Low-altitude helicopter communication navigation signal enhancement method and system based on multiple paths

Abstract

The invention discloses a multi-path-based low-altitude helicopter communication navigation signal enhancement method and system, and the method comprises the steps: carrying out the precise adjustment of a multi-path signal through a phase calibration algorithm according to the phase deviation value of each path, calculating the phase compensation amount through the fast Fourier transform if the phase deviation value exceeds a preset threshold value, and obtaining a multi-path signal; multi-path signal groups with consistent phases are obtained; the method comprises the following steps of: acquiring a multi-path signal group with consistent phase, performing weighted synthesis on each path signal by adopting a beam forming technology, and aiming at a synthesis efficiency improvement requirement, optimizing an energy concentration ratio in a main signal direction by adjusting a weight coefficient to obtain a primary enhanced main signal waveform; and acquiring coordinate output after the navigation precision is improved, aiming at the phase mismatch problem caused by signal scattering characteristics, adjusting phase calibration parameters by monitoring an environment change trend in real time, and generating enhanced signal data adaptive to a complex environment by adopting a dynamic updating mechanism.

Description

Technical Field

[0001] The present invention belongs to the field of information technology, and particularly relates to a method and system for enhancing communication and navigation signals of low-altitude helicopters based on multi-path optimization and multi-path. Background Art

[0002] The technology of enhancing communication and navigation signals of low-altitude helicopters based on multi-path is a crucial research direction in the aviation field, which is directly related to flight safety, mission execution efficiency, and navigation reliability in complex environments. With the increasing demand for low-altitude flight, especially in urban areas and scenes with complex terrain, ensuring signal stability and accuracy has become a key issue that cannot be ignored. Traditional communication and navigation enhancement methods mostly rely on single-path signal optimization or external base station assistance. However, these methods often face significant limitations in the low-altitude environment. Single-path optimization is difficult to adapt to changing terrain and obstacle interference, while insufficient external base station coverage or signal attenuation further weakens its practicality, leading to a decline in communication quality and an increased risk of navigation inaccuracy.

[0003] In this context, the multi-path propagation characteristics have become one of the core factors affecting the signal enhancement of low-altitude helicopters. Due to the reflection and scattering of obstacles such as terrain and buildings, the signal propagation paths are diversified. Traditional methods often regard these multi-path signals as interference sources and suppress them, rather than fully utilizing their potential value. In addition, phase mismatch and low signal synthesis efficiency are also prominent defects in the existing technology. Without precise control of phase adjustment, multi-path signals may not only fail to cooperate but may even exacerbate interference. If the synthesis process cannot effectively integrate the information of each path, it is difficult to enhance the main signal strength. These unsolved technical factors directly lead to unstable communication quality and insufficient navigation accuracy in the low-altitude environment, thus giving rise to the unique problem of how to achieve signal enhancement under complex conditions.

[0004] Therefore, how to accurately adjust the phase and efficiently synthesize multi-path signals through intelligent algorithms to convert the scattered propagation paths into favorable resources for enhancing the main signal has become the key issue for improving the communication and navigation performance of low-altitude helicopters. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method and system for enhancing communication and navigation signals of low-altitude helicopters based on multi-path optimization and multi-path.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for enhancing communication and navigation signals of low-altitude helicopters based on multi-path, comprising:

[0008] Based on the multipath signal data received in the low-altitude helicopter communication and navigation system, an original signal set containing multipath propagation characteristics is obtained;

[0009] For the reflected signals and scattered signals in the original signal set, determine the phase adjustment requirements and potential synthesis contribution degrees for each path;

[0010] According to the phase offset values of each path, precisely adjust the multipath signals through a phase calibration algorithm. If the phase offset value exceeds the preset threshold, calculate the phase compensation amount using the fast Fourier transform to obtain a group of multipath signals with consistent phases;

[0011] Obtain a group of multipath signals with consistent phases, and use beamforming technology to perform weighted synthesis on the signals of each path. For the requirement of improving the synthesis efficiency, optimize the energy concentration degree in the main signal direction by adjusting the weight coefficients to obtain a preliminarily enhanced main signal waveform;

[0012] According to the preliminarily enhanced main signal waveform, obtain the optimized main signal data;

[0013] For the optimized main signal data, calculate the position and direction information through a navigation solution module, combine the obstacle influence factors in the complex environment, and use Kalman filtering technology to fuse the time-delay characteristics of the multipath signals to determine the coordinate output with improved navigation accuracy;

[0014] According to the coordinate output with improved navigation accuracy, obtain the enhanced signal data adapted to the complex environment;

[0015] According to the enhanced signal data, utilize the reflection signal gain in the multipath propagation characteristics, and use an intelligent optimization algorithm to compensate for the problem of low synthesis efficiency. Adjust the signal superposition method by recursively analyzing the contribution degrees of each path to obtain the final main signal enhancement result.

[0016] Preferably, obtain the multipath signal data received in the low-altitude helicopter communication and navigation system, collect the signal intensity, phase information, and propagation time delay of each path through a sensor array, and use a time-domain analysis method to separate the reflected signals and scattered signals caused by terrain and buildings to obtain an original signal set containing multipath propagation characteristics.

[0017] Preferably, for the reflected signals and scattered signals in the original signal set, adopt an adaptive filtering technology to extract the signal reflection characteristics and signal scattering characteristics, separate the phase offset values and amplitude attenuation values of each path from them, and determine the phase adjustment requirements and potential synthesis contribution degrees for each path.

[0018] Preferably, feature parameters are extracted from the preliminarily enhanced main signal waveform, and a signal quality evaluation model is used to judge the enhancement degree of the main signal. If the signal-to-noise ratio after enhancement is lower than the preset standard, the weight distribution in beamforming is adjusted through an iterative optimization algorithm to obtain optimized main signal data.

[0019] Preferably, the coordinate output with improved navigation accuracy is obtained. For the phase mismatch problem caused by signal scattering characteristics, the phase calibration parameters are adjusted by real-time monitoring of the environmental change trend, and a dynamic update mechanism is used to generate enhanced signal data adapted to complex environments.

[0020] The present invention also provides a multi-path-based low-altitude helicopter communication and navigation signal enhancement system, including:

[0021] A first processing module, configured to obtain an original signal set including multi-path propagation characteristics according to the multi-path signal data received in the low-altitude helicopter communication and navigation system;

[0022] A second processing module, configured to determine the phase adjustment requirement and potential synthesis contribution degree of each path for the reflected signal and scattered signal in the original signal set;

[0023] A third processing module, configured to accurately adjust the multi-path signal through a phase calibration algorithm according to the phase offset value of each path. If the phase offset value exceeds the preset threshold, the phase compensation amount is calculated by using fast Fourier transform to obtain a multi-path signal group with consistent phases;

[0024] A fourth processing module, configured to obtain the multi-path signal group with consistent phases, perform weighted synthesis on the signals of each path by using beamforming technology, and optimize the energy concentration degree in the main signal direction by adjusting the weight coefficient according to the requirement of improving synthesis efficiency to obtain a preliminarily enhanced main signal waveform;

[0025] A fifth processing module, configured to obtain optimized main signal data according to the preliminarily enhanced main signal waveform;

[0026] A sixth processing module, configured to calculate the position and direction information through a navigation solution module for the optimized main signal data, combine the obstacle influence factor in the complex environment, and use Kalman filtering technology to fuse the time delay characteristics of the multi-path signal to determine the coordinate output with improved navigation accuracy;

[0027] A seventh processing module, configured to obtain enhanced signal data adapted to complex environments according to the coordinate output with improved navigation accuracy;

[0028] The eighth processing module is used to compensate for the problem of low synthesis efficiency according to the enhanced signal data, utilize the reflection signal gain in the multipath propagation characteristics, and adopt an intelligent optimization algorithm to adjust the signal superposition method by recursively analyzing the contribution degree of each path, so as to obtain the final enhanced result of the main signal.

