Self-adaptive rotating baseline direction finding method, device, equipment, medium and terminal

Abstract

The invention discloses a self-adaptive rotating baseline direction finding method, device and equipment, a medium and a terminal. Signals are continuously collected through the base line rotatable antenna array; rapidly identifying and outputting environment parameters by using the trained environment evaluation model, and evaluating the environment grade; constructing a dynamic direction-finding angle model by combining the attitude parameters and the motion parameters of the mobile platform, the control parameters of the antenna array and the environmental parameters; according to the environment parameters and the environment grades, a dynamic parameter optimization rule is formulated, and control parameters, direction-finding resolving parameters and signal processing algorithm parameters of the antenna array are adjusted; and based on the environmental parameters, the adjusted control parameters of the antenna array, the adjusted direction-finding resolving parameters and the adjusted adaptive filter parameters, calculating an incoming wave angle by using a dynamic direction-finding angle model. According to the method, high-precision incoming wave signal direction finding is realized in a dynamic environment, and the method is suitable for mobile scenes such as unmanned aerial vehicles and vehicle-mounted communication.

Description

Technical Field

[0001] This invention belongs to the field of radio direction finding technology, specifically relating to an adaptive rotating baseline direction finding method, apparatus, equipment, medium, and terminal. Background Technology

[0002] In modern wireless communication and electronic warfare, accurately acquiring the location information of signal sources is of paramount importance. Traditional direction-finding systems typically employ fixed-baseline antenna arrays, determining the direction of the signal source by measuring the phase difference or time difference of the signal arriving at different antennas. However, the performance of this method is limited by the fixed baseline length and position, resulting in low spatial resolution. Furthermore, when the direction-finding system is applied to mobile platforms such as drones and vehicle-mounted equipment, the dynamic movement of the platform alters the actual geometry of the baseline, leading to inaccurate phase difference measurements. In addition, multipath interference in complex environments further amplifies the time delay and phase error of the signal propagation path, significantly reducing the stability of the direction-finding results. These limitations severely restrict the accuracy and reliability of fixed-baseline direction-finding systems in dynamic scenarios, making it difficult to meet the requirements of practical applications.

[0003] While some existing rotating baseline direction finding techniques can improve direction finding errors to some extent, they also rely on the assumption of a static environment. When affected by motion noise, Doppler frequency shift and multipath interference, they lack an effective adaptive signal processing mechanism and are unable to respond to changes in the signal environment in real time, making it difficult to guarantee the accuracy and reliability of direction finding results in highly dynamic environments. Summary of the Invention

[0004] To effectively address the aforementioned problems in the existing technology, this invention provides an adaptive rotating baseline direction finding method, apparatus, device, medium, and terminal, suitable for highly dynamic environments. By combining a rotatable antenna array, a dynamic direction finding angle model is constructed, and dynamic parameter optimization rules are formulated, enabling high-precision signal source positioning in dynamic environments and significantly improving direction finding errors in mobile scenarios.

[0005] This invention provides an adaptive rotating baseline direction finding method, comprising: Step 110: Construct a baseline rotatable antenna array, which is mounted on a mobile platform; the antenna array includes at least three baselines, and incoming wave signals are continuously acquired during the rotation period of the baselines; the control parameters of the antenna array are adjustable, including the number of baselines and the signal acquisition frequency; Step 120: Establish an environmental assessment model based on a convolutional neural network. Use a dataset of environmental signals to pre-train and self-test the environmental assessment model to obtain a trained environmental assessment model, which is used to quickly identify and output environmental parameters. Use the environmental parameters to design an environmental scoring formula to classify the environment and obtain the environmental level. Step 130: Using the attitude and motion parameters of the mobile platform, the control parameters of the antenna array, and the environmental parameters, obtain multiple direction finding angle sequences and dynamic direction finding error sequences, and construct a dynamic direction finding angle model. Step 140: Based on environmental parameters and environmental level, formulate dynamic parameter optimization rules to adaptively or dynamically adjust the control parameters, direction finding parameters, and signal processing algorithm parameters of the antenna array; the direction finding parameters include direction finding mode and accuracy weight parameters; the signal processing algorithm parameters include at least adaptive filter parameters. Step 150: Based on environmental parameters, adjusted antenna array control parameters, adjusted direction finding parameters, and adjusted adaptive filter parameters, calculate the incoming wave angle using a dynamic direction finding angle model. Step 160: By calculating the residuals, outliers in multiple direction finding angle sequences are eliminated, and the optimized wave arrival angle is obtained again using the dynamic direction finding angle model to complete the direction finding.

[0006] On the other hand, the present invention provides an adaptive rotating baseline direction finding device, which implements the steps of the aforementioned adaptive rotating baseline direction finding method. The device includes the following modules: The first module is used to construct a baseline-rotatable antenna array, which is mounted on a mobile platform; the antenna array includes at least three baselines, and continuously acquires incoming wave signals during the rotation period of the baselines; the control parameters of the antenna array are adjustable, including the number of baselines configured and the signal acquisition frequency; The second module is used to establish an environmental assessment model based on a convolutional neural network. It uses a dataset of environmental signals to pre-train and self-test the environmental assessment model to obtain a trained environmental assessment model, which is used to quickly identify and output environmental parameters. It then uses the environmental parameters to design an environmental scoring formula to classify the environment and obtain the environmental level. The third module is used to acquire multiple direction-finding angle sequences and dynamic direction-finding error sequences by utilizing the attitude and motion parameters of the mobile platform, the control parameters of the antenna array, and environmental parameters, and to construct a dynamic direction-finding angle model. The fourth module is used to formulate dynamic parameter optimization rules based on environmental parameters and environmental levels, and to adaptively or dynamically adjust the control parameters, direction finding parameters, and signal processing algorithm parameters of the antenna array; the direction finding parameters include direction finding mode and accuracy weight parameters; the signal processing algorithm parameters include at least adaptive filter parameters. The fifth module is used to calculate the incoming wave angle based on environmental parameters, adjusted antenna array control parameters, adjusted direction finding parameters, and adjusted adaptive filter parameters, using a dynamic direction finding angle model. The sixth module is used to remove outliers from multiple direction-finding angle sequences through residual calculation, and then use the dynamic direction-finding angle model to obtain the optimized wave arrival angle to complete the direction finding.

