Multi-path-based method and system for enhancing communication and navigation signals of low-altitude helicopters

Abstract

This invention discloses a method and system for enhancing communication and navigation signals for low-altitude helicopters based on multipath propagation. The method includes: precisely adjusting the multipath signals using a phase calibration algorithm based on the phase offset values ​​of each path; if the phase offset value exceeds a preset threshold, calculating the phase compensation amount using fast Fourier transform to obtain a multipath signal group with consistent phase; acquiring the phase-consistent multipath signal group, using beamforming technology to weightedly synthesize the signals from each path, and optimizing the energy concentration of the main signal direction by adjusting the weighting coefficients to improve synthesis efficiency, thus obtaining a preliminarily enhanced main signal waveform; acquiring the coordinate output after improved navigation accuracy; addressing the phase mismatch problem caused by signal scattering characteristics by adjusting the phase calibration parameters through real-time monitoring of environmental changes, and using a dynamic update mechanism to generate enhanced signal data adapted to complex environments.

Description

Technical Field

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

[0002] Multipath-based low-altitude helicopter communication and navigation signal enhancement technology is a crucial research direction in the aviation field, directly impacting flight safety, mission efficiency, and navigation reliability in complex environments. With the increasing demand for low-altitude flight, especially in urbanized areas and complex terrain scenarios, ensuring signal stability and accuracy has become a critical issue. Traditional communication and navigation enhancement methods often rely on single-path signal optimization or external base station assistance; however, these methods often face significant limitations in low-altitude environments. Single-path optimization struggles to adapt to varying terrain and obstacle interference, while insufficient external base station coverage or signal attenuation further weakens its practicality, leading to decreased communication quality and increased risks of navigation inaccuracies.

[0003] Against this backdrop, multipath propagation characteristics have become one of the core factors affecting signal enhancement for low-altitude helicopters. Due to reflection and scattering from obstacles such as terrain and buildings, signal propagation paths are diverse. Traditional methods often treat these multipath signals as interference sources and suppress them, failing to fully utilize their potential value. Furthermore, phase mismatch and low signal synthesis efficiency are also prominent shortcomings in existing technologies. Without precise control of phase adjustment, multipath signals not only fail to coordinate but may also exacerbate interference; if the synthesis process cannot effectively integrate information from each path, it is difficult to improve the strength of the main signal. These unresolved technical factors directly lead to unstable communication quality and insufficient navigation accuracy in low-altitude environments, thus giving rise to the unique challenge of achieving signal enhancement under complex conditions.

[0004] Therefore, how to use intelligent algorithms to accurately adjust the phase and efficiently synthesize multipath signals, so as to transform the dispersed propagation paths into a valuable resource to enhance the main signal, has become a key issue in 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 low-altitude helicopter communication and navigation signals based on multi-path optimization.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

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

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

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

[0010] Based on the phase offset value of each path, the multipath signal is precisely adjusted by the phase calibration algorithm. If the phase offset value exceeds the preset threshold, the phase compensation amount is calculated by fast Fourier transform to obtain a multipath signal group with consistent phase.

[0011] A multipath signal group with consistent phase is obtained, and beamforming technology is used to weight and synthesize the signals of each path. To improve the synthesis efficiency, the energy concentration of the main signal direction is optimized by adjusting the weighting coefficients to obtain the preliminarily enhanced main signal waveform.

[0012] Based on the initially enhanced main signal waveform, the optimized main signal data is obtained;

[0013] For the optimized main signal data, the position and direction information are calculated by the navigation solution module. Combined with the obstacle influence factor in the complex environment, Kalman filtering technology is used to fuse the time delay characteristics of multipath signals to determine the coordinate output after the navigation accuracy is improved.

[0014] Based on the improved coordinate output after enhanced navigation accuracy, enhanced signal data adapted to complex environments is obtained;

[0015] Based on the enhanced signal data, the reflection signal gain in the multipath propagation characteristics is utilized, and an intelligent optimization algorithm is employed to compensate for the low synthesis efficiency. By recursively analyzing the contribution of each path and adjusting the signal superposition method, the final main signal enhancement result is obtained.