[0029] Preferably, the first processing module acquires the multipath signal data received in the low-altitude helicopter communication and navigation system, collects the signal intensity, phase information and propagation delay of each path through a sensor array, and uses a time-domain analysis method to separate the reflection signal and scattering signal caused by terrain and buildings, so as to obtain the original signal set containing multipath propagation characteristics.

[0030] Preferably, for the reflection signal and scattering signal in the original signal set, the second processing module adopts an adaptive filtering technique to extract the signal reflection characteristics and signal scattering characteristics, separates the phase offset value and amplitude attenuation value of each path from them, and determines the phase adjustment requirement and potential synthesis contribution degree of each path.

[0031] Preferably, the fifth processing module extracts characteristic parameters from the preliminarily enhanced main signal waveform, uses a signal quality evaluation model to judge the enhancement degree of the main signal. If the signal-to-noise ratio after enhancement is lower than the preset standard, the weight distribution in beamforming is adjusted through an iterative optimization algorithm to obtain the optimized main signal data.

[0032] Preferably, the seventh processing module acquires the coordinate output after the navigation accuracy is improved. For the phase mismatch problem caused by the signal scattering characteristics, the phase calibration parameters are adjusted by real-time monitoring of the environmental change trend, and a dynamic update mechanism is adopted to generate enhanced signal data adapted to complex environments.

[0033] Aiming at the problem of reduced navigation accuracy caused by multipath signal propagation in complex environments, the present invention collects multipath signal data through a sensor array and uses a time-domain analysis method to separate reflection and scattering signals. An adaptive filtering technique is used to extract signal characteristics, perform phase calibration and beamforming, and optimize the energy concentration degree in the main signal direction. Combining with environmental impact factors, a Kalman filter is used to fuse the time-delay characteristics of multipath signals to improve navigation accuracy. The phase calibration parameters are adjusted by real-time monitoring of environmental changes, and an intelligent optimization algorithm is adopted to compensate for the problem of low synthesis efficiency. The present invention effectively improves the signal quality and navigation accuracy of the low-altitude helicopter communication and navigation system in complex environments, and enhances the adaptability and reliability of the system in a multipath interference environment. Description of the Drawings

[0034] Figure 1 It is a flowchart of the method for enhancing the communication and navigation signals of a low-altitude helicopter based on multipath of the present invention. Detailed Embodiments

[0035] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this specification without creative efforts shall fall within the scope of protection of this specification.

[0036] Embodiment 1:

[0037] As Figure 1 shown, an embodiment of the present invention provides a method for enhancing communication and navigation signals of a low-altitude helicopter based on multi-path optimization, including:

[0038] Step S101, obtain multi-path signal data received in a low-altitude helicopter communication and navigation system, collect the signal strength, phase information, and propagation delay of each path through a sensor array, and use a time-domain analysis method to separate the reflected signals and scattered signals caused by terrain and buildings, obtaining an original signal set containing multi-path propagation characteristics.

[0039] Collect multi-path signal data through a sensor array, obtain the signal strength, phase information, and propagation delay of each path, and obtain a first signal set. Use a time-domain analysis method to process the first signal set, separate the reflected signals and scattered signals caused by terrain influence and building influence, and obtain a second signal set. For the reflected signals and scattered signals in the second signal set, obtain the propagation delay and phase information, determine the propagation characteristics of each path signal, and obtain a third signal set. If there is a signal in the third signal set whose propagation delay is greater than a preset threshold, then use the Fourier transform method to decompose the frequency components of the signal, obtaining a fourth signal set. According to the frequency components in the fourth signal set, determine whether there is a low-frequency component caused by terrain influence, obtaining a fifth signal set. Determine the contribution of terrain influence to the propagation characteristics of multi-path signals through the low-frequency components and phase information in the fifth signal set, obtaining a sixth signal set. Generate an original signal set containing multi-path propagation characteristics using the propagation characteristics and signal strength in the sixth signal set.

[0040] Exemplarily, collecting multi-path signal data through a sensor array is a basic step in analyzing signal propagation characteristics in the field of wireless communication.

[0041] Exemplarily, assume that multiple sensor nodes are deployed in an urban environment, and the signal travels from the transmitter to the receiver through different paths, being affected by terrain undulations and building obstructions. The first signal set collected may include direct signals, reflected signals, and scattered signals, and the signal strength, phase information, and propagation delay of each path can be recorded by a high-precision antenna array.

[0042] For example, the signal strength of a certain path is -60 dBm, the phase shift is 30 degrees, and the propagation delay is 50 nanoseconds, while the strength of another path is -75 dBm, the phase shift is 45 degrees, and the delay is 70 nanoseconds. These data constitute the first signal set, reflecting the preliminary characteristics of multipath effects.

[0043] In a possible implementation, a time-domain analysis method is used to process the first signal set to separate the reflected and scattered signals caused by terrain and buildings. Time-domain analysis distinguishes paths by the difference in signal arrival times.

[0044] For example, the time-window technique is used to separate the direct signal with a shorter delay from the reflected signal with a longer delay. Suppose the delay of the reflected signal caused by a high-rise building in the city is 60 nanoseconds, while the delay of the scattered signal from a distant hillside is 80 nanoseconds. The second signal set can be obtained through time-domain filtering. This separation helps subsequent analysis of the specific effects of different paths and improves the accuracy of signal processing. For the reflected and scattered signals in the second signal set, obtaining the propagation delay and phase information is the key to determining the propagation characteristics.

[0045] Specifically, the reflected signal may show a stable phase change, while the scattered signal shows phase randomness due to multi-point diffusion.

[0046] For example, the phase change range of the reflected signal is between 20 and 40 degrees, and the delay is concentrated around 60 nanoseconds; while the phase change range of the scattered signal reaches 60 degrees, and the delay is distributed between 70 and 90 nanoseconds. The third signal set is thus formed, containing the propagation characteristics of each path signal, laying a foundation for further analysis.

[0047] It should be noted that if there is a signal in the third signal set with a propagation delay greater than a preset threshold (such as 100 nanoseconds), Fourier transform is required to decompose the frequency components. Suppose a signal has a delay of 120 nanoseconds. After Fourier transform, it is found that its frequency components include a low-frequency part (0 - 10 Hz) and a high-frequency part (50 - 100 Hz), obtaining the fourth signal set. This decomposition can reveal the frequency characteristics of the signal, especially sensitive to the low-frequency interference caused by the terrain, and helps to identify the environmental impact.

[0048] In one embodiment, it is determined whether there is a low-frequency component caused by the terrain according to the fourth signal set.