[0007] This invention protects a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the aforementioned adaptive rotating baseline orientation method.

[0008] The present invention also provides a storage medium for storing a computer program, which, when executed by a processor, implements the steps of the aforementioned adaptive rotating baseline orientation method.

[0009] Furthermore, the present invention protects an information data processing terminal, which includes the aforementioned adaptive rotating baseline direction finding device.

[0010] Compared with existing technologies, the adaptive rotating baseline direction finding method, apparatus, device, medium, and terminal provided by the present invention have the following beneficial technical effects: (1) By combining the baseline rotation of the antenna array, the attitude parameters and motion parameters of the mobile platform and the environmental parameters, a dynamic direction finding angle model is constructed. The control parameters of the antenna array, the attitude, speed, Doppler frequency shift, environmental interference, etc. of the mobile platform are incorporated into the dynamic direction finding and direction finding error formula of the incoming wave angle. Multiple direction finding angle sequences and dynamic direction finding error sequences are obtained, and the incoming wave angle is calculated. The weighted fusion method is used to provide compensation basis and eliminate the interference of the dynamic environment on the phase difference.

[0011] (2) Based on environmental parameters and environmental level, dynamic parameter optimization rules are formulated to optimize and adjust the control parameters, direction finding calculation parameters and adaptive filter parameters of the antenna array in real time, realizing dynamic matching between the baseline direction finding method and environmental conditions, and making it more adaptable to the dynamic environment direction finding of complex environments.

[0012] (3) Using the residual results obtained by residual calculation, the abnormal data in the direction finding results obtained by calculating the wave angle using the weighted average method are repeatedly removed, which further improves the accuracy of the direction finding results. Attached Figure Description

[0013] Figure 1 This is a flowchart illustrating an adaptive rotating baseline direction finding method according to the first embodiment of the present invention; Figure 2 In the second embodiment of the present invention, the measured value of the baseline phase difference within one rotation of baseline AB varies with... Change curve graph; Figure 3 The dynamic direction finding angle calculated in the second embodiment of the present invention varies with... Change curve graph; Figure 4 The dynamic direction finding error calculated in the second embodiment of the present invention varies with... Change curve graph; Figure 5 The residual corresponding to the initial calculation of the wave arrival angle in the second embodiment of the present invention varies with... Change curve graph; Figure 6 The standardized residual corresponding to the initial calculation of the wave arrival angle in the second embodiment of the present invention is as follows: Change curve graph; Figure 7 The measured value of the baseline phase difference within one rotation of baseline AB in the third embodiment of the present invention varies with... Change curve graph; Figure 8 The dynamic direction finding angle calculated in the third embodiment of the present invention varies with... Change curve graph; Figure 9 The dynamic direction finding error calculated in the third embodiment of the present invention varies with... Change curve graph; Figure 10 The residual corresponding to the initial calculation of the wave arrival angle in the third embodiment of the present invention is as follows: Change curve graph; Figure 11 The standardized residual corresponding to the initial calculation of the wave arrival angle in the third embodiment of the present invention is as follows: Change curve graph; Figure 12 The measured value of the baseline phase difference within one rotation of baseline AB in the fourth embodiment of the present invention varies with... Change curve graph; Figure 13 The dynamic direction finding angle calculated in the fourth embodiment of the present invention varies with... Change curve graph; Figure 14 The dynamic direction finding error calculated in the fourth embodiment of the present invention varies with... Change curve graph; Figure 15 The residual corresponding to the initial calculation of the wave arrival angle in the fourth embodiment of the present invention is as follows: Change curve graph; Figure 16 The standardized residual corresponding to the initial calculation of the wave arrival angle in the fourth embodiment of the present invention is as follows: Change curve graph. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0015] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] In the first embodiment, as Figure 1 As shown, the present invention provides an adaptive rotating baseline direction finding method, comprising: Step 110: Construct a baseline rotatable antenna array, which is mounted on a mobile platform; the antenna array includes at least three baselines, and incoming wave signals are continuously acquired during the rotation period of the baselines; the control parameters of the antenna array are adjustable, including the number of baselines and the signal acquisition frequency; Step 120: Establish an environmental assessment model based on a convolutional neural network. Use a dataset of environmental signals to pre-train and self-test the environmental assessment model to obtain a trained environmental assessment model, which is used to quickly identify and output environmental parameters. Use the environmental parameters to design an environmental scoring formula to classify the environment and obtain the environmental level. Step 130: Using the attitude and motion parameters of the mobile platform, the control parameters of the antenna array, and the environmental parameters, obtain multiple direction finding angle sequences and dynamic direction finding error sequences, and construct a dynamic direction finding angle model. Step 140: Based on environmental parameters and environmental level, formulate dynamic parameter optimization rules to adaptively or dynamically adjust the control parameters, direction finding parameters, and signal processing algorithm parameters of the antenna array; the direction finding parameters include direction finding mode and accuracy weight parameters; the signal processing algorithm parameters include at least adaptive filter parameters. Step 150: Based on environmental parameters, adjusted antenna array control parameters, adjusted direction finding parameters, and adjusted adaptive filter parameters, calculate the incoming wave angle using a dynamic direction finding angle model. Step 160: By calculating the residuals, outliers in multiple direction finding angle sequences are eliminated, and the optimized wave arrival angle is obtained again using the dynamic direction finding angle model to complete the direction finding.