[0016] As a preferred approach, multipath signal data received by the low-altitude helicopter communication and navigation system is acquired. The signal strength, phase information, and propagation delay of each path are collected through a sensor array. The reflected and scattered signals caused by terrain and buildings are separated by time-domain analysis to obtain the original signal set containing multipath propagation characteristics.

[0017] As a preferred approach, adaptive filtering techniques are used to extract the signal reflection and scattering characteristics from the reflected and scattered signals in the original signal set. From this, the phase offset and amplitude attenuation values ​​of each path are separated, and the phase adjustment requirements and potential synthesis contribution of each path are determined.

[0018] As a preferred approach, feature parameters are extracted from the initially enhanced main signal waveform, and a signal quality assessment model is used to determine the degree of main signal enhancement. If the enhanced signal-to-noise ratio 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.

[0019] As a preferred approach, the coordinate output after improving navigation accuracy is obtained. To address the phase mismatch problem caused by signal scattering characteristics, the phase calibration parameters are adjusted by real-time monitoring of environmental change trends, and an enhanced signal data adapted to complex environments is generated using a dynamic update mechanism.

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

[0021] The first processing module is used to obtain a set of original signals containing multipath propagation characteristics based on the multipath signal data received from the low-altitude helicopter communication and navigation system.

[0022] The second processing module is used to determine the phase adjustment requirements and potential synthesis contribution of each path for the reflected and scattered signals in the original signal set.

[0023] The third processing module is used to precisely adjust the multipath signal according to the phase offset value of each path through a phase calibration algorithm. If the phase offset value exceeds the preset threshold, the phase compensation amount is calculated using fast Fourier transform to obtain a multipath signal group with consistent phase.

[0024] The fourth processing module is used to acquire multipath signal groups with consistent phase, and uses beamforming technology to weight and synthesize the signals of each path. In order to improve the synthesis efficiency, the energy concentration of the main signal direction is optimized by adjusting the weighting coefficients to obtain the preliminarily enhanced main signal waveform.

[0025] The fifth processing module is used to obtain optimized main signal data based on the initially enhanced main signal waveform;

[0026] The sixth processing module is used to calculate the position and direction information of the optimized main signal data through the navigation solution module, combine the obstacle influence factors in the complex environment, use Kalman filtering technology to fuse the time delay characteristics of multipath signals, and determine the coordinate output after the navigation accuracy is improved.

[0027] The seventh processing module is used to obtain enhanced signal data adapted to complex environments based on the coordinate output after the navigation accuracy is improved;

[0028] The eighth processing module is used to compensate for the low synthesis efficiency by using the reflection signal gain in the multipath propagation characteristics based on the enhanced signal data and employing an intelligent optimization algorithm. It adjusts the signal superposition method by recursively analyzing the contribution of each path to obtain the final main signal enhancement result.

[0029] Preferably, the first processing module acquires multipath signal data received from the low-altitude helicopter communication and navigation system, collects the signal strength, phase information and propagation delay of each path through the sensor array, and uses time-domain analysis to separate the reflected and scattered signals caused by terrain and buildings, thus obtaining the original signal set containing multipath propagation characteristics.

[0030] Preferably, the second processing module uses adaptive filtering technology to extract the signal reflection characteristics and signal scattering characteristics from the reflected and scattered signals in the original signal set, and separates the phase offset value and amplitude attenuation value of each path to determine the phase adjustment requirement and potential synthesis contribution of each path.

[0031] Preferably, the fifth processing module extracts feature parameters from the initially enhanced main signal waveform, uses a signal quality assessment model to determine the degree of main signal enhancement, and if the enhanced signal-to-noise ratio is lower than the preset standard, it adjusts the weight distribution in beamforming through an iterative optimization algorithm to obtain optimized main signal data.

[0032] As a preferred option, the seventh processing module acquires the coordinate output after the navigation accuracy is improved. To address the phase mismatch problem caused by signal scattering characteristics, it adjusts the phase calibration parameters by monitoring the environmental change trend in real time and uses a dynamic update mechanism to generate enhanced signal data that adapts to complex environments.