[0049] For example, if the low-frequency component dominates (such as the energy ratio reaches 70%), it may be related to the reflection from a distant mountain; if the high-frequency component is more significant, it may be caused by the scattering of nearby buildings. The fifth signal set is thus generated, focusing on the low-frequency characteristics of the terrain impact. This analysis can effectively distinguish different environmental factors and improve the accuracy of signal feature extraction.

[0050] Preferably, the contribution of the terrain to the propagation characteristics of the multipath signal is determined by the low-frequency components and phase information in the fifth signal set.

[0051] For example, the phase shift corresponding to the low-frequency component is small (e.g., 10 degrees), indicating that the terrain reflection path is relatively stable. Therefore, the sixth signal set records the specific characteristics affected by the terrain. This method helps to optimize the signal model and reduce the impact of terrain interference on communication quality.

[0052] For example, the propagation characteristics and signal strength in the sixth signal set are used to generate an original signal set containing multipath propagation characteristics. Assuming that the signal strength of a certain path is -65 dBm, combined with the time delay of 80 nanoseconds and phase shift of 20 degrees caused by the terrain, the propagation path characteristics of the signal can be reconstructed. This reconstruction not only restores the multipath characteristics of the signal but also provides data support for subsequent channel modeling and optimization.

[0053] It can be understood that the advantage of this method is to improve the robustness of signal processing, especially the communication performance in complex terrain environments is significantly improved.

[0054] Step S102: For the reflected signals and scattered signals in the original signal set, an adaptive filtering technique is used to extract the signal reflection characteristics and signal scattering characteristics, separate the phase shift values and amplitude attenuation values of each path, and determine the phase adjustment requirements and potential synthesis contribution degrees of each path.

[0055] The adaptive filtering technique is used to process the reflected signals and scattered signals in the signal set to separate the first reflection characteristic data and the first scattering characteristic data, obtaining a preliminary separation result. Through path separation operations on the first reflection characteristic data and the first scattering characteristic data, the second reflection characteristic data and the second scattering characteristic data corresponding to each path are generated to determine the characteristic distribution of each path. For the second reflection characteristic data and the second scattering characteristic data, the fast Fourier transform algorithm is used to extract the phase shift values of each path, obtaining the first phase shift data set. For the second reflection characteristic data and the second scattering characteristic data, the fast Fourier transform algorithm is used to extract the amplitude attenuation values of each path, obtaining the first amplitude attenuation data set. According to the first phase shift data set, if the phase shift value of a certain path exceeds the preset phase threshold, the phase adjustment requirement is calculated through the arctangent function, obtaining the first phase adjustment requirement data set. According to the first amplitude attenuation data set and the first phase adjustment requirement data set, a weighted calculation method is used to evaluate the synthesis contribution degrees of each path, obtaining the first synthesis contribution degree data set. Through sorting operations on the first synthesis contribution degree data set, the priority order of each path is determined, obtaining the final path optimization result.

[0056] Exemplarily, when using adaptive filtering technology to process reflected signals and scattered signals in a signal set, it can be understood as dynamically adjusting the parameters of the filter to adapt to the changes of signals in different paths.

[0057] For example, in a low-altitude helicopter communication scenario, assuming that the signals received by a sensor array are mixed with reflection and scattering components from the ground and buildings, the adaptive filter can gradually separate the first reflection characteristic data and the first scattering characteristic data according to the real-time characteristics of the signals.

[0058] Exemplarily, for a reflected signal with a signal strength of -50 dBm, the filter may identify that its main frequency components are concentrated in a certain range, thereby separating the corresponding characteristic data. The advantage of this method is that it can quickly adapt to environmental changes and improve the accuracy of separation.

[0059] In a possible implementation, when generating the second reflection characteristic data and the second scattering characteristic data through path separation operations on the first reflection characteristic data and the first scattering characteristic data, the spatial distribution characteristics of the signals can be utilized.

[0060] For example, assuming that the reflected signal of a certain path comes from the ground with a propagation delay of 2 microseconds, while the scattered signal comes from a side building with a delay of 2.5 microseconds, the characteristic distributions of the two can be clearly distinguished through path separation.

[0061] Specifically, this operation helps to more accurately locate the signal source in subsequent analysis. When using the fast Fourier transform algorithm to extract the phase offset value for the second reflection characteristic data and the second scattering characteristic data.

[0062] Preferably, the signal can be decomposed into multiple frequency components.

[0063] For example, after transformation, the reflected signal of a certain path shows a phase offset value of 30 degrees, while the scattered signal has an offset value of 45 degrees, thus constructing the first phase offset data set. The benefit of this method is that it can quantify the phase changes of signals in different paths and provide a basis for subsequent adjustments. Similarly, when extracting the amplitude attenuation value, the fast Fourier transform can reveal the process of the amplitude of a certain path signal decaying from -40 dBm to -45 dBm, forming the first amplitude attenuation data set. According to the first phase offset data set, if the phase offset value of a certain path exceeds a preset threshold, such as set to 40 degrees, then the phase adjustment requirement is calculated through the arctangent function.

[0064] For example, for a signal with an offset value of 45 degrees, its adjustment requirement may be calculated as 5 degrees, generating the first phase adjustment requirement data set. This operation can identify the paths that need to be optimized and ensure the pertinence of signal processing.

[0065] In one embodiment, when evaluating the synthetic contribution degree using a weighted calculation method based on the first amplitude attenuation data set and the first phase adjustment requirement data set, the weight of the phase adjustment requirement can be set to 0.6, and the weight of the amplitude attenuation can be set to 0.4.

[0066] For example, if the phase adjustment requirement of a certain path is 5 degrees and the amplitude attenuation is 5 dBm, its synthetic contribution degree may be calculated as 4.0 and included in the first synthetic contribution degree data set. The advantage of this weighting method is that it comprehensively considers multi-dimensional features and improves the comprehensiveness of the evaluation.

[0067] It should be noted that when determining the priority order of each path by sorting the first synthetic contribution degree data set, the paths with higher contribution degrees can be processed preferentially.

[0068] For example, for three paths with contribution degrees of 4.0, 3.5, and 3.0 respectively, the final optimization result may be to allocate resources mainly based on the path with 4.0. The significance of this sorting operation is to optimize the overall performance of the communication system and ensure the signal quality of key paths.

[0069] Step S103, according to the phase offset values of each path, precisely adjust the multi-path signals through a phase calibration algorithm. If the phase offset value exceeds the preset threshold, calculate the phase compensation amount using the fast Fourier transform to obtain a multi-path signal group with consistent phases.

[0070] By collecting data of each path, obtain the phase offset value and multi-path signal characteristics to determine the initial signal state. If the phase offset value exceeds the preset threshold, calculate the phase compensation amount through the fast Fourier transform to obtain the compensated phase data. Use the phase calibration algorithm to adjust the multi-path signals to obtain the calibrated signal group. Based on the calibrated signal group, judge the phase consistency to obtain the consistency verification result. Through the consistency verification result, adjust the algorithm parameters to determine the optimized calibration model. For the optimized calibration model, obtain the final multi-path signal group to complete the phase consistency processing. Extract the path difference characteristics from the final signal group to obtain the complete signal processing output.

