A controller orientation positioning method and system based on beamforming main lobe direction

Abstract

This invention discloses a controller orientation localization method and system based on the main lobe direction of beamforming, comprising: acquiring and receiving intermediate frequency or baseband signals from a UAV terminal to obtain timestamped IQ data, and dividing it into multiple time slices according to a preset duration; determining a corresponding angle label for each time slice to form an angle-data segment mapping relationship, wherein the angle label is generated by UAV motion scanning; performing spectral or time-frequency analysis on the IQ data of each time slice to determine the target frequency band where the target signal is located; extracting and normalizing intensity indicators; constructing radiation pattern data; performing radiation pattern smoothing and main lobe sector determination; and performing orientation output and confidence calculation. This invention does not require strict array calibration and multi-channel angle measurement, and can achieve controller orientation estimation under single-channel or few-channel UAV reception conditions. This invention does not rely on communication protocol demodulation, and can construct the radiation pattern based on the energy characteristics of the target frequency band, making it applicable to different standards and encryption conditions.

Description

Technical Field

[0001] This invention relates to the fields of radio direction finding, beamforming, and UAV-borne signal processing technology, specifically a controller orientation positioning method and system based on the main lobe direction of beamforming. Background Technology

[0002] In applications such as UAV surveillance, inspection, and situational awareness, it is often necessary to quickly estimate the direction of the ground controller (operator) from the air to assist in positioning, tracking, or handling. In existing technologies, controller direction estimation usually relies on multi-channel array angle measurement (such as angle of arrival AoA), multi-station cooperative positioning (such as time difference of arrival TDoA), or demodulation / feature recognition of communication signals before positioning.

[0003] However, the following limitations exist in real-world drone-borne scenarios:

[0004] 1) Due to limitations in size, weight, power consumption, and installation calibration, UAV platforms often can only carry single-channel or multi-channel receivers, making it difficult to achieve high-precision AoA angle measurement under strict array calibration conditions;

[0005] 2) The controller link may involve multi-vendor protocols, frequency hopping, encryption and power control, etc. The methods based on demodulation or protocol identification have insufficient universality and stability.

[0006] 3) The presence of multipath, obstruction and co-frequency interference in the environment can cause instantaneous measurement fluctuations, which can easily lead to unstable direction estimation or the generation of false peaks.

[0007] Meanwhile, in many systems communicating with the controller, the signal source often employs beamforming to concentrate energy in the controller's direction to improve link quality, making the radiated energy exhibit main lobe directivity in space. If a UAV can move around the signal source and sample the signal from multiple azimuth angles, angular coverage can be formed using motion scanning, and a radiation pattern can be constructed based on energy characteristics. This allows for controller orientation estimation without relying on complex array calibration or protocol demodulation. Therefore, there is an urgent need for a controller orientation localization scheme based on beamforming main lobe directivity suitable for UAVs. Summary of the Invention

[0008] The purpose of this invention is to provide a controller orientation positioning method and system based on the main lobe direction of beamforming, so as to solve the problems of difficult array angle measurement, strong demodulation dependence, and unstable orientation estimation under multipath interference in the prior art. This invention achieves stable and universal estimation of controller orientation and outputs confidence / uncertainty information.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A controller orientation positioning method based on the main lobe direction of beamforming includes:

[0011] S1. The UAV terminal collects and receives intermediate frequency or baseband signals through a single-channel receiver. The received signals are communication signals transmitted to its controller by the signal source using beamforming technology. IQ data with timestamps is obtained and divided into multiple time slices according to a preset duration.

[0012] S2. For each time slice obtained in S1, determine the corresponding angle label to form an angle-data segment mapping relationship. The angle label is generated by the UAV motion scan.

[0013] S3. Perform spectral or time-frequency analysis on the IQ data of each time slice obtained in S1 to determine the target frequency band where the target signal is located;

[0014] S4. Within the target frequency band determined in S3, extract intensity indices from the IQ data of each time slice and normalize them to obtain normalized intensity indices.