[0017] Specifically, in step 110, the baseline rotatable antenna array adopts an equilateral triangle rotating antenna array with three equal-length baselines, capable of continuously acquiring incoming wave signals. The baseline rotation speed and the incoming wave signal acquisition frequency are adjustable. The symmetry and rotation characteristics of the equilateral triangle rotating antenna array, through algorithm fusion in anti-interference mode, can effectively suppress interference signals.

[0018] In step 120, the environmental assessment model uses a convolutional neural network for pre-training and self-testing with a large-scale, diverse dataset. This enables automatic feature extraction of the time and frequency domain information of the acquired signals, and outputs the signal-to-noise ratio (SNR) and multipath delay spread (SDR). Interference power ratio () Environmental parameters such as ).

[0019] The dataset includes both measured and simulated data. The measured data was acquired using a USRP (Universal Software Radio Peripheral) device to collect actual environmental signals in urban and suburban areas, and the SNR was simultaneously recorded using a spectrum analyzer and channel sounder. Reference values. Simulation data are obtained by labeling the actual SNR, , Then Gaussian noise and impulse interference signals are added to generate the signal.

[0020] The signal-to-noise ratio (SNR) parameter output by the environmental assessment model is normalized: ; in, ,when hour, Approaching 0, hour, It tends to 1.

[0021] Multipath delay spread ( Perform parameter normalization: ; in, ,when hour, Approaching 0, hour, It tends to 1.

[0022] For interference power ratio ( Perform parameter normalization: ; in, ,when hour, Approaching 0, hour, It tends to 1.

[0023] right Set the weighting coefficients for the environmental parameters respectively , and Establish an environmental scoring formula: ; set up ∈ (0.7,1] represents a superior environment. ∈ (0.3, 0.7] represents a moderate environment. The range [0, 0.3] represents a poor environment. In practical applications, the environment score is calculated every 100ms, triggering a level switch.

[0024] The process for constructing the dynamic orientation angle model in step 130 above is as follows: Assume the rotating platform is located on the horizontal plane of the spatial coordinate system. Rotate counterclockwise around the origin O, with the axis of rotation being... The axis and antennas A, B, and C are fixed on the rotating platform, with a straight-line distance of 0.5 between each other. In the initial state, the normal to baseline AB is perpendicular to... The three antennas are parallel in the positive direction of the axes, and their positions in the spatial coordinate system are as follows: , , Let the direction vectors of baselines AB, BC, and CA be perpendicular to each other. The included angles in the positive direction of the axis are respectively The included angle range is The baseline direction vector can be obtained as follows: ; If the antenna array is mounted on a mobile platform, the platform will generate a yaw angle during its movement. Pitch angle Roll angle The value ranges are respectively , , The attitude parameters of the mobile platform include: yaw angle. Pitch angle Roll angle And the rotation matrix. Changes in the pose of the mobile platform cause changes in the baseline vector, which are expressed by the rotation matrix. Represented as: ; ; ; ; ; in , , They are respectively around axis, axis, The rotation matrix of the axis. It is the baseline direction vector after considering changes in platform attitude.

[0025] At this point, let the wavelength of the incoming space wave signal be... The angle of the incident wave is , This indicates that the incident direction of the incoming wave signal is in Projection on a plane and The angle between the positive axes.

[0026] The direction vector of the incoming signal is: .

[0027] Let the measured phase differences of baselines AB, BC, and CA be respectively The formula for the phase difference of the baseline can be obtained as follows: ; Further calculations yielded the following: ; Further substitution and rearrangement yields the dynamic direction-finding angle formula: ; in: , , ; In the above formula, It is a rotation matrix The first two rows and first two columns of elements can be obtained from... Calculated.

[0028] According to the dynamic direction-finding angle formula, direction-finding accuracy is closely related to the measurement of parameters such as baseline phase difference, signal wavelength, baseline length, baseline rotation angle, and carrier attitude angle. The dynamic direction-finding angle formula can be transformed as follows: ; Take the difference between the two sides of the equation: ; Solving Difference: ; in, It is a signal wavelength measurement error. It is the baseline The phase difference measurement error, due to Compared with baseline It is related, so it is recorded as .

[0029] This invention suppresses [the problem] by using a high-precision sensor. The measurement error is negligible. error By simplifying the difference formula, we obtain the dynamic direction finding error reference formula: ; in, It is a signal wavelength measurement error. It is the baseline The phase difference measurement error.

[0030] A detailed analysis of the above formula leads to the following conclusions: (1) Denominator This indicates the geometrical orthogonality between the baseline direction and the direction of incoming waves; the direction finding accuracy is highest when the two are perpendicular.

[0031] (2) Numerator The smaller the measurement error of the signal wavelength and phase difference, the higher the direction finding accuracy.

[0032] During the operation of a mobile platform, the antenna array is prone to a highly dynamic environment, making the phase difference measurement error and signal wavelength measurement error particularly important for direction finding accuracy. This is addressed by utilizing the motion parameters of the mobile platform, including its speed and direction of motion.

[0033] Phase difference measurement error generally consists of two parts: random error and interference error. The random error of phase difference is related to the repeatability of the measurement. Let the signal acquisition frequency be... Total number of phase difference measurements With time The relationship is The random error of phase difference decreases as the amount of data increases. ; Phase difference interference errors mainly include noise errors, multipath errors, and interference source errors: Noise can generally be considered as Gaussian white noise, and the error it causes is related to the environmental signal-to-noise ratio (SNR), which can be expressed as: .