[0033] This invention addresses the navigation accuracy degradation caused by multipath signal propagation in complex environments. It acquires multipath signal data using a sensor array and employs time-domain analysis to separate reflected and scattered signals. Adaptive filtering technology is used to extract signal characteristics, perform phase calibration and beamforming, and optimize the energy concentration in the main signal direction. Combining environmental influence factors, Kalman filtering is used to fuse multipath signal delay characteristics, improving navigation accuracy. Phase calibration parameters are adjusted in real-time by monitoring environmental changes, and an intelligent optimization algorithm compensates for low synthesis efficiency. This invention effectively improves the signal quality and navigation accuracy of low-altitude helicopter communication and navigation systems in complex environments, enhancing the system's adaptability and reliability in multipath interference environments. Attached Figure Description

[0034] Figure 1 This is a flowchart of the low-altitude helicopter communication and navigation signal enhancement method based on multipath according to the present invention. Detailed Implementation

[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 with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0036] Example 1:

[0037] like Figure 1 As shown, this embodiment of the invention provides a multi-path-based method for enhancing low-altitude helicopter communication and navigation signals, comprising:

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

[0039] Multipath signal data is acquired using a sensor array to obtain signal strength, phase information, and propagation delay for each path, resulting in a first signal set. Time-domain analysis is used to process this first signal set, separating reflected and scattered signals caused by terrain and building influences, resulting in a second signal set. Propagation delay and phase information are obtained for the reflected and scattered signals in the second signal set to determine the propagation characteristics of each path, resulting in a third signal set. If a signal in the third signal set has a propagation delay greater than a preset threshold, its frequency components are decomposed using Fourier transform, resulting in a fourth signal set. Based on the frequency components in the fourth signal set, the presence of low-frequency components caused by terrain influences is determined, resulting in a fifth signal set. The contribution of terrain influence to the propagation characteristics of the multipath signals is determined using the low-frequency components and phase information in the fifth signal set, resulting in a sixth signal set. The propagation characteristics and signal strength from the sixth signal set are used to generate an original signal set containing the multipath propagation characteristics.

[0040] For example, acquiring multipath signal data through a sensor array is a fundamental step in analyzing signal propagation characteristics in the field of wireless communication.

[0041] For example, suppose multiple sensor nodes are deployed in an urban environment. Signals travel from the transmitter to the receiver via different paths, affected by terrain undulations and building obstructions. The initial set of collected signals may include direct signals, reflected signals, and scattered signals. The signal strength, phase information, and propagation delay of each path can be recorded using a high-precision antenna array.

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

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

[0044] For example, time-windowing techniques can be used to separate direct signals with shorter delays from reflected signals with longer delays. Suppose the reflected signal from a tall building in a city has a delay of 60 nanoseconds, while the scattered signal from a distant hillside has a delay of 80 nanoseconds. Time-domain filtering can yield a second set of signals. This separation helps in subsequent analysis of the specific impact of different paths, improving the accuracy of signal processing. For the reflected and scattered signals in this second set, obtaining propagation delay and phase information is crucial for determining the propagation characteristics.

[0045] Specifically, reflected signals may exhibit stable phase changes, while scattered signals exhibit phase randomness due to multi-point diffusion.

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

[0047] It should be noted that if the third signal set contains signals with a propagation delay greater than a preset threshold (e.g., 100 nanoseconds), then a Fourier transform must be used to decompose the frequency components. Assuming a signal has a delay of 120 nanoseconds, the Fourier transform reveals that its frequency components include low-frequency components (0-10Hz) and high-frequency components (50-100Hz), resulting in a fourth signal set. This decomposition reveals the frequency characteristics of the signal, and is particularly sensitive to low-frequency interference caused by terrain, which helps in identifying environmental influences.

[0048] In one embodiment, the presence of a low-frequency component caused by terrain is determined based on a fourth set of signals.

[0049] For example, if low-frequency components dominate (e.g., energy ratio reaches 70%), it may be related to reflections from distant mountains; if high-frequency components are more prominent, it may be caused by scattering from nearby buildings. A fifth signal set is thus generated, focusing on the low-frequency characteristics of terrain influence. This analysis can effectively distinguish different environmental factors and improve the accuracy of signal feature extraction.

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

[0051] For example, the smaller phase shift (e.g., 10 degrees) corresponding to low-frequency components indicates a more stable terrain reflection path, thus the sixth signal set records the specific characteristics of terrain influence. This method helps optimize the signal model and reduce the impact of terrain interference on communication quality.