[0015] S5. Based on the angle annotations obtained in S2, the normalized intensity index obtained in S4 is assigned to the bins by angle and statistically fused to construct the angle-intensity corresponding radiation pattern data.

[0016] S6. Smooth the radiation pattern data obtained in S5 and determine the main lobe sector based on the main lobe criterion;

[0017] S7. Based on the main lobe sector output controller direction obtained in S6, calculate the confidence level.

[0018] Furthermore, in S2, the angle label is the geometric azimuth angle of the receiver relative to the source, which is determined by the receiver's positioning information and the source's position; if the angle label has a delay or is missing, it is aligned to the center time of the time slice using nearest neighbor matching, interpolation, smoothing, or filtering.

[0019] Further, S4 includes:

[0020] The intensity index of each time slice within the target frequency band is calculated, and noise floor estimation and normalization are performed to obtain the normalized intensity index. The intensity index is one or more of the following: peak power spectrum of the target frequency band, frequency band energy integral, and weighted energy integral.

[0021] Further, S5 includes:

[0022] The normalized intensity index is assigned to the corresponding angle or angle bin according to the angle label, and statistical fusion is performed on multiple samples of the same angle to obtain the angle-intensity correspondence and form a radiation pattern dataset.

[0023] Further, S6 includes:

[0024] Interpolation, filtering, smoothing, or fitting are performed on the radiation pattern dataset to obtain a smooth radiation pattern; the main lobe sector is determined based on the main lobe criterion, which includes multiple of the following: relative peak threshold, cumulative energy threshold, curve fitting peak width, and fitting residual.

[0025] Furthermore, S6 also includes:

[0026] When there are multiple local peaks in the radiation pattern, the main lobe sector is selected based on the difference between the main peak and the secondary peak, the peak width, the consistency across cycles, or the fitting residual.

[0027] Further, S7 includes:

[0028] Output the main lobe sector center angle or weighted center angle as the controller direction, and output the confidence level;

[0029] The output may also include the main lobe sector width or an uncertainty index calculated based on the main lobe sector width.

[0030] The present invention also provides a controller orientation positioning system based on the main lobe direction of beamforming, for implementing the controller orientation positioning method described above, comprising:

[0031] The signal acquisition module is used to acquire timestamped IQ data and divide it into time slices using a single-channel receiver;

[0032] The synchronization annotation module is used to perform time synchronization on time slices and generate angle annotations to form an angle-data segment mapping;

[0033] The frequency band determination module is used to determine the target frequency band of the target signal;

[0034] The intensity calculation module is used to extract the intensity index of the target frequency band and perform noise floor estimation and normalization.

[0035] The radiation pattern construction module is used to bin the data by angle and statistically fuse it to build a radiation pattern dataset.

[0036] The main lobe determination module is used to smooth or fit the radiation pattern and determine the main lobe sector.

[0037] The output module is used to output the controller direction, confidence level, and / or uncertainty index.

[0038] Furthermore, it also includes a pose acquisition unit, which is used to acquire the UAV's position, heading, or attitude information and provide it to the synchronous annotation module to generate angle annotations.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1) No strict array calibration and multi-channel angle measurement are required; controller orientation estimation can be achieved under single-channel or low-channel receiver conditions of UAV.

[0041] 2) It does not rely on communication protocol demodulation; it can construct radiation patterns based on the energy characteristics of the target frequency band, and is applicable to different standards and encryption conditions.

[0042] 3) Improve stability under gain drift, multipath and sudden interference conditions by combining noise floor normalization and robust statistics;

[0043] 4) By determining the main lobe sector rather than the maximum value at a single point, the ability to resist sidelobe and multi-peak interference is enhanced, and the confidence / uncertainty is output to facilitate upper-level decision-making;

[0044] 5) The drone can repeatedly sample the same angle by circling the signal source multiple times, further improving the directional accuracy. Figure 1 Consistency and reliability of main lobe determination. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of a controller orientation positioning method based on the main lobe direction of beamforming.

