

[0073] Please see Figure 9 In one embodiment, the mobile carrier 1000 is a multi-rotor UAV. A world coordinate system E and a body coordinate system B are established. During the hovering and rotating process of the UAV, the origin of the world coordinate system E is fixed at the global reference point on the ground, and the body coordinate system B is fixed at the geometric center of the UAV. In this embodiment, it is assumed that the interference of translation, pitch, and roll motions can be ignored when the UAV is in the hovering and rotating working state. When the UAV hovers at a certain spatial point and rotates uniformly around the vertical axis, the yaw rate in the UAV rotation parameters is denoted as... (rad / s), the initial yaw angle is denoted as All of these are known quantities obtained from actual measurements: antenna spacing d and signal wavelength. The quantity is known.

[0074] Then follow the equally spaced angular steps. right Perform a one-dimensional traversal search for each candidate azimuth angle. Based on the Doppler frequency difference theoretical model, the corresponding theoretical Doppler frequency difference curve is generated. The formula for calculating the theoretical curve is as follows:

[0075] After performing the above operations on all preset candidate azimuth angles, the theoretical curve corresponding to each preset candidate azimuth angle is obtained. Then, the actual Doppler frequency difference observation values ​​are connected in time order to form the measured Doppler frequency difference change curve. The measured curve is matched with the theoretical curve corresponding to each candidate azimuth angle one by one. The candidate azimuth angle corresponding to the theoretical curve with the highest matching degree is the azimuth angle of the radio frequency signal source relative to the mobile carrier 1000.

[0076] Thus, by matching the actual measured Doppler frequency difference curve with the frequency difference curves of each preset candidate azimuth angle, the azimuth angle can be determined without relying on a large antenna array, reducing hardware complexity and system cost. At the same time, the matching process utilizes all observation data throughout the entire rotation cycle, avoiding interference from local errors on the final result and improving the stability and accuracy of the mobile carrier 1000 in determining the azimuth angle.

[0077] Please see Figure 10 In some embodiments, step 032 includes: 0321, map each theoretical Doppler frequency difference curve to the target time-frequency power spectrum; 0322, extract the power value of the target time-frequency power spectrum along the trajectory of each theoretical Doppler frequency difference curve; 0323, sum up the power values ​​corresponding to the same theoretical Doppler frequency difference curve to obtain the cumulative energy value corresponding to each curve; 0324, the preset candidate azimuth angle corresponding to the theoretical Doppler frequency difference curve with the largest cumulative energy value is taken as the azimuth angle.

[0078] Please combine further Figure 2 In some implementations, steps 0321-0324 can be implemented by the determining module 30. Alternatively, the determining module 30 can map each theoretical Doppler frequency difference curve to the target time-frequency power spectrum; extract the power value of the target time-frequency power spectrum along the trajectory of each theoretical Doppler frequency difference curve; accumulate the power values ​​corresponding to the same theoretical Doppler frequency difference curve to obtain the cumulative energy value corresponding to each curve; and use the preset candidate azimuth angle corresponding to the theoretical Doppler frequency difference curve with the largest cumulative energy value as the azimuth angle.

[0079] In some implementations, the processor can map each theoretical Doppler frequency difference curve to the target time-frequency power spectrum; extract the power value of the target time-frequency power spectrum along the trajectory of each theoretical Doppler frequency difference curve; accumulate the power values ​​corresponding to the same theoretical Doppler frequency difference curve to obtain the cumulative energy value corresponding to each curve; and use the preset candidate azimuth angle corresponding to the theoretical Doppler frequency difference curve with the largest cumulative energy value as the azimuth angle.

[0080] It's worth noting that mapping refers to placing the theoretical Doppler frequency difference curve onto the coordinate system of the target time-frequency power spectrum, so that the theoretical curve forms a trajectory on the time-frequency plane. The horizontal axis of the target time-frequency power spectrum represents time, and the vertical axis represents frequency. The power value at each coordinate point represents the signal energy at that time and frequency. After mapping, the coordinate positions along the theoretical Doppler frequency difference path on the time-frequency plane can be determined.

[0081] The trajectory refers to the path that the theoretical Doppler frequency difference curve follows on the target's time-frequency power spectrum. Since the theoretical curve corresponds to a frequency value at each moment, and this frequency value changes continuously with time, it forms a continuous path on the time-frequency plane. The power value refers to the numerical value at a specific coordinate position in the target's time-frequency power spectrum, representing the signal energy at that moment and frequency.