[0071] Exemplarily, for the process of collecting data of each path to obtain the phase offset value and multi-path signal characteristics.

[0072] Exemplarily, multi-path signals in a wireless communication system can be monitored in real time by a high-precision signal acquisition device.

[0073] In one possible implementation, assume that in an indoor wireless communication scenario, the signal propagates through multiple paths from the transmitter to the receiver, and the acquisition device records the signal arrival time and intensity of each path.

[0074] Preferably, for a certain path, if the time difference of arrival causes the phase offset value to reach 60 degrees while the preset threshold is 45 degrees, it indicates that the phase offset of this path exceeds the standard and further processing is required.

[0075] In one embodiment, when calculating the phase compensation amount through fast Fourier transform, the collected time-domain signal can be converted into a frequency-domain signal.

[0076] Specifically, assuming that the phase offset of a path signal shows 70 degrees after frequency-domain analysis while the ideal value is 20 degrees, the compensation amount can be calculated through the frequency-domain phase difference.

[0077] It should be noted that this method can quickly identify the phase components that need to be compensated, ensuring the efficiency of subsequent calibration. When using the phase calibration algorithm to adjust multi-path signals.

[0078] For example, the least mean square error algorithm can be introduced to iteratively optimize the signal group.

[0079] In a possible implementation, the calibration algorithm will adjust the phase distribution of the signal according to the phase data of each path to make it as close to the ideal value as possible. Assuming that after calibration, the phase deviation of the signal group is reduced from the initial 30 degrees to within 5 degrees, the consistency verification stage can be entered at this time. Regarding the judgment of the consistency verification result.

[0080] Preferably, a consistency standard can be set, for example, requiring that the phase deviation of all paths does not exceed 10 degrees. Assuming that in a certain verification, it is found that the deviation of one path is still 15 degrees, then the parameters of the calibration algorithm need to be adjusted, such as increasing the number of iterations or adjusting the weighting coefficient.

[0081] It can be understood that this dynamic adjustment can improve the adaptability of the model and ensure the stability of signal processing. When obtaining the final multi-path signal group based on the optimized calibration model.

[0082] For example, the performance of the model can be verified through simulation. Assuming that the phase deviation of each path in the final signal group is controlled within 3 degrees, it indicates that the phase consistency processing is completed. This high consistency helps to improve the anti-interference ability of the communication system. When extracting the path difference characteristics from the final signal group.

[0083] Specifically, the signal strength and time delay differences of each path can be analyzed.

[0084] For example, the signal strength of a certain path decays by 3 dB and the time delay is 2 ns, while another path decays by 5 dB and the time delay is 4 ns. These characteristics can be used for subsequent signal optimization. This detailed feature extraction helps to more comprehensively understand the signal propagation environment.

[0085] Step S104, obtain a multipath signal group with consistent phases, and use beamforming technology to perform weighted synthesis on the signals of each path. For the requirement of improving synthesis efficiency, optimize the energy concentration degree in the main signal direction by adjusting the weight coefficients to obtain a preliminarily enhanced main signal waveform.

[0086] Obtain a multipath signal group, use phase detection technology to judge the phase consistency of the signals of each path, and obtain a signal set with consistent phases. Extract the path signals from the signal set with consistent phases, and use beamforming technology to perform weighted synthesis on the path signals to obtain an initial synthesized signal. For the initial synthesized signal, obtain the main signal direction, and determine the directivity of the main signal by calculating the direction angle. According to the directivity of the main signal, adjust the weight coefficients, and use the gradient descent algorithm to optimize the energy concentration degree to obtain optimized weight coefficients. Use the optimized weight coefficients to perform weighted processing on the initial synthesized signal to obtain an enhanced main signal waveform. Extract features from the enhanced main signal waveform, use Fourier transform to analyze the waveform frequency distribution, and judge the improvement degree of the synthesis efficiency. According to the frequency distribution result, obtain the change trend of the energy concentration degree, and determine the final main signal waveform by comparing the changes before and after.

[0087] Exemplarily, after obtaining the multipath signal group, when using phase detection technology to judge the phase consistency of the signals of each path, it can be achieved by measuring the phase differences of the signals of each path. Assume that in a communication system, there are three path signals, and their phase values are 30 degrees, 45 degrees, and 60 degrees respectively, and the preset phase consistency threshold is 15 degrees. Through phase detection technology, the calculated phase differences are 15 degrees and 30 degrees respectively. Obviously, the phase differences of some path signals exceed the threshold. At this time, the signals with phase differences within the threshold can be classified into the consistent signal set. For example, a signal subset with a phase difference less than 15 degrees can be selected for subsequent processing. This method can effectively identify the signal set with consistent phases and lay a foundation for subsequent beamforming.

[0088] In a possible implementation manner, after extracting the path signals from the signal set with consistent phases, use beamforming technology to perform weighted synthesis on the path signals.

[0089] For example, in a base station antenna array, the signal set contains three path signals, corresponding to different receiving angles respectively. Through beamforming technology, initial weight coefficients are assigned, such as 0.4, 0.3, and 0.3, and the signals are weighted and then synthesized into an initial synthesized signal. This weighted synthesis can preliminarily converge the signal energy and provide support for the subsequent determination of the main signal direction.

[0090] Specifically, when obtaining the main signal direction for the initial synthesized signal, it can be achieved by calculating the direction angle.

[0091] For example, by using the geometric characteristics of the array antenna, the angle of arrival of the signal is measured. Assuming that the calculated direction angle of the main signal is 60 degrees. This means that the energy of the main signal is mainly concentrated in the 60-degree direction, providing a direction basis for subsequent weight adjustment.

[0092] Preferably, when adjusting the weight coefficient according to the directivity of the main signal, the gradient descent algorithm is used to optimize the energy concentration.

[0093] For example, the initial weights are 0.4, 0.3, and 0.3. By iterative calculation, the weights are gradually adjusted to make the energy more concentrated in the 60-degree direction, and finally the optimized weight coefficients are obtained, such as 0.5, 0.3, and 0.2. This adjustment method can significantly improve the directivity of the signal and enhance the strength of the main signal.

[0094] In one embodiment, after weighting the initial synthesized signal with the optimized weight coefficient, an enhanced main signal waveform is obtained.

[0095] For example, after the weighting process, the amplitude of the main signal waveform increases from the initial 5 units to 8 units, indicating that the signal strength has been enhanced. This enhancement provides a clearer data basis for the feature extraction of the main signal.

[0096] For example, when extracting features from the enhanced main signal waveform, Fourier transform is used to analyze the waveform frequency distribution. Assuming that the analysis results show that the frequency distribution of the main signal is concentrated in the range of 2 kHz to 5 kHz, and the energy ratio increases from the initial 60% to 80%, indicating that the synthesis efficiency has been improved. This frequency distribution analysis can intuitively reflect the optimization degree of the signal.

[0097] It can be understood that when obtaining the change trend of the energy concentration according to the frequency distribution result, the final main signal waveform can be determined by comparing the front and back data.

[0098] For example, if the energy concentration of the initial signal is 50% and it is increased to 75% after optimization, it means that the optimized main signal waveform is more suitable for the actual application scenario. This comparison before and after can provide a reference basis for subsequent system adjustment.