[0046] Figure 2 This is a block diagram of a controller orientation positioning system based on the main lobe direction of beamforming.

[0047] Figure 3 This is a schematic diagram of an application scenario in Example 1.

[0048] Figure 4 This is a schematic diagram of the radiation pattern and main lobe sector determination in Example 1.

[0049] Figure 5 This is a schematic diagram illustrating the time alignment and angle mapping of the intensity index, where, Figure 5 (a) shows the original sampling sequence of intensity index changes over time and the segmentation of the orbital period; Figure 5 Figure (b) shows the angle-intensity curve obtained by aligning the intensity indices of each period to time / period and mapping them to the angle domain, and then performing angle binning statistics and smoothing.

[0050] In the diagram: 10 is the signal source, 20 is the controller, 30 is the UAV, 31 is the receiving antenna, 40 is the main lobe, and 50 is the flight trajectory. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Please see Figure 1 A controller orientation positioning method based on the main lobe direction of beamforming, comprising:

[0053] S1. The UAV terminal acquires the received intermediate frequency or baseband signal through a single-channel receiver. The received signal is the communication signal transmitted to its controller by the signal source using beamforming technology. IQ data with timestamps is obtained and divided into multiple time slices according to a preset duration. IQ data is a complex signal containing a real part (I, In-phase) and an imaginary part (Q, Quadrature).

[0054] S2. For each time slice obtained in S1, determine the corresponding angle label to form an angle-data segment mapping relationship. The angle label is generated by the UAV motion scan.

[0055] The angle label is the geometric azimuth angle of the receiver relative to the source. This geometric azimuth angle can be determined by the receiver's positioning information and the source's position. If the angle label is delayed or missing, it can be aligned to the center time of the time slice using nearest neighbor matching, interpolation, smoothing, or filtering.

[0056] S3. Perform spectral or time-frequency analysis on the IQ data of each time slice obtained in S1 to determine the target frequency band where the target signal is located.

[0057] S4. Within the target frequency band determined in S3, extract intensity indices from the IQ data of each time slice and normalize them to obtain normalized intensity indices, including:

[0058] The intensity index of each time slice within the target frequency band is calculated, and noise floor estimation and normalization are performed to obtain the normalized intensity index. The intensity index can be the peak power spectrum of the target frequency band, the frequency band energy integral, the weighted energy integral, etc. Normalization can be performed by difference, ratio or other equivalent methods to reduce the influence of noise drift and gain change.

[0059] S5. Based on the angle annotations obtained in S2, the normalized intensity indices obtained in S4 are assigned to bins by angle and statistically fused to construct angle-intensity corresponding radiation pattern data, including:

[0060] Normalized intensity indices are assigned to corresponding angles or angle bins based on angle labels, and statistical fusion is performed on multiple samples from the same angle to obtain the angle-intensity correspondence, forming a radiation pattern dataset. Statistical fusion can be based on the mean, median, truncated mean, quantile mean, or other robust statistics.

[0061] S6. Smooth the radiation pattern data obtained in S5, and determine the main lobe sector based on the main lobe criterion, including:

[0062] Perform interpolation, filtering, smoothing, or fitting on the radiation pattern dataset to obtain a smooth radiation pattern; determine the main lobe sector based on the main lobe criterion, which may include, but is not limited to, the relative peak threshold (e.g., -3dB), the cumulative energy threshold, the curve fitting peak width, and the fitting residual.

[0063] When there are multiple local peaks in the radiation pattern, the main lobe sector can be selected based on the difference between the main peak and the secondary peak, the peak width, the consistency across cycles, or the fitting residual.