[0034] Multipath error is caused by signal superposition and is related to signal bandwidth. 、 Multipath delay spread ( Related to ) can be represented as Phase error caused by interference source and power ratio of interference to signal ( Related to this, it can be estimated as follows: .

[0035] By assuming that various error sources are independent, and by summing the variances, an estimate of the phase difference measurement error is obtained. It can be represented as: ; in This is a proportionality coefficient, which can be obtained through laboratory measurement.

[0036] Signal wavelength measurement error is mainly affected by the Doppler effect. Let the speed of the moving platform be... The direction of movement of the mobile platform is determined by... Projection on a plane and The angle between the positive axes is given and set as follows: The resulting Doppler frequency shift is: ; The corresponding wavelength changes are: ; in For the speed of light, For the signal wavelength, The signal frequency.

[0037] Taking all the above error terms into account, and Substituting into the previous direction-finding error formula, we can obtain the dynamic direction-finding error: ; Based on the dynamic direction-finding angle formula and dynamic direction-finding error, a weighted vector fusion method is used to estimate the high-precision wave arrival angle, resulting in a dynamic direction-finding angle model: conduct The dynamic direction finding angle sequence obtained from the independent direction finding is { The corresponding direction-finding error is { }, , ; Accuracy weight parameters are constructed using direction-finding errors, and the accuracy weight allocation is set to be inversely proportional to the square of the direction-finding error: ; It is a term to prevent division by zero; Using precision weight parameters to analyze dynamic direction finding angle sequences { A weighted average is then performed to obtain multiple sequences of direction-finding angles: ; ; in, This refers to the number of baseline configurations; By taking the weighted average of multiple direction-finding angle sequences and accuracy weight parameters, the angle of arrival is estimated, thus obtaining the dynamic direction-finding angle model in single-baseline direction-finding mode: ; in, .

[0038] When using the single-baseline direction finding mode, single baseline The dynamic direction finding angle sequence obtained from the independent direction finding is { The corresponding direction-finding error is { }, Either 2 or 3, due to symmetry, let's take... .

[0039] Take multiple direction finding angle sequences The weighted average value is used to estimate the angle of arrival, thus obtaining the dynamic angle model for single-baseline orientation finding mode: , ; in, , ; It is a precision weighting parameter. It is to prevent division by zero.

[0040] Similarly, when using the three-baseline direction finding mode, The dynamic direction finding angle sequence is obtained from the sub-independent direction finding. }, The corresponding direction finding error is { }, Multiple direction finding angle sequences After weighted averaging, the angle of arrival is estimated, resulting in the dynamic direction finding angle model under the three-baseline direction finding mode: , ; in, , .

[0041] In step 140, the dynamic parameter optimization rules include adaptively adjusting the control parameters of the antenna array, and dynamically adjusting the direction finding calculation parameters and signal processing algorithm parameters.

[0042] The control parameters of the antenna array include the number of baselines and the signal acquisition frequency. In a superior environment, a single baseline is configured with a signal acquisition frequency of 1kHz; in a medium environment, three baselines are configured with a signal acquisition frequency of 500Hz; and in a poor environment, three baselines are configured with a signal acquisition frequency of 100Hz.

[0043] The direction finding solution parameters include the direction finding mode and the accuracy weighting parameter.

[0044] Under optimal conditions, a single baseline direction finding mode is used, and the dynamic direction finding angle model is as follows: ,in, , ; The precision weighting parameter is dynamically adjusted based on environmental parameters. , .

[0045] In medium and poor environments, a three-baseline orientation finding mode is used, and the dynamic orientation finding angle model is as follows: ,in , ; , ; .

[0046] The precision weighting parameter is dynamically adjusted based on environmental parameters: , ; .

[0047] in The normalized multipath delay and interference power ratio.

[0048] Signal processing algorithms mainly refer to adaptive filter parameters. Adaptive filters use the recursive least squares (RLS) algorithm to set filter parameters: (1) Filter order The ability to capture multipath delay; (2) Forgetting factor Controlling the weight of historical data ( , The smaller the value, the faster the tracking speed. (3) Regularization parameters To prevent the covariance matrix from being singular (usually by taking small positive numbers, such as...), ).

[0049] Define the algorithm iteration formula: (1) Input signal: , Indicates matrix transpose; (2) Expected signal: (Estimated value of direct path signal); (3) Gain vector update: ; (4) Error calculation: ; (5) Filter coefficient update: ; (6) Covariance matrix update: .

[0050] Set filter parameter adjustment rules: (1) Filter order According to multipath delay spread ( Dynamic adjustment: ; order Follow Increase linearly, cover 3 times the standard deviation.

[0051] (2) Forgetting factor Combined signal-to-noise ratio (SNR) and interference power ratio ( )Adjustment: ; When the signal-to-noise ratio is high Increased interference enables rapid tracking; however, with increased interference, This reduces and enhances noise suppression capabilities, thereby improving steady-state accuracy.

[0052] (3) Regularization parameters Based on the signal-to-noise ratio (SNR) and interference power ratio ( )Adjustment: ; When the signal-to-noise ratio decreases and interference increases, increase This prevents ill-conditioned covariance matrices and improves numerical stability.

[0053] Furthermore, in step 160, the specific formula for calculating the residual is as follows: residual , ; Standardized residuals: ;in, It is a mean function. This is the standard deviation function.

[0054] like Then remove the data from multiple direction finding angle sequences, and repeat this step until convergence ({ }≤ Or the direction finding angle obtained from two consecutive iterations (The difference is less than 0.1°), or the maximum number of iterations has been reached. (For example, suppose) =10 times).