[0099] Step S105, extract feature parameters from the preliminarily enhanced main signal waveform, use the signal quality evaluation model to judge the enhancement degree of the main signal. If the signal-to-noise ratio after enhancement is lower than the preset standard, adjust the weight distribution in the beamforming through the iterative optimization algorithm to obtain the optimized main signal data.

[0100] Extract feature parameters from the preliminarily enhanced main signal waveform, calculate time-domain and frequency-domain features through time-frequency analysis methods, and obtain a set of feature parameters of the main signal waveform. Process the set of feature parameters using a signal quality assessment model, calculate the signal-to-noise ratio corresponding to the enhancement degree of the main signal, determine whether the signal-to-noise ratio is lower than a preset threshold, and obtain a preliminary judgment result. If the preliminary judgment result indicates that the signal-to-noise ratio is lower than the preset threshold, adjust the weight distribution in beamforming through an iterative optimization algorithm to obtain the adjusted weight distribution data. Perform beamforming processing on the main signal waveform according to the adjusted weight distribution data to generate optimized main signal data. Extract a new set of feature parameters from the optimized main signal data, and use the signal quality assessment model to recalculate the signal-to-noise ratio to obtain the optimized signal-to-noise ratio. If the optimized signal-to-noise ratio is still lower than the preset threshold, repeat the iterative optimization algorithm to adjust the weight distribution until the signal-to-noise ratio reaches the preset threshold to determine the final main signal data. Generate a main signal data file that meets the requirements of subsequent processing by storing and format-converting the final main signal data.

[0101] Exemplarily, when extracting feature parameters from the preliminarily enhanced main signal waveform, the time-frequency analysis method is crucial.

[0102] For example, the time-domain amplitude variation and frequency-domain energy distribution of the waveform can be obtained through short-time Fourier transform.

[0103] Exemplarily, in a communication system, the main signal waveform may contain a main frequency component of 2 kHz. Through time-frequency analysis, the trend of amplitude decay over time and the concentration degree of frequency distribution can be obtained, forming a set of feature parameters including information such as amplitude, frequency, and phase shift.

[0104] In a possible implementation, the signal quality assessment model can process these feature parameters based on signal-to-noise ratio calculation.

[0105] Specifically, assuming that the signal power in the initial waveform is 10 dBm and the noise power is -20 dBm, the calculated signal-to-noise ratio is 30 dB. If the preset threshold is 35 dB, the preliminary judgment result shows that the signal-to-noise ratio is insufficient.

[0106] It should be noted that this evaluation not only reflects the enhancement degree but also provides a basis for subsequent optimization. If the signal-to-noise ratio is lower than the threshold, it is particularly important to adjust the beamforming weight distribution through the iterative optimization algorithm.

[0107] For example, in a multi-antenna system, the initial weights may be evenly distributed as 0.25, but through gradient descent iteration, they may be adjusted to a distribution of 0.4, 0.3, 0.2, 0.1.

[0108] Preferably, this adjustment can make the energy in the direction of the main signal more concentrated.

[0109] It is understandable that the adjusted weight distribution data will directly affect the directivity of beamforming. When generating optimized primary signal data based on the adjusted weight distribution, the beamforming process will resynthesize the signal.

[0110] In one embodiment, assuming that the energy of the primary signal direction after adjustment is increased from 60% to 80%, the new set of characteristic parameters shows a narrower frequency distribution, indicating an improvement in signal quality. After recalculating the signal-to-noise ratio, if the result rises from 30 dB to 36 dB and exceeds the threshold, the optimization is completed. This iterative process ensures the stability and reliability of the signal. When extracting a new set of characteristic parameters from the optimized primary signal data, the effect can be further verified.

[0111] For example, the offset of the main frequency component may be reduced from 50 Hz to 20 Hz, indicating that the waveform is closer to the ideal state. The signal quality assessment model recalculates the signal-to-noise ratio here to ensure that the result meets expectations. If it still does not meet the standard, repeat the iteration until the requirements are met, reflecting the robustness of the method. After the final primary signal data is determined, storage and format conversion are necessary steps.

[0112] Specifically, the data can be saved in WAV format with a sampling rate of 44.1 kHz for convenient subsequent analysis.

[0113] Preferably, this format conversion can also be compatible with a variety of processing tools, improving the versatility of the data.

[0114] For example, a 10-second signal file may occupy 1 MB of space, which not only saves storage but also facilitates transmission.

[0115] In one embodiment, the entire process from feature extraction to data storage forms a closed-loop optimization system.

[0116] It is understandable that this method not only improves the clarity of the primary signal but also lays a foundation for the improvement of the overall system performance.

[0117] Exemplarily, in wireless communication, this optimization can significantly reduce the bit error rate and improve the transmission efficiency, having high practical value.

[0118] Step S106, for the optimized primary signal data, calculate the position and direction information through the navigation solution module, combine the obstacle influence factor in the complex environment, and adopt the Kalman filtering technology to fuse the time-delay characteristics of multipath signals to determine the coordinate output with improved navigation accuracy.

[0119] Obtain the optimized main signal data through the signal processing module, fuse the signal optimization results, and obtain the preliminary processed data. Use the navigation solution module to process the preliminary processed data, combine the position information and direction information to determine the initial navigation result. Extract the environmental complexity features from the initial navigation result, fuse the obstacle factor, and obtain the environmental adjustment parameter. If the environmental adjustment parameter exceeds the preset threshold, then fuse the multipath signal and time delay characteristics through Kalman filtering to determine the filtered navigation data. Adjust the navigation accuracy according to the filtered navigation data, fuse the position information and direction information to obtain the accuracy improvement result. Generate the final coordinate output through the accuracy improvement result, combine the direction information and position information to determine the navigation end value. Extract the navigation accuracy index from the navigation end value, and judge the optimization effect by comparing with the initial navigation result.

[0120] Exemplarily, when obtaining the optimized main signal data through the signal processing module and fusing the signal optimization results to obtain the preliminary processed data.

[0121] It can be understood that the core of the signal processing module lies in the feature extraction and optimized fusion of the main signal data. For example.

[0122] In a possible implementation manner, the signal processing module first performs time-frequency analysis on the main signal data, extracts the power spectral density and spectral distribution characteristics of the signal, and then integrates the signal optimization results from different sources through a weighted fusion algorithm to generate the preliminary processed data. This method can effectively improve the stability and consistency of the data and provide reliable input for subsequent navigation solution. When using the navigation solution module to process the preliminary processed data and combine the position information and direction information to determine the initial navigation result, the navigation solution module usually completes the calculation based on multi-source data fusion.

[0123] Exemplarily, assume that the preliminary processed data includes the received signal strength and phase information. The navigation solution module calculates the initial navigation result through triangulation combined with the position information (such as longitude and latitude coordinates) and direction information (such as yaw angle). This method can initially determine the position and movement trajectory of the target and provide a basis for subsequent environmental adjustment. In the process of extracting the environmental complexity features from the initial navigation result and fusing the obstacle factor to obtain the environmental adjustment parameter, the environmental complexity features are usually related to the complexity of the signal propagation path.

[0124] Specifically, if the initial navigation result shows that there are multiple reflections or occlusions in the signal propagation path, the extracted environmental complexity feature value will be higher.