[0052] For example, using the propagation characteristics and signal strengths from the sixth signal set, an original signal set containing multipath propagation characteristics can be generated. Assuming a signal strength of -65 dBm for a certain path, combined with a terrain-induced delay of 80 nanoseconds and a phase shift of 20 degrees, 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] Understandably, the advantage of this method lies in improving the robustness of signal processing, especially significantly enhancing communication performance in complex terrain environments.

[0054] Step S102: For the reflected and scattered signals in the original signal set, adaptive filtering technology is used to extract the signal reflection characteristics and signal scattering characteristics, and the phase offset value and amplitude attenuation value of each path are separated from them to determine the phase adjustment requirement and potential synthesis contribution of each path.

[0055] Adaptive filtering techniques are used to process the reflected and scattered signals in the signal set, separating the first reflection characteristic data and the first scattering characteristic data to obtain preliminary separation results. Path separation is then performed on the first reflection and scattering characteristic data to generate second reflection and scattering characteristic data for each path, determining the feature distribution of each path. For the second reflection and scattering characteristic data, a Fast Fourier Transform (FFT) algorithm is used to extract the phase offset values ​​of each path, obtaining the first phase offset dataset. For the second reflection and scattering characteristic data, a FFT algorithm is used to extract the amplitude attenuation values ​​of each path, obtaining the first amplitude attenuation dataset. Based on the first phase offset dataset, if the phase offset value of a certain path exceeds a preset phase threshold, the phase adjustment requirement is calculated using the arctangent function, obtaining the first phase adjustment requirement dataset. Based on the first amplitude attenuation dataset and the first phase adjustment requirement dataset, a weighted calculation method is used to evaluate the synthetic contribution of each path, obtaining the first synthetic contribution dataset. By sorting the first synthetic contribution dataset, the priority order of each path is determined, obtaining the final path optimization result.

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

[0057] For example, in a low-altitude helicopter communication scenario, assuming that the signal received by the sensor array contains 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 signal.

[0058] For example, for a reflected signal with a signal strength of -50dBm, 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 approach is that it can quickly adapt to environmental changes and improve the accuracy of separation.

[0059] In one possible implementation, when generating second reflection characteristic data and second scattering characteristic data by performing path separation operation on the first reflection characteristic data and the first scattering characteristic data, the spatial distribution characteristics of the signal can be utilized.

[0060] For example, suppose the reflected signal of a certain path comes from the ground and has a propagation delay of 2 microseconds, while the scattered signal comes from the side building and has a delay of 2.5 microseconds. The characteristic distribution of the two can be clearly distinguished by path separation.

[0061] Specifically, this operation helps to more accurately locate the signal source in subsequent analysis. When extracting the phase shift value using the Fast Fourier Transform algorithm 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 from a certain path shows a phase shift of 30 degrees, while the scattered signal has a phase shift of 45 degrees, thus constructing the first phase shift dataset. The advantage of this method is its ability to quantify the phase changes of signals from different paths, providing a basis for subsequent adjustments. Similarly, when extracting amplitude attenuation values, the Fast Fourier Transform can reveal the process of a signal amplitude attenuating from -40dBm to -45dBm along a certain path, forming the first amplitude attenuation dataset. Based on the first phase shift dataset, if the phase shift value of a certain path exceeds a preset threshold, such as 40 degrees, the phase adjustment requirement is calculated using the arctangent function.

[0064] For example, a signal with an offset of 45 degrees might have an adjustment requirement calculated as 5 degrees, generating a first phase adjustment requirement dataset. This operation identifies paths that need optimization, ensuring targeted signal processing.

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

[0066] For example, if a path requires a phase adjustment of 5 degrees and an amplitude attenuation of 5 dBm, its composite contribution might be calculated as 4.0 and included in the first composite contribution dataset. The advantage of this weighting method is that it comprehensively considers multi-dimensional features, improving the overall comprehensiveness of the evaluation.

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

[0068] For example, given three paths with contributions of 4.0, 3.5, and 3.0 respectively, the final optimization result might prioritize resource allocation based on the path with contribution of 4.0. The significance of this ranking operation lies in optimizing the overall performance of the communication system and ensuring the signal quality of critical paths.