[0082] The cumulative energy value refers to the sum of the power values ​​corresponding to all coordinate positions along the trajectory of the same theoretical Doppler frequency difference curve. When the candidate azimuth angle is close to the true azimuth angle of the signal source, the theoretical curve and the high-energy trajectory of the true signal on the time-frequency power spectrum basically coincide, and the power value extracted and accumulated along this trajectory is larger, resulting in a higher cumulative energy value. When the candidate azimuth angle deviates from the true azimuth angle, the theoretical curve deviates from the high-energy trajectory of the true signal, passing through low-energy regions, resulting in a smaller extracted power value and a lower cumulative energy value. Therefore, the cumulative energy value reflects the degree of matching between each candidate azimuth angle and the true azimuth angle.

[0083] The angular power spectrum is a spectrum formed with candidate azimuth angles as independent variables and the cumulative energy value corresponding to each candidate azimuth angle as dependent variable. The peak position of this spectrum corresponds to the candidate azimuth angle with the largest cumulative energy value. Peak search refers to the process of searching for the location of the maximum value in the angular power spectrum, and the angle corresponding to the maximum value is the azimuth angle of the signal source.

[0084] In one embodiment, the mobile carrier 1000 is a multi-rotor unmanned aerial vehicle (UAV). The UAV hovers at a certain spatial point and rotates uniformly around its vertical axis. First, the theoretical Doppler frequency difference curves corresponding to each candidate azimuth angle are mapped onto the target time-frequency power spectrum. Taking the curve corresponding to a candidate azimuth angle of 30° as an example, this curve describes the theoretical Doppler frequency difference value at each time point from the start time to the end time. After mapping, a trajectory is formed on the time-frequency plane. The power value at the corresponding coordinate position in the target time-frequency power spectrum is extracted point by point along this trajectory, and all the extracted power values ​​on the curve are accumulated to obtain the cumulative energy value corresponding to the candidate azimuth angle of 30°.

[0085] The above operations are performed on the theoretical curves corresponding to each candidate azimuth angle to obtain the cumulative energy value corresponding to each candidate azimuth angle, thus forming an angular power spectrum. Then, the angular power spectrum is searched, and the preset candidate azimuth angle corresponding to the theoretical curve with the largest cumulative energy value is taken as the azimuth angle of the radio frequency signal source relative to the moving carrier 1000.

[0086] Please see Figure 11 , Figure 11 This embodiment uses the angular power spectrum obtained from the actual test of the UAV rotating in place to determine the azimuth angle. Figure 11 As can be seen from the data, the peak value appears at -104°, while the actual signal source location was measured to be -101° before the experiment, with an angle error of 3°.

[0087] Thus, by mapping the theoretical curve to the target time-frequency power spectrum and accumulating the power value along the trajectory, the matching process is transformed into an energy accumulation problem. Utilizing all observation data within the rotation period, the local error propagation problem that may occur in traditional techniques can be effectively avoided. Meanwhile, the azimuth angle obtained from the actual test basically matches the true azimuth angle position, further verifying the high accuracy and effectiveness of this application.

[0088] Please see Figure 12 In some implementations, step 04 includes: 041. Using any possible position of the radio frequency signal source as a candidate position, determine the theoretical Doppler frequency difference corresponding to the candidate position based on the candidate position and translation parameters. 042. Match the theoretical Doppler frequency difference with the actual Doppler frequency difference to construct the objective function; 043, The search space for candidate positions is constrained by the azimuth angle; 044. The search is performed in the search space with the objective function as the target, and the optimal solution is determined as the position of the radio frequency signal source relative to the mobile carrier 1000.

[0089] Please combine further Figure 2 In some implementations, steps 041 to 044 can be implemented by the determining module 30. Alternatively, the determining module 30 can take any possible position of the radio frequency signal source as a candidate position, determine the theoretical Doppler frequency difference corresponding to the candidate position based on the candidate position and translation parameters; match the theoretical Doppler frequency difference with the actual Doppler frequency difference to construct an objective function; constrain the search space of the candidate positions based on the azimuth angle; and search within the search space with the objective function as the target, determining the optimal solution as the position of the radio frequency signal source relative to the moving carrier 1000.