[0055] By substituting the multiple direction-finding angle sequences after removing the data back into the dynamic direction-finding angle model and recalculating the angle of arrival, the optimized angle of arrival can be obtained. .

[0056] In summary, the algorithm for calculating the angle of arrival to complete direction finding includes: Initialization, setting the control parameters of the antenna array: initial baseline configuration number Signal acquisition frequency ; Set adaptive filter parameters: filter order Forgetting factor =0.99, regularization parameter =0.001; Real-time acquisition of incoming wave signals; environmental assessment model outputs signal-to-noise ratio (SNR) and multipath delay spread in real time. Interference power ratio This triggers environmental grading. Based on the dynamic parameter optimization rules, the parameters are adjusted as follows: Under optimal conditions, the control parameters of the antenna array remain unchanged, the direction finding mode adopts the single-baseline direction finding mode, and the accuracy weight parameters and adaptive filter parameters are dynamically adjusted in real time according to the environmental parameters; under medium and poor conditions, the control parameters of the antenna array are adjusted to the number of baseline configurations. Signal acquisition frequency The direction finding mode is adjusted to the three-baseline direction finding mode, and the accuracy weight parameters and adaptive filter parameters are dynamically adjusted in real time according to the environmental parameters.

[0057] The environmental parameters, the adjusted control parameters of the antenna array, the adjusted direction finding parameters, and the adjusted adaptive filter parameters are used to perform multiple direction finding operations using the dynamic direction finding angle formula. After obtaining multiple direction finding angle sequences, the dynamic direction finding error is substituted into the dynamic direction finding error, and the incoming wave angle is calculated using the dynamic direction finding angle model. Outliers in multiple direction-finding angle sequences are removed by residual calculation. Then, based on the direction-finding angle sequences after outlier removal, an optimized wave arrival angle is obtained using a dynamic direction-finding angle model. .

[0058] In the second embodiment of the present invention, an adaptive rotating baseline antenna array is mounted on a UAV to achieve direction finding in suburban / urban environments, with the antenna baseline length... m, Scene settings include: drone stationary ( The location is open, with no buildings obstructing the view and no interference signals. (m / s)

[0059] Direction finding process: Step 1: Initialization. Set the antenna array control parameters: Configure a single baseline, i.e., only use the AB baseline to acquire the incoming signal, with a signal acquisition frequency of 1kHz; Set the filter parameters: order. Forgetting factor =0.99, regularization parameter =0.001; Step 2: Real-time acquisition of spatial signals; the environmental assessment model outputs the signal-to-noise ratio (SNR) and multipath delay spread in real time. Interference power ratio () Triggering environment classification: Superior environment; Step 3: Adaptive Adjustment. The antenna array control parameters remain unchanged; the direction finding parameters are adjusted in real time; the filter parameters are adjusted as follows: ; Step 4: Substitute the environmental parameters, the signal characteristic parameters after adaptive filter, the control parameters of the current antenna array, and the direction finding calculation parameters into the dynamic direction finding error, and use the dynamic direction finding angle model to calculate the wave angle.

[0060] In this embodiment, the measured value of the baseline phase difference within one rotation of baseline AB is as follows: Figure 2 As shown, the calculated dynamic direction finding angle is as follows: Figure 3 As shown, the corresponding dynamic direction finding error is as follows: Figure 4 As shown, the incoming wave angle obtained using the dynamic direction finding angle model is 45.1321°.

[0061] Step 5: Remove outliers from multiple direction-finding angle sequences through residual calculation, and recalculate to obtain the optimized arrival angle. .

[0062] In this embodiment, the initial calculation of the wave arrival angle, and the corresponding residuals and standardized residuals are as follows: Figure 5 , Figure 6 As shown, outliers in the direction-finding angle sequence are removed, and the optimized arrival angle is recalculated. The direction finding angle is 45.1312°, obtained from two iterations. If the difference is less than 0.1°, it is considered convergent. Therefore, the final calculated wave angle in this embodiment is 45.1312°.

[0063] Result: Under optimal conditions, the calculated angle of arrival is compared with the true value, and the absolute direction finding error is ≤0.5°.

[0064] In the third embodiment, the scenario is set as: drone flying at low speed ( (m / s), the site is open with a few buildings blocking the view and some interference signals.

[0065] Direction finding process: Step 1: Initialization. Set the antenna array control parameters: Configure a single baseline, i.e., only use the AB baseline to acquire the incoming signal, with a signal acquisition frequency of 1kHz; Set the filter parameters: order. Forgetting factor =0.99, regularization parameter =0.001; Step 2: Real-time acquisition of spatial signals; the environmental assessment model outputs the signal-to-noise ratio (SNR) and multipath delay spread in real time. Interference power ratio () Triggering environment classification: Medium environment; Step 3: Adaptive Adjustment. The antenna array control parameters are adjusted as follows: configure three baselines, i.e., use baselines AB, BC, and CA to acquire the incoming wave signal, with a signal acquisition frequency of 500Hz; adjust the direction finding parameters in real time; adjust the filter parameters according to environmental parameters as follows: ; Step 4: Substitute the environmental parameters, the signal characteristic parameters after adaptive filter, the control parameters of the current antenna array, and the direction finding calculation parameters into the dynamic direction finding error, and use the dynamic direction finding angle model to calculate the wave angle.

[0066] In this embodiment, the measured value of the baseline phase difference within one rotation of baseline AB is as follows: Figure 7 As shown, the calculated dynamic direction finding angle is as follows: Figure 8 As shown, the corresponding dynamic direction finding error is as follows: Figure 9 As shown, the incoming wave angle obtained using the dynamic direction finding angle model is 45.5801°.

[0067] Step 5: Remove outliers from multiple direction-finding angle sequences through residual calculation, and recalculate to obtain the optimized arrival angle. .