[0069] Step S103: Based on the phase offset value of each path, the multipath signal is precisely adjusted using a phase calibration algorithm. If the phase offset value exceeds a preset threshold, the phase compensation amount is calculated using a fast Fourier transform to obtain a multipath signal group with consistent phase.

[0070] By collecting data from each path, phase offset values ​​and multipath signal characteristics are obtained to determine the initial signal state. If the phase offset value exceeds a preset threshold, a phase compensation amount is calculated using a Fast Fourier Transform (FFT) to obtain the compensated phase data. A phase calibration algorithm is used to adjust the multipath signals, obtaining a calibrated signal set. Based on the calibrated signal set, phase consistency is assessed to obtain a consistency verification result. Based on the consistency verification result, algorithm parameters are adjusted to determine the optimized calibration model. For the optimized calibration model, the final multipath signal set is obtained, completing the phase consistency processing. Path difference features are extracted from the final signal set to obtain the complete signal processing output.

[0071] For example, this describes the process of acquiring data from each path to obtain phase offset values ​​and multipath signal characteristics.

[0072] For example, multipath signals in a wireless communication system can be monitored in real time using high-precision signal acquisition equipment.

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

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

[0075] In one embodiment, when calculating the phase compensation amount using Fast Fourier Transform, the acquired time-domain signal can be converted into a frequency-domain signal.

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

[0077] It should be noted that this method can quickly identify the phase components that need compensation, ensuring the efficiency of subsequent calibration. This is particularly relevant when using phase calibration algorithms to adjust multipath signals.

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

[0079] In one possible implementation, the calibration algorithm adjusts the phase distribution of the signal based on the phase data of each path, making it as close as possible to the ideal value. Assuming that after calibration, the phase deviation of the signal group decreases from the initial 30 degrees to within 5 degrees, the consistency verification stage can then begin. The consistency verification result is then judged.

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

[0081] Understandably, this dynamic adjustment can improve the model's adaptability and ensure the stability of signal processing. This is applied when obtaining the final multipath signal set based on the optimized calibration model.

[0082] For example, the model's performance can be verified through simulation. Assuming the phase deviation of each path in the final signal group is controlled within 3 degrees, it indicates that phase consistency processing is complete. This high consistency helps improve the anti-interference capability of the communication system. This is used when extracting path difference features from the final signal group.

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

[0084] For example, one path might have a signal strength attenuation of 3 dB and a delay of 2 nanoseconds, while another path might have an attenuation of 5 dB and a delay of 4 nanoseconds. These characteristics can be used for subsequent signal optimization. This detailed feature extraction helps to gain a more comprehensive understanding of the signal propagation environment.

[0085] Step S104: Obtain a multipath signal group with consistent phase, and use beamforming technology to weight and synthesize the signals of each path. To improve the synthesis efficiency, the energy concentration of the main signal direction is optimized by adjusting the weighting coefficients to obtain the preliminarily enhanced main signal waveform.

[0086] A multipath signal group is acquired, and phase detection technology is used to determine the phase consistency of each path signal, resulting in a set of phase-consistent signals. Path signals are extracted from this set, and beamforming technology is used to weight and synthesize them to obtain an initial synthesized signal. For the initial synthesized signal, the direction of the main signal is obtained, and its directivity is determined by calculating the direction angle. Based on the directivity of the main signal, the weighting coefficients are adjusted, and a gradient descent algorithm is used to optimize the energy concentration, resulting in optimized weighting coefficients. The initial synthesized signal is then weighted using these optimized weighting coefficients to obtain an enhanced main signal waveform. Features are extracted from the enhanced main signal waveform, and Fourier transform is used to analyze the waveform frequency distribution to determine the degree of improvement in synthesis efficiency. Based on the frequency distribution results, the trend of energy concentration is obtained, and the final main signal waveform is determined by comparing the changes before and after.

[0087] For example, after acquiring a multipath signal group, the phase consistency of each path signal can be determined using phase detection technology by measuring the phase difference between the paths. Assume a communication system has three path signals with phase values ​​of 30 degrees, 45 degrees, and 60 degrees, and a preset phase consistency threshold of 15 degrees. Using phase detection technology, the calculated phase differences are 15 degrees and 30 degrees, respectively. Clearly, the phase differences of some path signals exceed the threshold. In this case, signals with phase differences within the threshold can be categorized into a consistent signal set, for example, a subset of signals with phase differences less than 15 degrees can be selected for subsequent processing. This method effectively identifies phase-consistent signal sets, laying the foundation for subsequent beamforming.