[0125] In one embodiment, assuming that the range of the environmental complexity eigenvalue is from 0 to 1, the obstacle factor is calculated by the sensor detecting the density of surrounding objects (for example, if the number of obstacles is 5, the factor value is 0.5), and the two are multiplied to obtain the environmental adjustment parameter. If the parameter value is 0.6, which is higher than the preset threshold of 0.4, it indicates that the environmental interference is large and further optimization is required. If the environmental adjustment parameter exceeds the preset threshold, the Kalman filter is used to fuse the multi-path signals and the time-delay characteristics to determine the filtered navigation data.

[0126] Preferably, the Kalman filter corrects the time-delay characteristics of the multi-path signals through iterative prediction and update steps.

[0127] For example, assuming that the multi-path signal causes a time-delay error of 20 milliseconds, the Kalman filter gradually reduces the error to within 5 milliseconds by fusing historical data and current measurements, thereby generating more accurate filtered navigation data and improving the reliability of subsequent processing. When adjusting the navigation accuracy according to the filtered navigation data and fusing the position information and the direction information to obtain the accuracy improvement result, it can be further optimized through weighted fusion. For example.

[0128] In a possible implementation, if the error range of the filtered navigation data is ±2 meters, by combining the high-precision position information (such as the ±0.5-meter error provided by differential GPS) and the direction information (such as the ±1-degree error measured by the gyroscope), the accuracy improvement result is obtained through weighted calculation, and the error range is reduced to ±1 meter. When generating the final coordinate output through the accuracy improvement result and combining the direction information and the position information to determine the navigation end value, the final coordinate output is usually expressed in a standard format.

[0129] Exemplarily, if the accuracy improvement result shows that the coordinates are 120 degrees east longitude and 30 degrees north latitude, and the direction information is 45 degrees north, the navigation end value can be represented as a complete vector including the position and the direction, providing intuitive data for subsequent applications. When extracting the navigation accuracy index from the navigation end value and judging the optimization effect by comparing it with the initial navigation result, the navigation accuracy index can include the position deviation and the direction deviation.

[0130] In one embodiment, if the position deviation of the initial navigation result is 5 meters and the direction deviation is 3 degrees, while the deviations of the navigation end value are reduced to 1 meter and 0.5 degree respectively, it indicates that the optimization effect is significant. This comparative analysis helps to verify the effectiveness of the entire process.

[0131] Step S107, obtain the coordinate output with improved navigation accuracy. For the phase mismatch problem caused by the signal scattering characteristics, adjust the phase calibration parameters by real-time monitoring the environmental change trend, and adopt a dynamic update mechanism to generate enhanced signal data adapted to the complex environment.

[0132] Collect environmental change data through sensors. For factors such as temperature, humidity, and obstacle distribution, calculate the environmental change trend to obtain an environmental change trend vector. Use the environmental change trend vector to analyze the scattering characteristics in the signal propagation path. If the scattering intensity exceeds a preset threshold, calculate the phase mismatch amount caused by scattering through a geometric optical model to obtain phase mismatch data. According to the phase mismatch data, use the least squares method to optimize the phase calibration parameters. For regions with a large mismatch amount, adjust the calibration parameter weights to determine a set of calibration parameters. Through a dynamic update mechanism, combine the set of calibration parameters to adjust the phase offset value of the signal transmitter in real time to generate enhanced signal data adapted to a complex environment. Obtain the enhanced signal data, use the Kalman filter algorithm to smooth the signal, determine whether the signal stability reaches a preset standard to obtain the smoothed enhanced signal. For the smoothed enhanced signal, combine the multipath effect model at the receiving end to calculate the signal propagation delay deviation, determine the improvement amplitude of the navigation accuracy, and output the corrected coordinate data. Use the corrected coordinate data to further optimize the position solution by triangulation, determine whether there are coordinate points with too large deviations. If so, re-iterate the correction to obtain the final high-precision coordinates.

[0133] Exemplarily, collecting environmental change data through sensors is the basis for obtaining real-time dynamic information in a navigation system.

[0134] For example, in an urban environment, sensors can detect that the temperature rises from 20 degrees to 30 degrees, the humidity increases from 60% to 80%, and identify the distribution of obstacles such as buildings ahead. This data collection can provide the original basis for subsequent analysis.

[0135] In a possible implementation, a sensor network is deployed around the navigation device, and the environmental change trend vector is calculated through multi-point sampling.

[0136] Exemplarily, the trend vector may show that the change in temperature causes a change in the refractive index of the air, affecting the signal propagation speed. The environmental change trend vector plays a key role in the analysis of scattering characteristics.

[0137] Specifically, if the scattering intensity exceeds a preset threshold, such as the signal reflection intensity reaching twice the normal value, further processing is required.

[0138] It can be understood that scattering is usually caused by obstacles such as high-rise buildings or trees.

[0139] In one embodiment, with the help of a geometric optical model, calculate the phase mismatch amount caused by scattering.

[0140] For example, assuming that the signal path is blocked by a building, the model can deduce that the phase shift is about 30 degrees. The generation of this phase mismatch data helps to identify the specific source of signal distortion.

[0141] Preferably, when optimizing the phase calibration parameters using the least squares method, the weights can be adjusted according to the magnitude of the mismatch.

[0142] For example, in areas with a large mismatch, such as dense building areas, the calibration parameter weight can be increased to 1.5 times to enhance the calibration effect.

[0143] In a possible implementation, the set of calibration parameters is formed through multiple iterations to ensure coverage of the phase adjustment requirements under different environmental conditions. This can significantly improve the stability of the signal. The dynamic update mechanism is the core of real-time adjustment of the phase offset of the signal transmitter.

[0144] For example, when the phase offset value is adjusted from 5 degrees to 10 degrees, the enhanced signal data can better adapt to complex environments.

[0145] It should be noted that this adjustment relies on the real-time feedback of the set of calibration parameters.

[0146] In one embodiment, after the enhanced signal data is generated, the Kalman filtering algorithm can reduce its noise level from 10% to 2%, improving the signal smoothness. This smoothing process ensures that the signal stability reaches the preset standard. Calculating the signal propagation delay deviation is a key step for the multipath effect model.

[0147] For example, when the signal delay detected at the receiving end increases from 2 milliseconds to 5 milliseconds, it can be inferred that the navigation accuracy is improved by approximately 10 meters.

[0148] Specifically, the application of triangulation further optimizes the position solution.

[0149] For example, by the intersection of signals from three base stations, it is determined whether the coordinate point deviation exceeds 50 meters. If it exceeds, the correction is iterated again. This method can effectively reduce errors and ensure high-precision coordinate output.

[0150] In one embodiment, iterative correction may find that the deviation of a certain coordinate point reaches 100 meters. By adjusting the transmitter parameters and filtering strategy, the final deviation is reduced to 5 meters. This multi-faceted optimization process reflects the complete logical chain from data acquisition to final output, and can significantly improve the reliability and adaptability of the navigation system.

[0151] Step S108, according to the enhanced signal data, using the reflection signal gain in the multipath propagation characteristics, compensating for the problem of low synthesis efficiency by adopting an intelligent optimization algorithm, adjusting the signal superposition method by recursively analyzing the contribution degree of each path, and obtaining the final enhanced result of the main signal.