[0068] In this embodiment, the initial calculation of the wave arrival angle, and the corresponding residuals and standardized residuals are as follows: Figure 10 , Figure 11 As shown, outliers in the direction-finding angle sequence are removed, and the optimized arrival angle is recalculated. The direction finding angle is 45.5279°, obtained from two iterations. If the difference is less than 0.1°, it is considered convergent. Therefore, the final calculated wave angle in this embodiment is 45.5279°.

[0069] Result: Under moderate conditions, the absolute direction finding error is ≤1°.

[0070] In the fourth embodiment of the present invention, the scenario is set as: high-speed flight of a drone ( (m / s), surrounded by numerous office buildings, so omnidirectional jamming equipment was activated to interfere.

[0071] Direction finding process: Step 1: Initialization. Set the antenna array control parameters: Configure a single baseline, i.e., only use the AB baseline to acquire the incoming signal, with a signal acquisition frequency of 1kHz; Set the filter parameters: order. Forgetting factor =0.99, regularization parameter =0.001; Step 2: Real-time acquisition of spatial signals; the environmental assessment model outputs the signal-to-noise ratio (SNR) and multipath delay spread in real time. Interference power ratio () Triggering environment classification: Poor environment; Step 3: Adaptive Adjustment. The antenna array control parameters are adjusted as follows: configure three baselines, i.e., use baselines AB, BC, and CA to acquire the incoming wave signal, with a signal acquisition frequency of 100Hz; adjust the direction finding parameters in real time; adjust the filter parameters according to environmental parameters. ; Step 4: Substitute the environmental parameters, the signal characteristic parameters after adaptive filter, the control parameters of the current antenna array, and the direction finding calculation parameters into the dynamic direction finding error, and use the dynamic direction finding angle model to calculate the wave angle.

[0072] In this embodiment, the measured value of the baseline phase difference within one rotation of baseline AB is as follows: Figure 12 As shown, the calculated dynamic direction finding angle is as follows: Figure 13 As shown, the corresponding dynamic direction finding error is as follows: Figure 14 As shown, the incoming wave angle obtained using the dynamic direction finding angle model is 47.1203°.

[0073] Step 5: Remove outliers from multiple direction-finding angle sequences through residual calculation, and recalculate to obtain the optimized arrival angle. .

[0074] In this embodiment, the initial calculation of the wave arrival angle, and the corresponding residuals and standardized residuals are as follows: Figure 15 , Figure 16 As shown, outliers in the direction-finding angle sequence are removed, and the optimized arrival angle is recalculated. The direction finding angle is 47.1193°, obtained from two iterations. If the difference is less than 0.1°, it is considered convergent. Therefore, the final calculated wave angle in this embodiment is 47.1193°.

[0075] Result: Under differential conditions, the absolute direction finding error is ≤2.5°.

[0076] The aforementioned embodiments of the present invention improve the direction finding error of incoming signals in complex dynamic environments through the provided adaptive rotating baseline direction finding method. Specifically, by combining the baseline rotation of the antenna array, the attitude and motion parameters of the mobile platform, and environmental parameters, a dynamic direction finding angle model is constructed. The control parameters of the antenna array, the attitude, velocity, Doppler shift, and environmental interference of the mobile platform are incorporated into the dynamic direction finding angle formula and the calculation of the direction finding error. After fusion, a high-precision angle of arrival is estimated. This process provides a compensation basis for the weighted fusion method and eliminates the interference of the dynamic environment on the phase difference. Based on environmental parameters and environmental levels, dynamic parameter optimization rules are formulated to optimize and adjust the control parameters of the antenna array, the direction finding calculation parameters, and the adaptive filter parameters in real time. This achieves dynamic matching between the baseline direction finding method and environmental conditions, making it more adaptable to direction finding in complex dynamic environments. Finally, residual calculation is used to remove outlier data. Based on the measurement data after removing outliers, the angle of arrival is re-estimated using the dynamic direction finding angle model, further improving the accuracy of the direction finding results.

[0077] In a fifth embodiment of the present invention, an adaptive rotating baseline direction finding device is provided, comprising the following modules: The first module is used to construct a baseline-rotatable antenna array, which is mounted on a mobile platform; the antenna array includes at least three baselines, and continuously acquires incoming wave signals during the rotation period of the baselines; the control parameters of the antenna array are adjustable, including the number of baselines configured and the signal acquisition frequency; The second module is used to establish an environmental assessment model based on a convolutional neural network. It uses a dataset of environmental signals to pre-train and self-test the environmental assessment model to obtain a trained environmental assessment model, which is used to quickly identify and output environmental parameters. It then uses the environmental parameters to design an environmental scoring formula to classify the environment and obtain the environmental level. The third module is used to acquire multiple direction-finding angle sequences and dynamic direction-finding error sequences by utilizing the attitude and motion parameters of the mobile platform, the control parameters of the antenna array, and environmental parameters, and to construct a dynamic direction-finding angle model. The fourth module is used to formulate dynamic parameter optimization rules based on environmental parameters and environmental levels, and to adaptively or dynamically adjust the control parameters, direction finding parameters, and signal processing algorithm parameters of the antenna array; the direction finding parameters include direction finding mode and accuracy weight parameters; the signal processing algorithm parameters include at least adaptive filter parameters. The fifth module is used to calculate the incoming wave angle based on environmental parameters, adjusted antenna array control parameters, adjusted direction finding parameters, and adjusted adaptive filter parameters, using a dynamic direction finding angle model. The sixth module is used to remove outliers from multiple direction-finding angle sequences through residual calculation, and then use the dynamic direction-finding angle model to obtain the optimized wave arrival angle to complete the direction finding.