[0088] In one possible implementation, after extracting the path signal from a set of phase-coherent signals, beamforming technology is used to weight and synthesize the path signal.

[0089] For example, in a base station antenna array, the signal set contains three path signals, each corresponding to a different receiving angle. Using beamforming technology, initial weighting coefficients, such as 0.4, 0.3, and 0.3, are assigned, and the signals are weighted and synthesized into an initial composite signal. This weighted synthesis can initially converge signal energy, providing support for determining the direction of the subsequent main signal.

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

[0091] For example, by utilizing the geometric characteristics of an array antenna to measure the angle of arrival of a signal, we can assume 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 directional basis for subsequent weight adjustments.

[0092] Preferably, when adjusting the weighting coefficients according to the directionality of the main signal, the gradient descent algorithm is used to optimize the energy concentration.

[0093] For example, with initial weights of 0.4, 0.3, and 0.3, the weights are gradually adjusted through iterative calculations to concentrate the energy more in the 60-degree direction, ultimately yielding optimized weight coefficients 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, the enhanced main signal waveform is obtained by weighting the initial synthesized signal with optimized weighting coefficients.

[0095] For example, after weighted processing, the amplitude of the main signal waveform increased from the initial 5 units to 8 units, indicating that the signal strength was enhanced. This enhancement provides a clearer data basis for feature extraction of the main signal.

[0096] For example, when extracting features from the enhanced master signal waveform, Fourier transform analysis is used to analyze the waveform frequency distribution. Assuming the analysis shows that the frequency distribution of the master signal is concentrated in the range of 2kHz to 5kHz, and the energy percentage increases from the initial 60% to 80%, this indicates an improvement in synthesis efficiency. This frequency distribution analysis can intuitively reflect the degree of signal optimization.

[0097] It is understandable that when obtaining the trend of energy concentration change based on the frequency distribution results, the final main signal waveform can be determined by comparing the data before and after.

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

[0099] Step S105: Extract feature parameters from the initially enhanced main signal waveform, use a signal quality assessment model to determine the degree of main signal enhancement, and if the enhanced signal-to-noise ratio is lower than the preset standard, adjust the weight distribution in beamforming through an iterative optimization algorithm to obtain the optimized main signal data.

[0100] Feature parameters are extracted from the initially enhanced main signal waveform. Time-domain and frequency-domain features are calculated using time-frequency analysis methods to obtain a set of feature parameters for the main signal waveform. A signal quality assessment model is used to process this set of feature parameters, calculating the signal-to-noise ratio (SNR) corresponding to the enhancement level of the main signal. The SNR is then checked against a preset threshold to obtain a preliminary judgment result. If the preliminary judgment indicates that the SNR is below the preset threshold, an iterative optimization algorithm is used to adjust the weight distribution in beamforming, obtaining adjusted weight distribution data. Beamforming is then performed on the main signal waveform based on the adjusted weight distribution data to generate optimized main signal data. A new set of feature parameters is extracted from the optimized main signal data, and the SNR is recalculated using the signal quality assessment model to obtain an optimized SNR value. If the optimized SNR is still below the preset threshold, the iterative optimization algorithm is repeated to adjust the weight distribution until the SNR reaches the preset threshold, determining the final main signal data. Finally, the final main signal data is stored and format-converted to generate a main signal data file that meets the requirements of subsequent processing.

[0101] For example, time-frequency analysis methods are crucial when extracting characteristic parameters from the initially enhanced master signal waveform.

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

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

[0104] In one possible implementation, the signal quality assessment model can process these characteristic parameters based on signal-to-noise ratio calculations.

[0105] Specifically, assuming the initial waveform has a signal power of 10dBm and a noise power of -20dBm, the calculated signal-to-noise ratio (SNR) is 30dB. If the preset threshold is 35dB, the initial judgment indicates that the SNR is insufficient.

[0106] It should be noted that this evaluation not only reflects the degree of enhancement but also provides a basis for subsequent optimization. If the signal-to-noise ratio is below the threshold, iterative optimization algorithms become particularly important for adjusting the beamforming weight distribution.