[0064] S7. Based on the main lobe sector output controller direction obtained in S6, calculate the confidence level, including:

[0065] The output main lobe sector center angle or weighted center angle serves as the controller direction, and a confidence level is also output. The confidence level can be determined by factors such as the difference between the main peak and secondary peaks, the ratio of the main peak to the noise floor, the fitting residual, peak sharpness, and cross-cycle consistency; its calculation form can be linear weighting, normalized scoring, or other equivalent methods. The output may also include the main lobe sector width or an uncertainty index calculated based on the main lobe sector width.

[0066] Please see Figure 2 A controller orientation positioning system based on the main lobe direction of beamforming is used to implement the controller orientation positioning method described above. It includes a signal acquisition module, a synchronization labeling module, a frequency band determination module, an intensity calculation module, a radiation pattern construction module, a main lobe determination module, an output module, and a pose acquisition unit. Each module can be implemented by hardware circuits, computer programs executed by processors, or a combination of both.

[0067] The signal acquisition module is used to acquire timestamped IQ data and divide it into time slices using a single-channel receiver.

[0068] The synchronization annotation module is used to perform time synchronization on time slices and generate angle annotations, forming an angle-data segment mapping.

[0069] The frequency band determination module is used to determine the target frequency band of the target signal.

[0070] The intensity calculation module is used to extract the intensity index of the target frequency band and perform noise floor estimation and normalization.

[0071] The orientation pattern building module is used to bin the data by angle and statistically fuse it to build an orientation pattern dataset.

[0072] The main lobe determination module is used to smooth or fit the radiation pattern and determine the main lobe sector.

[0073] The output module is used to output the controller's direction, confidence level, and / or uncertainty index.

[0074] The pose acquisition unit is used to acquire the UAV's position, heading, or attitude information and provide it to the synchronous annotation module to generate angle annotations.

[0075] Example 1: Controller Orientation Estimation for UAV Flight Circumduction Motion Scan

[0076] like Figure 3 As shown, in this embodiment, the UAV carries a receiver to perform a fly-around mission in the air. The fly-around target is a signal source communicating with the controller. The signal source can be a fixed ground device or an airborne mobile platform. The signal source uses beamforming technology to transmit communication signals towards the controller, thereby forming a main lobe directionality in space. The UAV collects this signal by flying around at different azimuth angles and reconstructs the radiation pattern to estimate the controller's direction.

[0077] 1) S1 signal acquisition

[0078] The drone receives radio frequency signals through a single-channel receiver and down-converts them to intermediate frequency or baseband to obtain IQ data. It adds a timestamp to each segment of IQ data and divides it into time slices according to the duration T (e.g., 10ms to 200ms).

[0079] 2) S2 Synchronization and Angle Labeling

[0080] The UAV acquires pose information (e.g., position and heading or attitude output by GNSS / IMU fusion). Preferably, when the source position P... s Known or estimable UAV position P u When (t) is known, calculate the geometric azimuth angle θ(t) of the information source relative to the UAV as the angle label, for example:

[0081]

[0082] In the formula, P s =(x s ,y s P represents the position coordinates of the information source in the planar coordinate system. u (t)=(x u (t),y u (t) represents the position coordinates of the UAV in the coordinate system at time t; atan2(y,x) represents the two-parameter arctangent function, used to calculate the polar angle of the vector calculated from the plane rectangular coordinates.

[0083] Then, θ(t) is aligned to the center time of each time slice to form an angle-data segment mapping. If there is a delay or missing angle labeling, nearest neighbor matching, linear interpolation, or filtering (such as Kalman filtering) can be used to align the angle sequence.

[0084] 3) S3 target frequency band determination

[0085] Power spectral density is obtained by performing FFT or STFT on each time slice. Peak search or energy clustering is then performed in the candidate frequency bands to determine the target frequency band [f1, f2]. When the target signal frequency band is known, it can be set directly; when multiple candidate frequency bands exist, they can be calculated in parallel, and the frequency band with the highest confidence is selected as the target frequency band. The target frequency band can be located at 2.4 GHz, 5.8 GHz, or other frequency bands; this invention is not limited to a specific frequency band.