[0152] Through the multipath propagation characteristic, enhanced signal data is obtained from the receiving end. Filtering processing is used to separate each path signal component, and a preliminary signal set is obtained. According to the preliminary signal set, the recursive analysis method is used to calculate the contribution degree of each path, determine the gain influence of each path on the main signal, and obtain a path contribution degree list. If the path contribution degree is lower than the preset threshold, the corresponding path signal component is removed from the path contribution degree list, and an updated signal set is obtained. For the updated signal set, the genetic algorithm is used to adjust the signal superposition method, optimize the phase and amplitude of each path signal, and obtain an optimized signal set. Through the optimized signal set, the weighted superposition method is used to calculate the composite signal, and it is judged whether the synthesis efficiency reaches the preset standard to obtain a preliminary main signal. According to the preliminary main signal, the mean smoothing method is used to process the signal to eliminate the noise that may be introduced during the superposition process, and a smoothed main signal is obtained. Through the smoothed main signal, its frequency domain characteristics are analyzed by Fourier transform, and it is judged whether there is spectral distortion to obtain the final main signal enhancement result.

[0153] Exemplarily, when obtaining enhanced signal data from the receiving end through the multipath propagation characteristic.

[0154] It can be understood that when a signal propagates in a complex environment, multiple path components will be generated due to reflection, refraction, etc.

[0155] Exemplarily, in an urban environment, the signal may form multiple paths due to the reflection of high-rise buildings, and the enhanced signal data captured by the receiving end contains these components. When using filtering processing to separate each path signal component, a possible implementation method is to use a band-pass filter to separate signal components in different frequency ranges.

[0156] For example, assuming that the frequency of a certain path signal is concentrated around 5 kHz, it can be extracted through a filter to obtain a preliminary signal set. When calculating the contribution degree of each path according to the preliminary signal set by using the recursive analysis method.

[0157] It should be noted that the recursive analysis judges the influence on the main signal by successively comparing the intensity and delay of each path signal.

[0158] Specifically, if the intensity of a certain path signal is only 10% of the main signal due to being blocked by an obstacle, its contribution degree is relatively low.

[0159] In one embodiment, the contribution degree threshold is set to 20%, and path signals lower than this value will be removed.

[0160] For example, if the contribution degrees of three paths are 30%, 15%, and 10% respectively, then only the signal of the first path is retained in the updated signal set. When using the genetic algorithm to adjust the signal superposition method for the updated signal set.

[0161] Preferably, the natural selection process will be simulated to optimize the phase and amplitude.

[0162] It can be understood that phase adjustment can reduce the interference between paths.

[0163] For example, assuming that the phase difference between the two-path signals is 90 degrees, it is adjusted to be close to 0 degrees through the genetic algorithm, and the superposition effect is better, obtaining an optimized signal set. When calculating the composite signal using the weighted superposition method through the optimized signal set.

[0164] For example, weights can be assigned according to the path contribution degree. The weight of the path signal with a high contribution degree is set to 0.7, and the low one is set to 0.3. In this way, the synthesis efficiency is higher and the preliminary main signal is more stable.

[0165] In one possible implementation, if the synthesis efficiency reaches more than 85%, it is considered to meet the preset standard. This method can effectively improve the clarity of the signal. When processing according to the preliminary main signal using the mean smoothing method.

[0166] Specifically, by taking the average value of the previous and next 5 sampling points, random noise is eliminated.

[0167] For example, if the signal values at a certain point are 10, 12, 11, 9, 13, it may become 11 after smoothing. This way can make the smoothed main signal more stable and reduce mutations. When analyzing the frequency domain characteristics using the Fourier transform through the smoothed main signal.

[0168] In one embodiment, the signal is decomposed into a spectrogram to check whether there are abnormal peaks.

[0169] For example, if an unexpected peak of 2 kHz appears in the spectrum, it may indicate spectrum distortion, and the foregoing steps need to be further adjusted. Therefore, the final main signal enhancement result is more reliable and suitable for high-precision applications such as navigation. From multiple aspects, the above method progresses step by step through links such as separation, optimization, and smoothing to ensure signal quality.

[0170] For example, filtering separation lays the foundation, the genetic algorithm optimizes the phase and amplitude to improve the superposition effect, and mean smoothing and Fourier analysis further ensure stability. These steps support each other and jointly improve the usability of the main signal, especially suitable for signal processing requirements in complex environments.

[0171] Embodiment 2:

[0172] The embodiment of the present invention also provides a multi-path based low-altitude helicopter communication navigation signal enhancement system, including:

[0173] The first processing module is used to obtain an original signal set containing multi-path propagation characteristics according to the multi-path signal data received in the low-altitude helicopter communication navigation system;

[0174] The second processing module is used to determine the phase adjustment requirements and potential synthesis contribution degrees of each path for the reflected signals and scattered signals in the original signal set;

[0175] The third processing module is used to accurately adjust the multipath signals through a phase calibration algorithm according to the phase offset values of each path. If the phase offset value exceeds a preset threshold, the phase compensation amount is calculated using the fast Fourier transform to obtain a group of multipath signals with consistent phases;

[0176] The fourth processing module is used to obtain a group of multipath signals with consistent phases, perform weighted synthesis on the signals of each path using beamforming technology, and optimize the energy concentration degree in the main signal direction by adjusting the weight coefficients according to the requirement of improving the synthesis efficiency to obtain a preliminarily enhanced main signal waveform;

[0177] The fifth processing module is used to obtain optimized main signal data according to the preliminarily enhanced main signal waveform;

[0178] The sixth processing module is used to calculate the position and direction information through the navigation solution module for the optimized main signal data, combine the obstacle influence factors in the complex environment, and use the Kalman filtering technology to fuse the time-delay characteristics of the multipath signals to determine the coordinate output with improved navigation accuracy;

[0179] The seventh processing module is used to obtain enhanced signal data adapted to the complex environment according to the coordinate output with improved navigation accuracy;

[0180] The eighth processing module is used to compensate for the problem of low synthesis efficiency according to the enhanced signal data by using the reflection signal gain in the multipath propagation characteristics, and adjust the signal superposition method by recursively analyzing the contribution degrees of each path to obtain the final main signal enhancement result.

[0181] As an implementation manner of an embodiment of the present invention, the first processing module obtains the multipath signal data received in the low-altitude helicopter communication and navigation system, collects the signal intensity, phase information, and propagation time delay of each path through a sensor array, and uses a time-domain analysis method to separate the reflected signals and scattered signals caused by terrain and buildings to obtain an original signal set containing multipath propagation characteristics.

[0182] As an implementation manner of an embodiment of the present invention, the second processing module uses an adaptive filtering technology for the reflected signals and scattered signals in the original signal set to extract the signal reflection characteristics and signal scattering characteristics, separates the phase offset values and amplitude attenuation values of each path therefrom, and determines the phase adjustment requirements and potential synthesis contribution degrees of each path.

[0183] As an implementation manner of an embodiment of the present invention, the fifth processing module extracts characteristic parameters from the preliminarily enhanced main signal waveform, and uses a signal quality evaluation model to judge the enhancement degree of the main signal. If the signal-to-noise ratio after enhancement is lower than a preset standard, the weight distribution in beamforming is adjusted through an iterative optimization algorithm to obtain optimized main signal data.