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

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

[0109] Understandably, the adjusted weight distribution data directly affects the directivity of beamforming. When generating optimized master signal data based on the adjusted weight distribution, the beamforming process resynthesizes the signal.

[0110] In one embodiment, assuming the adjusted main signal directional energy increases from 60% to 80%, the new set of feature parameters shows a narrower frequency distribution, indicating improved signal quality. After recalculating the signal-to-noise ratio, if the result increases from 30dB to 36dB, exceeding the threshold, the optimization is complete. This iterative process ensures signal stability and reliability. The effect can be further verified by extracting a new set of feature parameters from the optimized main signal data.

[0111] For example, the offset of the dominant frequency component might decrease from 50Hz to 20Hz, indicating that the waveform is closer to the ideal state. The signal quality assessment model then recalculates the signal-to-noise ratio to ensure that the result meets expectations. If it still does not meet the requirements, the iteration is repeated until the requirements are met, demonstrating the robustness of the method. After the final dominant 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 44.1kHz for easier subsequent analysis.

[0113] Preferably, this format conversion is also compatible with multiple processing tools, improving the universality of the data.

[0114] For example, a 10-second signal file may take up 1MB of space, which saves storage and facilitates transmission.

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

[0116] Understandably, this method not only improves the clarity of the main signal, but also lays the foundation for improving the overall performance of the system.

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

[0118] Step S106: For the optimized main signal data, the position and direction information are calculated by the navigation solution module. Combined with the obstacle influence factor in the complex environment, the Kalman filter technology is used to fuse the time delay characteristics of the multipath signals to determine the coordinate output after the navigation accuracy is improved.

[0119] The signal processing module acquires optimized main signal data and fuses the signal optimization results to obtain preliminary processed data. The navigation calculation module processes this preliminary data, combining position and direction information to determine the initial navigation result. Environmental complexity features are extracted from the initial navigation result, and obstacle factors are fused to obtain environmental adjustment parameters. If the environmental adjustment parameters exceed a preset threshold, Kalman filtering is used to fuse multipath signals and time delay characteristics to determine the filtered navigation data. Navigation accuracy is adjusted based on the filtered navigation data, fusing position and direction information to obtain an accuracy improvement result. The accuracy improvement result generates the final coordinate output, which, combined with direction and position information, determines the final navigation value. Navigation accuracy indicators are extracted from the final navigation value and compared with the initial navigation result to determine the optimization effect.

[0120] For example, when the optimized main signal data is obtained through the signal processing module and the signal optimization results are fused, preliminary processed data is obtained.

[0121] Understandably, the core of the signal processing module lies in the feature extraction and optimized fusion of the main signal data. For example...

[0122] In one possible implementation, the signal processing module first performs time-frequency analysis on the main signal data to extract the power spectral density and spectral distribution characteristics. Then, a weighted fusion algorithm is used to integrate the optimization results from different sources to generate preliminary processed data. This approach effectively improves data stability and consistency, providing reliable input for subsequent navigation calculations. When the navigation calculation module processes the preliminary processed data and combines position and direction information to determine the initial navigation result, the navigation calculation module typically performs calculations based on multi-source data fusion.

[0123] For example, assuming the initial processed data includes received signal strength and phase information, the navigation solution module uses triangulation to combine position information (such as latitude and longitude coordinates) and direction information (such as yaw angle) to calculate the initial navigation result. This method can initially determine the target's position and trajectory, providing a basis for subsequent environmental adjustments. In the process of extracting environmental complexity features from the initial navigation results and fusing obstacle factors to obtain environmental adjustment parameters, the environmental complexity features are typically related to the complexity of the signal propagation path.

[0124] Specifically, if the initial navigation results show that there are multiple reflections or obstructions in the signal propagation path, the extracted environmental complexity feature value will be high.

[0125] In one embodiment, assuming the environmental complexity feature value ranges from 0 to 1, the obstacle factor is calculated by detecting the density of surrounding objects using sensors (e.g., if there are 5 obstacles, the factor value is 0.5). Multiplying the two values ​​yields the environmental adjustment parameter. If the parameter value is 0.6, which is higher than the preset threshold of 0.4, it indicates significant environmental interference, requiring further optimization. If the environmental adjustment parameter exceeds the preset threshold, Kalman filtering is used to fuse multipath signals and time delay characteristics to determine the filtered navigation data.