[0090] In some implementations, the processor can take any possible position of the radio frequency signal source as a candidate position, determine the theoretical Doppler frequency difference corresponding to the candidate position based on the candidate position and translation parameters; match the theoretical Doppler frequency difference with the actual Doppler frequency difference to construct an objective function; constrain the search space of the candidate position according to the azimuth angle; search within the search space with the objective function as the target, and determine the optimal solution as the position of the radio frequency signal source relative to the moving carrier 1000.

[0091] It is worth noting that candidate locations refer to the possible positions of the radio frequency signal source in space, representing the position quantity to be solved. Candidate locations are typically represented by three-dimensional coordinates. During the localization calculation, candidate locations can be traversed or iteratively searched to find the optimal solution. The theoretical Doppler frequency difference refers to the theoretical frequency difference value calculated using a Doppler frequency difference theoretical model for a given candidate location, based on the coordinates of that candidate location, the translational parameters of the moving vehicle 1000, and the instantaneous position of the antenna. This value reflects the specific theoretical Doppler frequency difference of the radio frequency signal source at that candidate location at the current moment.

[0092] The objective function is a function used to evaluate the degree of matching between candidate locations and the actual signal source. It quantifies the degree of matching between the theoretical Doppler frequency difference and the actual Doppler frequency difference corresponding to each candidate location. The larger the objective function value, the closer the candidate location is to the actual signal source location. The search space refers to the spatial range in which candidate locations may exist. The search space is limited by azimuth constraints, that is, the directional range of candidate locations relative to the moving carrier is limited by 100°. Candidate locations with excessive deviation from the azimuth are eliminated to narrow the search range and improve search efficiency.

[0093] In one embodiment, the mobile carrier 1000 is a multi-rotor drone. After determining the azimuth angle of the radio frequency signal source, the drone translates along a certain heading. First, a candidate position is selected. Possible locations for radio frequency signal sources, based on candidate locations Translational velocity vector in translational parameters and the instantaneous position of the antenna , ( () indicates the first or second receiving antenna, used to calculate the theoretical Doppler frequency difference at each moment. For the first and second antennas, each antenna points to a candidate position. The unit direction vector is:

[0094] Therefore, the theoretical Doppler frequency difference in the translation scenario is:

[0095] in, For radio frequency signal wavelength, The translational velocity vector is used to obtain the theoretical sequence. This theoretical sequence is then matched with the actual Doppler frequency difference observation sequence to construct the objective function.

[0096] Meanwhile, the constraints imposed on the objective function include a search space limited by the azimuth angle determined in the aforementioned embodiments, retaining only candidate positions consistent with the azimuth direction and eliminating candidate positions with excessive deviation from the azimuth angle. Within the constrained search space, the search is performed with the objective function as the target, and the candidate position corresponding to the maximum value of the objective function is determined as the position of the radio frequency signal source relative to the mobile carrier 1000.

[0097] In this way, by constraining the search space by azimuth angle, the search range is narrowed, the uncertainty of the search direction is reduced, the computational complexity is lowered, and the accuracy and reliability of positioning are improved. Position calculation can be completed with only a single continuous translational trajectory, without the need for multiple hovering intersections, which also improves positioning efficiency.

[0098] Please see Figure 13 In some implementations, step 042 includes: 0421, energy accumulation is performed along the trajectory of the theoretical Doppler frequency difference corresponding to each candidate position in the target time-frequency power spectrum, and the accumulated energy value is used as the objective function.

[0099] Please combine further Figure 2 In some implementations, step 0421 can be implemented by the determining module 30. Alternatively, the determining module 30 can accumulate energy along the trajectory of the theoretical Doppler frequency difference corresponding to each candidate position in the target time-frequency power spectrum, using the accumulated energy value as the objective function.

[0100] In some implementations, the processor can perform energy accumulation along the trajectory of the theoretical Doppler frequency difference corresponding to each candidate position in the target time-frequency power spectrum, using the accumulated energy value as the objective function.

[0101] It is worth noting that the trajectory of the theoretical Doppler frequency difference in the target time-frequency power spectrum refers to the path formed after mapping the theoretical Doppler frequency difference corresponding to the candidate position to the target time-frequency power spectrum in chronological order. The horizontal axis of the target time-frequency power spectrum represents time, and the vertical axis represents frequency. The theoretical Doppler frequency difference corresponds to a frequency value at each moment, and after mapping, a trajectory is formed on the time-frequency plane. Energy accumulation refers to extracting the power values ​​at the corresponding coordinate positions in the target time-frequency power spectrum point by point along this trajectory and summing them. The accumulated value is used to evaluate the degree of matching between the candidate position and the actual signal source position.