[0086] 4) S4 Intensity Index and Normalization

[0087] Calculate the intensity index I for each time slot within the [f1,f2] frequency band. k ,For example:

[0088] Peak value: ;

[0089] Energy Integral: ;

[0090] Weighted integral: .

[0091] Among them, P k (f) represents the power spectral density value at frequency f in the k-th time slice, and max indicates taking the maximum value; w(f) represents the frequency weighting function; I k This represents the intensity index corresponding to the k-th time slice, and its calculation method can be one of the above formulas.

[0092] Noise floor N k It can be estimated from neighboring frequency bands, the median of the spectrum, or a sliding window.

[0093] The normalized strength can be achieved by:

[0094] difference: ;

[0095] Or ratio: ;

[0096] in, This represents the normalized intensity index, where ε is a small constant to prevent the denominator from being zero.

[0097] 5) S5 constructs the directional pattern dataset

[0098] Will According to angle θk The samples are categorized into angle bins (e.g., one bin for every 1° to 10°), and the median or truncated mean is used to fuse the samples within the same angle bin to obtain the radiation pattern G(θ). When the UAV flies around multiple times, the same angle bin will be sampled multiple times, and fusion can suppress abnormal peaks caused by multipath interference and sudden disturbances.

[0099] like Figure 5 As shown in (a), the sampling sequence of the intensity index over time can be segmented according to the orbital period; as Figure 5 As shown in (b), the intensity index in each period can be mapped to the angle domain by angle labeling, and the angle-intensity curve can be obtained by angle binning statistical fusion and smoothing for subsequent main lobe sector determination. The mapping can be achieved by direct association through angle labeling, or by resampling / interpolating the samples in the period and mapping them to a uniform angle grid. This invention is not limited to these methods.

[0100] 6) S6 smoothing and main lobe sector determination

[0101] like Figure 4 As shown, interpolation and smoothing (e.g., moving average, Savitzky-Golay, or polynomial / Gaussian fitting) of G(θ) yields a smoothed radiation pattern. The main lobe sector can be determined using any one or a combination of the following criteria:

[0102] -3dB criterion: Take Peak point θ p The main lobe sector is to satisfy The connected angular interval;

[0103] Cumulative energy criterion: Find the minimum angle interval so that the cumulative energy reaches the threshold of the total energy (e.g., 50% to 90%).

[0104] Fitting criteria: Fit the main peak using a parametric model and determine the sector based on the peak width.

[0105] When multiple local peaks exist, the main lobe sector can be selected based on the peak difference, peak width, cross-cycle consistency, or fitting residual to suppress side lobes and interference peaks.

[0106] 7) S7 output direction and confidence level

[0107] Output the center angle θ of the main lobe sector out (Alternatively, the weighted center angle can be obtained by weighting the intensity within the sector) as the controller direction. The confidence level C can be calculated from the difference between the main peak and the secondary peak P1-P2, the difference or ratio between the main peak and the noise floor, and the fitting residual E. fit Factors such as peak sharpness and cross-circle consistency are considered. An example scoring function (not limited) is:

[0108]

[0109] Where P1 represents the intensity value of the main peak in the radiation pattern, and P2 represents the intensity value of the secondary peak in the radiation pattern. The weights are preset or adaptive, where N represents the low noise estimate and S is the sharpness index. Optionally, the main lobe sector width can also be output as an uncertainty index, or the uncertainty can be calculated based on the main lobe sector width.