[0184] As an implementation manner of an embodiment of the present invention, the seventh processing module obtains the coordinate output after the navigation accuracy is improved. For the phase mismatch problem caused by the signal scattering characteristics, the phase calibration parameters are adjusted by real-time monitoring of the environmental change trend, and a dynamic update mechanism is used to generate enhanced signal data adapted to the complex environment.

[0185] As described above, it is only the specific implementation manner of this specification. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, modules, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this specification is not limited thereto. Any person skilled in the art within the technical scope disclosed in this specification can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of this specification.

Claims

1. A multi-path based method for enhancing communication and navigation signals of low-altitude helicopters, characterized in that, Including: Obtain an original signal set containing multipath propagation characteristics based on the multipath signal data received in the low-altitude helicopter communication and navigation system; For the reflected signals and scattered signals in the original signal set, determine the phase adjustment requirements and potential synthesis contribution degrees of each path; According to the phase offset values of each path, precisely adjust the multipath signals through a phase calibration algorithm. If the phase offset value exceeds a preset threshold, calculate the phase compensation amount using the fast Fourier transform to obtain a group of multipath signals with consistent phases; Obtain the group of multipath signals with consistent phases, use beamforming technology to perform weighted synthesis on the signals of each path, and for the requirement of improving the synthesis efficiency, optimize the energy concentration degree in the main signal direction by adjusting the weight coefficients to obtain a preliminarily enhanced main signal waveform; Based on the preliminarily enhanced main signal waveform, obtain the optimized main signal data; For the optimized main signal data, calculate the position and direction information through a navigation solution module, combine the obstacle influence factors in the complex environment, and use the Kalman filtering technology to fuse the time-delay characteristics of the multipath signals to determine the coordinate output with improved navigation accuracy; Based on the coordinate output with improved navigation accuracy, obtain the enhanced signal data adapted to the complex environment; According to the enhanced signal data, utilize the reflection signal gain in the multipath propagation characteristics, use an intelligent optimization algorithm to compensate for the problem of low synthesis efficiency, and adjust the signal superposition method by recursively analyzing the contribution degrees of each path to obtain the final main signal enhancement result.

2. The method for enhancing communication and navigation signals of a low-altitude helicopter based on multi-path as claimed in claim 1, wherein, Obtain the multipath signal data received in the low-altitude helicopter communication and navigation system, collect the signal intensity, phase information, and propagation time delay of each path through a sensor array, and use a time-domain analysis method to separate the reflected signals and scattered signals caused by terrain and buildings to obtain an original signal set containing multipath propagation characteristics.

3. The method for enhancing communication and navigation signals of a low-altitude helicopter based on multi-path as claimed in claim 2, wherein For the reflected signals and scattered signals in the original signal set, use an adaptive filtering technology to extract the signal reflection characteristics and signal scattering characteristics, separate the phase offset values and amplitude attenuation values of each path from them, and determine the phase adjustment requirements and potential synthesis contribution degrees of each path.

4. The multipath-based low-altitude helicopter communication and navigation signal enhancement method according to claim 3, characterized in that Extract characteristic parameters from the preliminarily enhanced main signal waveform, use a signal quality evaluation model to judge the enhancement degree of the main signal. If the signal-to-noise ratio after enhancement is lower than the preset standard, adjust the weight distribution in the beamforming through an iterative optimization algorithm to obtain the optimized main signal data.

5. The method for enhancing communication and navigation signals of a low-altitude helicopter based on multi-path as claimed in claim 4, wherein Obtain the coordinate output with improved navigation accuracy. For the phase mismatch problem caused by the signal scattering characteristics, adjust the phase calibration parameters by real-time monitoring of the environmental change trend, and use a dynamic update mechanism to generate the enhanced signal data adapted to the complex environment.

6. A multi-path based low-altitude helicopter communication and navigation signal enhancement system, characterized in that, Including: A first processing module for obtaining an original signal set containing multipath propagation characteristics based on the multipath signal data received in the low-altitude helicopter communication and navigation system; A second processing module for determining the phase adjustment requirements and potential synthesis contribution degrees of each path for the reflected signals and scattered signals in the original signal set; The third processing module is used to precisely adjust the multipath signals through a phase calibration algorithm according to the phase offset values of each path. If the phase offset value exceeds a preset threshold, the fast Fourier transform is used to calculate the phase compensation amount, and a multipath signal group with consistent phases is obtained. The fourth processing module is used to obtain the multipath signal group with consistent phases, perform weighted synthesis on the signals of each path using beamforming technology, and optimize the energy concentration degree in the main signal direction by adjusting the weight coefficients to meet the requirement of improving the synthesis efficiency, thereby obtaining a preliminarily enhanced main signal waveform. The fifth processing module is used to obtain optimized main signal data based on the preliminarily enhanced main signal waveform. The sixth processing module is used to calculate the position and direction information through the navigation solution module for the optimized main signal data, combine the obstacle influence factors in the complex environment, and use the Kalman filtering technology to fuse the time-delay characteristics of the multipath signals to determine the coordinate output with improved navigation accuracy. The seventh processing module is used to obtain enhanced signal data adapted to the complex environment based on the coordinate output with improved navigation accuracy. The eighth processing module is used to compensate for the problem of low synthesis efficiency by using the reflection signal gain in the multipath propagation characteristics according to the enhanced signal data, adjust the signal superposition method by recursively analyzing the contribution degree of each path using an intelligent optimization algorithm, and obtain the final main signal enhancement result.

7. The multi-path based low-altitude helicopter communication and navigation signal enhancement system according to claim 8, characterized in that, The first processing module acquires the multipath signal data received in the low-altitude helicopter communication navigation system, collects the signal intensity, phase information, and propagation delay of each path through a sensor array, and uses a time-domain analysis method to separate the reflected signals and scattered signals caused by terrain and buildings, obtaining an original signal set containing multipath propagation characteristics.

8. The multi-path based low-altitude helicopter communication and navigation signal enhancement system according to claim 7, characterized in that, The second processing module uses an adaptive filtering technology for the reflected signals and scattered signals in the original signal set to extract the signal reflection characteristics and signal scattering characteristics, separates the phase offset values and amplitude attenuation values of each path from them, and determines the phase adjustment requirements and potential synthesis contribution degrees of each path.

9. The multi-path based low-altitude helicopter communication and navigation signal enhancement system according to claim 8, characterized in that, The fifth processing module extracts characteristic parameters from the preliminarily enhanced main signal waveform, uses a signal quality evaluation model to judge the enhancement degree of the main signal. If the signal-to-noise ratio after enhancement is lower than the preset standard, the weight distribution in beamforming is adjusted through an iterative optimization algorithm to obtain optimized main signal data.

10. The multi-path-based low-altitude helicopter communication and navigation signal enhancement system according to claim 9, characterized in that, The seventh processing module acquires the coordinate output with improved navigation accuracy, and for the phase mismatch problem caused by the signal scattering characteristics, adjusts the phase calibration parameters by real-time monitoring the environmental change trend, and uses a dynamic update mechanism to generate enhanced signal data adapted to the complex environment.

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