[0102] In one embodiment, after obtaining the theoretical Doppler frequency difference of each candidate position at each time step, the theoretical Doppler frequency difference of each candidate position is mapped to the target time-frequency power spectrum in chronological order to form a trajectory. The power values ​​at the corresponding coordinate positions in the target time-frequency power spectrum are extracted point by point along this trajectory and accumulated to obtain the cumulative energy value corresponding to the candidate position. This cumulative energy value is used as the objective function for the candidate position.

[0103] Let the total duration of translation observation be A total of Q sets of discrete observation data were collected. The first element in the target time-frequency power spectrum Time, Frequency Index The power value at that location, Candidate positions For the first The frequency index corresponding to the theoretical Doppler frequency difference at time t is given by the formula:

[0104] Then the candidate position The corresponding cumulative energy value is:

[0105] Thus, by transforming the localization problem into an energy accumulation problem along a theoretical trajectory, and using the accumulated energy value as the objective function, when the candidate position is close to the actual signal source, the theoretical trajectory and the high-energy trajectory of the actual signal basically coincide, resulting in a larger accumulated energy value; when the candidate position deviates from the actual position, the accumulated energy value is smaller. This objective function utilizes all observation data along the entire translational trajectory, exhibits good noise resistance, and provides a clear optimization direction for subsequent solutions.

[0106] Please see Figure 14 In some implementations, step 043 includes: 0431, Calculate the azimuth angle and azimuth angle deviation of the candidate position relative to the moving vehicle 1000; 0432, the area with a deviation value less than the preset angle threshold is defined as the search space.

[0107] Please combine further Figure 2 In some embodiments, steps 0431 to 0432 can be implemented by the determining module 30. Alternatively, the determining module 30 can calculate the azimuth angle and the deviation value of the candidate position relative to the moving carrier 1000; and determine the area with the deviation value less than a preset angle threshold as the search space.

[0108] In some implementations, the processor can calculate the azimuth angle and the deviation value of the candidate position relative to the moving carrier 1000; and determine the area where the deviation value is less than a preset angle threshold as the search space.

[0109] It is worth noting that the deviation value refers to the difference between the azimuth angle of the candidate position relative to the moving carrier 1000 and the azimuth angle determined in the aforementioned embodiment. The preset angle threshold refers to a pre-set angle value used to determine whether the candidate position is within the effective search space. The specific value of the preset angle threshold is not limited in this embodiment and can be set according to the accuracy requirements of the actual scenario. Candidate positions with a deviation value less than or equal to the preset angle threshold are retained, while candidate positions with a deviation value greater than the preset threshold are discarded.

[0110] In one embodiment, the azimuth angle of the radio frequency signal source relative to the moving carrier 1000 It is known. During the translation and positioning phase, for a given candidate position... Based on the candidate location coordinates and the real-time location coordinates of the mobile vehicle 1000, the azimuth angle of the candidate location relative to the mobile vehicle 1000 is calculated. and calculate and The deviation between them, i.e. .

[0111] Next, a preset angle threshold is established. Determine whether the deviation value corresponding to each candidate position is less than a preset threshold. The deviation value is less than or equal to The region is defined as the search space. ,Right now:

[0112] Deviation value greater than The candidate positions are excluded and do not participate in the subsequent objective function calculation and optimal search process.

[0113] In this way, by comparing the deviation between the azimuth angle of the candidate position and the direction finding result with the preset angle threshold, the search space is limited to the angle range where the signal source may exist, eliminating candidate positions that are obviously inconsistent with the direction of the signal source, narrowing the search range, and reducing the computational load of subsequent objective function calculation and optimal search.

[0114] Please see Figure 15 In some implementations, step 044 includes: 0441, Select multiple initial solutions within the search space; 0442, each starting from a different initial solution, performs an iterative search; 0443, the solution with the best objective function value among the local optimal solutions obtained by the search is taken as the optimal solution.

[0115] Please combine further Figure 2 In some implementations, steps 0441 to 0443 can be implemented by the determining module 30. Alternatively, the determining module 30 can select multiple initial solutions within the search space; perform iterative searches starting from each initial solution; and select the solution with the best objective function value among the locally optimal solutions obtained from the search as the optimal solution.

[0116] In some implementations, the processor can select multiple initial solutions within the search space; perform iterative searches starting from each initial solution; and select the solution with the best objective function value among the locally optimal solutions obtained from the search as the optimal solution.