[0110] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A controller orientation positioning method based on the main lobe direction of beamforming, characterized in that, include: S1. The UAV terminal collects and receives intermediate frequency or baseband signals through a single-channel receiver. The received signals are communication signals transmitted to its controller by the signal source using beamforming technology. IQ data with timestamps is obtained and divided into multiple time slices according to a preset duration. S2. For each time slice obtained in S1, determine the corresponding angle label to form an angle-data segment mapping relationship. The angle label is generated by the UAV motion scan. S3. Perform spectral or time-frequency analysis on the IQ data of each time slice obtained in S1 to determine the target frequency band where the target signal is located; S4. Within the target frequency band determined in S3, extract intensity indices from the IQ data of each time slice and normalize them to obtain normalized intensity indices. S5. Based on the angle annotations obtained in S2, the normalized intensity index obtained in S4 is assigned to the bins by angle and statistically fused to construct the angle-intensity corresponding radiation pattern data. S6. Smooth the radiation pattern data obtained in S5 and determine the main lobe sector based on the main lobe criterion; S7. Based on the main lobe sector output controller direction obtained in S6, calculate the confidence level.

2. The controller orientation positioning method based on the main lobe direction of beamforming according to claim 1, characterized in that, In step S2, the angle label is the geometric azimuth angle of the receiver relative to the source, which is determined by the receiver's positioning information and the source's position. If the angle label is delayed or missing, it is aligned to the center time of the time slice using nearest neighbor matching, interpolation, smoothing, or filtering.

3. The controller orientation positioning method based on the main lobe direction of beamforming according to claim 1, characterized in that, S4 includes: The intensity index of each time slice within the target frequency band is calculated, and noise floor estimation and normalization are performed to obtain the normalized intensity index. The intensity index is one or more of the following: peak power spectrum of the target frequency band, frequency band energy integral, and weighted energy integral.

4. The controller orientation positioning method based on the main lobe direction of beamforming according to claim 1, characterized in that, S5 includes: The normalized intensity index is assigned to the corresponding angle or angle bin according to the angle label, and statistical fusion is performed on multiple samples of the same angle to obtain the angle-intensity correspondence and form a radiation pattern dataset.

5. The controller orientation positioning method based on the main lobe direction of beamforming according to claim 1, characterized in that, S6 includes: Interpolation, filtering, smoothing, or fitting are performed on the radiation pattern dataset to obtain a smooth radiation pattern; the main lobe sector is determined based on the main lobe criterion, which includes multiple of the following: relative peak threshold, cumulative energy threshold, curve fitting peak width, and fitting residual.

6. The controller orientation positioning method based on the main lobe direction of beamforming according to claim 5, characterized in that, S6 further includes: When there are multiple local peaks in the radiation pattern, the main lobe sector is selected based on the difference between the main peak and the secondary peak, the peak width, the consistency across cycles, or the fitting residual.

7. The controller orientation positioning method based on the main lobe direction of beamforming according to claim 1, characterized in that, S7 includes: Output the main lobe sector center angle or weighted center angle as the controller direction, and output the confidence level; The output may also include the main lobe sector width or an uncertainty index calculated based on the main lobe sector width.

8. A controller orientation positioning system based on the main lobe direction of beamforming, used to implement the controller orientation positioning method as described in any one of claims 1-7, characterized in that, include: The signal acquisition module is used to acquire timestamped IQ data and divide it into time slices using a single-channel receiver; The synchronization annotation module is used to perform time synchronization on time slices and generate angle annotations to form an angle-data segment mapping; The frequency band determination module is used to determine the target frequency band of the target signal; The intensity calculation module is used to extract the intensity index of the target frequency band and perform noise floor estimation and normalization. The radiation pattern construction module is used to bin the data by angle and statistically fuse it to build a radiation pattern dataset. The main lobe determination module is used to smooth or fit the radiation pattern and determine the main lobe sector. The output module is used to output the controller direction, confidence level, and / or uncertainty index.

9. A controller orientation positioning system based on the main lobe direction of beamforming according to claim 8, characterized in that, It also includes a pose acquisition unit, which is used to acquire the UAV's position, heading or attitude information and provide it to the synchronous annotation module to generate angle annotations.

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