[0078] In one embodiment, a computer device, which may be a server, is provided. The device includes a processor, memory, a network interface, and a database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores adaptive rotating baseline direction finding data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the adaptive rotating baseline direction finding method.

[0079] Those skilled in the art will understand that the description of the device technical features in the above embodiments does not constitute a limitation on all devices to which the present invention is applied. Specific devices may include more or fewer components, or combinations of certain components, or different component arrangements.

[0080] In another embodiment, a storage medium is provided on which a computer program is stored, which, when executed by a processor, implements the steps of the aforementioned adaptive rotating baseline orientation method.

[0081] In one embodiment, the present invention also provides an information data processing terminal, which includes an adaptive rotating baseline direction finding device.

[0082] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0083] Matters not covered in this invention are common knowledge.

[0084] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the appended claims.

Claims

1. An adaptive rotating baseline direction finding method, characterized in that, include: Step 110: Construct a baseline rotatable antenna array, which is mounted on a mobile platform; The antenna array includes at least three baselines, and the incoming wave signal is continuously acquired during the rotation period of the baselines; the control parameters of the antenna array are adjustable, including the number of baselines and the signal acquisition frequency; Step 120: Establish an environmental assessment model based on a convolutional neural network. Use a dataset of environmental signals to pre-train and self-test the environmental assessment model to obtain a trained environmental assessment model, which is used to quickly identify and output environmental parameters. Use the environmental parameters to design an environmental scoring formula to classify the environment and obtain the environmental level. Step 130: Using the attitude and motion parameters of the mobile platform, the control parameters of the antenna array, and the environmental parameters, obtain multiple direction finding angle sequences and dynamic direction finding error sequences, and construct a dynamic direction finding angle model. Step 140: Based on environmental parameters and environmental level, formulate dynamic parameter optimization rules to adaptively or dynamically adjust the control parameters, direction finding parameters, and signal processing algorithm parameters of the antenna array; the direction finding parameters include direction finding mode and accuracy weight parameters; the signal processing algorithm parameters include at least adaptive filter parameters. Step 150: Based on environmental parameters, adjusted antenna array control parameters, adjusted direction finding parameters, and adjusted adaptive filter parameters, calculate the incoming wave angle using a dynamic direction finding angle model. Step 160: By calculating the residuals, outliers in multiple direction finding angle sequences are eliminated, and the optimized wave arrival angle is obtained again using the dynamic direction finding angle model to complete the direction finding.

2. The adaptive rotating baseline direction finding method according to claim 1, characterized in that, In step 120, the environmental parameters include output signal-to-noise ratio (SNR) and multipath delay spread. and interference power ratio ; The environmental signal dataset includes measured data and simulated data; the measured data is acquired using general-purpose software radio peripherals to collect actual environmental signals in urban and suburban areas, and the SNR is synchronously recorded using a spectrum analyzer and channel sounder. The reference value; the simulation data, through annotation of the real SNR, , It is generated by adding Gaussian noise and impulse interference signals; The environmental scoring formula is given by the following equation: in, This is the result of SNR after parameter normalization. yes The result after parameter normalization. yes The result after parameter normalization. , and These are the weighting coefficients for environmental parameters; The environmental levels are divided into: Excellent environment, if... ∈ (0.7,1]; Medium environment, if ∈ (0.3, 0.7]; Differential environment, if ∈[0,0.3].

3. The adaptive rotating baseline direction finding method according to claim 2, characterized in that, The antenna array has three baselines of equal length. Step 130, the process of constructing the dynamic direction-finding angle model, includes: Step 131: Using the attitude parameters of the mobile platform, calculate the dynamic direction-finding angle formula, which is given by the following formula: in, Indicates the first One baseline, For the first One baseline phase difference, The wavelength of the incoming wave signal. Baseline length It is the angle of arrival, indicating the incident direction of the incoming wave signal. Projection on a plane and The angle between the positive axes, and: ， In the formula, It is the first Baseline direction vectors to The angle between the positive axis and the axis; , , and It is a rotation matrix Elements in the rotation matrix It is given by the following formula: in, , , These are mobile platform bypass axis, axis, The rotation matrix of the axis. , and These are the attitude parameters: yaw angle, pitch angle, and roll angle. Step 132: After transforming the dynamic direction finding angle formula, calculate the result by taking the difference between both sides of the equation. High-precision sensors are used to suppress yaw angle. Pitch angle Roll angle The measurement error is ignored. In and From this, we obtain the dynamic direction finding error reference formula: in, It is a signal wavelength measurement error. It is the baseline Phase difference measurement error; Step 133: Utilize the motion parameters and signal acquisition frequency of the mobile platform. Calculate signal wavelength measurement error based on environmental parameters. and the estimated value of phase difference measurement error : in, It measures the total time. For the speed of light, The frequency of the incoming wave signal. yes Doppler shift, It's the speed of the mobile platform. Is the direction of movement of the mobile platform in Projection on a plane and The angle between the positive axes; This is a proportionality constant, obtained through experimental measurement; It is the signal bandwidth; Step 134, the signal wavelength measurement error Substituting the estimated value of the phase difference measurement error into the dynamic direction finding error reference formula, we take... The dynamic direction finding error is obtained as follows: Step 135, proceed Sub-independent direction finding, solving the dynamic direction finding angle formula to obtain the dynamic direction finding angle sequence { }, dynamic direction finding error sequence { }, ; By fusing the direction finding results from multiple baselines and calculating the weighted average of the dynamic direction finding angle sequence, multiple direction finding angle sequences are obtained. , ,in, It is a precision weighting parameter. , This refers to the number of baseline configurations; By taking a weighted average of multiple direction-finding angle sequences, a dynamic direction-finding angle model is constructed: ， ； in, ; When using single baseline orientation finding mode: , , ;in, It is a precision weighting parameter. It is a term to prevent division by zero; When using the three-baseline orientation finding mode: , , ,in, It is the precision weight parameter.