[0117] It is worth noting that the initial solution refers to the initial estimated point of the iterative search, i.e., one of the multiple random candidate positions selected within the search space. A local optimum refers to the optimal solution obtained by iteratively searching within a local search range, starting from a given initial solution. Since the objective function is non-convex, it may have multiple local optima. The optimal solution is the one with the best objective function value selected from the multiple local optima, and is determined as the estimated location value of the radio frequency signal source. Iterative search refers to the process of gradually approaching the optimal value of the objective function through multiple iterations, starting from the initial solution. In one embodiment of this application, the iterative search is implemented using the gradient ascent method. The gradient ascent method calculates the gradient direction of the objective function at the current solution, iteratively updates the position of the solution along the gradient direction, and gradually approaches the maximum value of the objective function. The convergence condition is used to determine whether the iterative search has reached the optimal solution; iteration stops when the gradient norm is less than a preset threshold or the maximum number of iterations is reached.

[0118] In one embodiment, first in the constrained search space Multiple initial solutions are selected, and then the gradient ascent method is used to iteratively search the objective function, starting from each initial solution. In each iteration, the coordinate update formula is:

[0119] in, This is the gradient ascent step size. Let the gradient of the objective function be the position coordinates. It can be approximated using numerical differencing. Repeat the iterations until the gradient norm is reached. Less than the preset convergence threshold Or reach the maximum number of iterations The current solution is the corresponding local optimum.

[0120] Since the objective function has multiple local optima, a single initial solution may converge to a local extremum and fail to reach the global optimum. After obtaining multiple local optima, the objective function values ​​corresponding to each local optima are compared, and the one with the best objective function value is selected as the optimal solution, that is, the position coordinates of the radio frequency signal source relative to the mobile carrier 1000.

[0121] Please see Figure 16 , Figure 16 The line connecting the two points represents the complete translational flight path of the UAV. The triangle in the figure represents the location of the radio frequency signal source as determined by the implementation method of this application, and the circle represents the actual location of the radio frequency signal source. The measured results show an error of only 0.86m.

[0122] Thus, by iteratively searching through multiple initial solutions, the problem of a non-convex objective function and the existence of multiple local optima can be effectively addressed, avoiding the single starting point from getting trapped in local extrema and improving the robustness of locating the radio frequency signal source. Experimental results show that the positioning error of this application is small, further verifying the effectiveness and high accuracy of the method.

[0123] This application also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the positioning method as described in any of the above embodiments.

[0124] Specifically, the computer-readable storage medium can be an optical disc, a digital universal optical disc, a USB flash drive, or a hard disk, solid-state drive, etc. A computer program containing a series of executable instructions is stored in the computer-readable storage medium. When the processor executes these instructions, the positioning method in any of the above embodiments will be implemented. This storage medium enables the technical solution of this application to be distributed, sold, and deployed in the form of a software product. Users can implement the positioning method for the radio frequency signal source of the mobile carrier 1000 by installing or loading it onto a device with corresponding computing capabilities.

[0125] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any other combination. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0126] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatus, mobile carrier 1000, and storage medium can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0128] In addition, the functional modules in the various embodiments of this application can be integrated into a merged module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0129] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for locating a radio frequency signal source, used on a mobile carrier, characterized in that, The mobile carrier includes a first antenna, a second antenna, and a clock source. The first antenna and the second antenna achieve time-frequency synchronization by sharing the clock source. Both the first antenna and the second antenna are used to receive radio frequency signals transmitted by the radio frequency signal source. The method includes: The motion parameters of the mobile carrier, the first radio frequency signal received by the first antenna, and the second radio frequency signal received by the second antenna are acquired in real time, wherein the motion parameters include translational parameters and rotational parameters; The first radio frequency signal and the second radio frequency signal are processed to extract the actual Doppler frequency difference between the first radio frequency signal and the second radio frequency signal; Based on the actual Doppler frequency difference and the rotation parameters, the azimuth angle of the radio frequency signal source relative to the moving carrier is determined; Based on the azimuth angle and the translational parameters of the moving carrier, the position of the radio frequency signal source relative to the moving carrier is determined; Determining the position of the radio frequency signal source relative to the moving carrier based on the azimuth angle and the translational parameters includes: Using any possible position of the radio frequency signal source as a candidate position, the theoretical Doppler frequency difference corresponding to the candidate position is determined based on the candidate position and the translational parameters. The theoretical Doppler frequency difference is matched with the actual Doppler frequency difference to construct the objective function; The search space for the candidate positions is constrained by the azimuth angle; The search is performed within the search space with the objective function as the goal, and the optimal solution is determined as the position of the radio frequency signal source relative to the mobile carrier.

2. The method according to claim 1, characterized in that, Processing the first radio frequency signal and the second radio frequency signal to extract the actual Doppler frequency difference between the first radio frequency signal and the second radio frequency signal includes: Perform a conjugate multiplication operation on the first radio frequency signal and the second radio frequency signal to obtain a conjugate multiplied signal; The actual Doppler frequency difference is obtained by performing time-frequency processing on the conjugate multiplication signal.

3. The method according to claim 2, characterized in that, The actual Doppler frequency difference is obtained by performing time-frequency processing on the conjugate multiplied signal, including: Perform a short-time Fourier transform on the conjugate multiplied signal to obtain the time-frequency power spectrum; The target time-frequency power spectrum is obtained by convolving and smoothing the time-frequency power spectrum along the frequency dimension using a Gaussian kernel function. The actual Doppler frequency difference is extracted based on the target time-frequency power spectrum.

4. The method according to claim 3, characterized in that, Determining the azimuth angle of the radio frequency signal source relative to the moving carrier based on the actual Doppler frequency difference and the rotation parameters includes: Based on the rotation parameters and multiple preset candidate azimuth angles, a theoretical Doppler frequency difference curve corresponding to the preset candidate azimuth angles is generated. The actual Doppler frequency difference is matched with each of the theoretical Doppler frequency difference curves to determine the azimuth angle of the radio frequency signal source relative to the moving carrier.

5. The method according to claim 4, characterized in that, Matching the actual Doppler frequency difference with the theoretical Doppler frequency difference curve to determine the azimuth angle of the radio frequency signal source relative to the moving carrier includes: Each of the theoretical Doppler frequency difference curves is mapped to the target time-frequency power spectrum; The power value of the target time-frequency power spectrum is extracted along the trajectory of each of the theoretical Doppler frequency difference curves; By summing up the power values ​​corresponding to the same theoretical Doppler frequency difference curve, the cumulative energy value corresponding to each curve is obtained. The azimuth angle is the preset candidate azimuth angle corresponding to the theoretical Doppler frequency difference curve with the largest cumulative energy value.

6. The method according to claim 3, characterized in that, The search within the search space aimed at optimizing the objective function includes: Multiple initial solutions are selected within the search space; Each of the aforementioned initial solutions is used as the starting point for an iterative search; The solution with the best objective function value among the local optima obtained from the search is taken as the optimal solution.

7. A positioning device for a radio frequency signal source, used for a moving carrier, characterized in that, The mobile carrier includes a first antenna, a second antenna, and a clock source. The first antenna and the second antenna achieve time-frequency synchronization by sharing the clock source. Both the first antenna and the second antenna are used to receive radio frequency signals transmitted by the radio frequency signal source. The device includes: The acquisition module is used to acquire in real time the motion parameters of the mobile carrier, the first radio frequency signal received by the first antenna, and the second radio frequency signal received by the second antenna, wherein the motion parameters include translational parameters and rotational parameters; The processing module is used to process the first radio frequency signal and the second radio frequency signal to extract the actual Doppler frequency difference between the first radio frequency signal and the second radio frequency signal; The determining module is used to determine the azimuth angle of the radio frequency signal source relative to the moving carrier based on the actual Doppler frequency difference and the rotation parameters; and to determine the position of the radio frequency signal source relative to the moving carrier based on the azimuth angle and the translational parameters of the moving carrier. Determining the position of the radio frequency signal source relative to the moving carrier based on the azimuth angle and the translational parameters includes: Using any possible position of the radio frequency signal source as a candidate position, the theoretical Doppler frequency difference corresponding to the candidate position is determined based on the candidate position and the translational parameters. The theoretical Doppler frequency difference is matched with the actual Doppler frequency difference to construct the objective function; The search space for the candidate positions is constrained by the azimuth angle; The search is performed within the search space with the objective function as the goal, and the optimal solution is determined as the position of the radio frequency signal source relative to the mobile carrier.

8. A mobile carrier, characterized in that, It includes a memory, a processor, and a computer program stored on the memory, wherein the processor executes the computer program to implement the positioning method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the positioning method as described in any one of claims 1 to 6.

Images