4. The adaptive rotating baseline direction finding method according to claim 3, characterized in that, In step 140, the dynamic parameter optimization rules include: Antenna array control parameter adaptive adjustment rules: Under optimal conditions, the number of baseline configurations... Signal acquisition frequency 1kHz; Number of baseline configurations in moderate environments. Signal acquisition frequency 500Hz; number of baseline configurations under differential conditions. The signal acquisition frequency is 100Hz; Dynamic adjustment rules for direction finding solution parameters: The direction finding mode is adjusted according to the environmental level: the single baseline direction finding mode is used in excellent environments, and the three baseline direction finding mode is used in medium and poor environments. The precision weighting parameter is dynamically adjusted based on environmental parameters: ， ； ； in, , For normalized multipath delay and interference power ratio; Dynamic adjustment rules for signal processing algorithm parameters should include at least adaptive filter parameter adjustment rules. Filter order Dynamically adjust based on multipath delay spread ,in It is the floor function; Forgetting factor Adjustments are made based on both the signal-to-noise ratio and the interference power ratio. ； Regularization parameters Adjust based on signal-to-noise ratio and interference power ratio: 。 5. The adaptive rotating baseline direction finding method according to claim 4, characterized in that, In step 160, outliers in multiple direction-finding angle sequences are removed through residual calculation, including: Design residual calculation formula: ; like Then remove the data. ,in, It is a mean function. It is a function of standard deviation; Repeat the data removal steps multiple times until the convergence condition of the residual change is met: the residual change satisfies all , Or the direction finding angle obtained from two consecutive iterations. The difference is less than 0.1°, or the maximum number of iterations has been reached. .

6. The adaptive rotating baseline direction finding method according to claim 5, characterized in that, The algorithm for calculating the angle of arrival to complete direction finding includes: Initialization, setting the control parameters of the antenna array: initial baseline configuration number Signal acquisition frequency ; Set adaptive filter parameters: filter order Forgetting factor =0.99, regularization parameter =0.001; Real-time acquisition of incoming wave signals; environmental assessment model outputs signal-to-noise ratio (SNR) and multipath delay spread in real time. Interference power ratio This triggers environmental grading. Based on the dynamic parameter optimization rules, the parameters are adjusted as follows: Under optimal conditions, the control parameters of the antenna array remain unchanged, the direction finding mode adopts the single-baseline direction finding mode, and the accuracy weight parameters and adaptive filter parameters are dynamically adjusted in real time according to the environmental parameters; under medium and poor conditions, the control parameters of the antenna array are adjusted to the number of baseline configurations. Signal acquisition frequency The direction finding mode is adjusted to the three baseline direction finding mode, and the accuracy weight parameters and adaptive filter parameters are dynamically adjusted in real time according to the environmental parameters. Substitute the environmental parameters, the adjusted antenna array control parameters, the adjusted direction finding solution parameters, and the adjusted adaptive filter parameters into the dynamic direction finding error, perform multiple direction finding operations using the dynamic direction finding angle formula, obtain multiple direction finding angle sequences, and then use the dynamic direction finding angle model to calculate the incoming wave angle. Outliers in multiple direction-finding angle sequences are removed by residual calculation. Then, based on the direction-finding angle sequences after outlier removal, an optimized wave arrival angle is obtained using a dynamic direction-finding angle model. .

7. An adaptive rotating baseline direction finding device, characterized in that, The apparatus implements the steps of the adaptive rotating baseline direction finding method as described in any one of claims 1-6, wherein the apparatus comprises the following modules: The first module is used to construct a baseline-rotatable antenna array, which is mounted on a mobile platform; the antenna array includes at least three baselines, and continuously acquires incoming wave signals during the rotation period of the baselines; the control parameters of the antenna array are adjustable, including the number of baselines configured and the signal acquisition frequency; The second module is used to establish an environmental assessment model based on a convolutional neural network. It uses a dataset of environmental signals to pre-train and self-test the environmental assessment model to obtain a trained environmental assessment model, which is used to quickly identify and output environmental parameters. It then uses the environmental parameters to design an environmental scoring formula to classify the environment and obtain the environmental level. The third module is used to acquire multiple direction-finding angle sequences and dynamic direction-finding error sequences by utilizing the attitude and motion parameters of the mobile platform, the control parameters of the antenna array, and environmental parameters, and to construct a dynamic direction-finding angle model. The fourth module is used to formulate dynamic parameter optimization rules based on environmental parameters and environmental levels, and to adaptively or dynamically adjust the control parameters, direction finding parameters, and signal processing algorithm parameters of the antenna array; the direction finding parameters include direction finding mode and accuracy weight parameters; the signal processing algorithm parameters include at least adaptive filter parameters. The fifth module is used to calculate the incoming wave angle based on environmental parameters, adjusted antenna array control parameters, adjusted direction finding parameters, and adjusted adaptive filter parameters, using a dynamic direction finding angle model. The sixth module is used to remove outliers from multiple direction-finding angle sequences through residual calculation, and then use the dynamic direction-finding angle model to obtain the optimized wave arrival angle to complete the direction finding.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the adaptive rotating baseline orientation method as described in any one of claims 1-6.

9. A storage medium for storing computer programs, characterized in that, When the computer program is executed by the processor, it implements the steps of the adaptive rotating baseline orientation method as described in any one of claims 1-6.

10. An information data processing terminal, characterized in that, The information data processing terminal includes the adaptive rotating baseline direction finding device as described in claim 7.

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