[0126] Preferably, the Kalman filter corrects the time delay characteristics of the multipath signal through iterative prediction and update steps.

[0127] For example, assuming a multipath signal causes a time delay error of 20 milliseconds, Kalman filtering, by fusing historical data and current measurements, gradually reduces the error to within 5 milliseconds, thereby generating more accurate filtered navigation data and improving the reliability of subsequent processing. When adjusting navigation accuracy based on the filtered navigation data, and fusing position and direction information to obtain improved accuracy, further optimization can be achieved through weighted fusion. For example...

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

[0129] For example, if the accuracy improvement result shows coordinates as 120 degrees east longitude and 30 degrees north latitude, with a direction information of 45 degrees north of east, then the final navigation value can be represented as a complete vector containing both position and direction, providing intuitive data for subsequent applications. Navigation accuracy metrics are extracted from the final navigation value. When judging the optimization effect by comparing it with the initial navigation result, the navigation accuracy metrics can include position deviation and direction deviation.

[0130] In one embodiment, if the initial navigation result has a positional deviation of 5 meters and a direction deviation of 3 degrees, and the final navigation deviation is reduced to 1 meter and 0.5 degrees respectively, it indicates a significant optimization effect. This comparative analysis helps to verify the effectiveness of the entire process.

[0131] Step S107: Obtain the coordinate output after the navigation accuracy is improved. To address the phase mismatch problem caused by signal scattering characteristics, adjust the phase calibration parameters by monitoring the environmental change trend in real time, and use a dynamic update mechanism to generate enhanced signal data that adapts to complex environments.

[0132] Environmental change data is collected by sensors, and environmental change trends are calculated for factors such as temperature, humidity, and obstacle distribution, resulting in an environmental change trend vector. Using this vector, the scattering characteristics in the signal propagation path are analyzed. If the scattering intensity exceeds a preset threshold, the phase mismatch caused by scattering is calculated using a geometric optics model, yielding phase mismatch data. Based on this data, the phase calibration parameters are optimized using the least squares method. For areas with large mismatches, the calibration parameter weights are adjusted to determine the calibration parameter set. Through a dynamic update mechanism, combined with the calibration parameter set, the phase offset value of the signal transmitter is adjusted in real time to generate enhanced signal data adapted to complex environments. The enhanced signal data is then acquired and smoothed using a Kalman filter algorithm. The signal stability is assessed to determine if it meets a preset standard, resulting in a smoothed enhanced signal. For the smoothed enhanced signal, combined with a multipath effect model at the receiver, the signal propagation delay deviation is calculated to determine the improvement in navigation accuracy, outputting corrected coordinate data. Using the corrected coordinate data, triangulation is used to further optimize the position calculation, identifying any coordinate points with excessive deviations. If such points exist, the correction is iterated again to obtain the final high-precision coordinates.

[0133] For example, collecting environmental change data through sensors is the foundation for obtaining real-time dynamic information in navigation systems.

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

[0135] In one possible implementation, a sensor network is deployed around the navigation device to calculate a vector of environmental change trends through multi-point sampling.

[0136] For example, a trend vector might show how rising temperatures cause changes in the air's refractive index, affecting signal propagation speed. Environmental change trend vectors play a crucial role in scattering characteristic analysis.

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

[0138] Understandably, scattering is usually caused by obstacles such as tall buildings or trees.

[0139] In one embodiment, the phase mismatch caused by scattering is calculated using a geometric optics model.

[0140] For example, assuming the signal path is blocked by a building, the model can deduce a phase shift of approximately 30 degrees. The generation of this phase mismatch data helps 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 large mismatches, such as densely built-up areas, the weight of calibration parameters can be increased by up to 1.5 times to enhance the correction effect.

[0143] In one possible implementation, the calibration parameter set is formed through multiple iterations to ensure coverage of phase adjustment requirements under different environmental conditions. This significantly improves signal stability. The dynamic update mechanism is central to the real-time adjustment of the signal transmitter's phase offset.

[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 real-time feedback from the set of calibration parameters.

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

[0147] For example, when the receiver detects that the signal delay has increased from 2 milliseconds to 5 milliseconds, it can be deduced that the navigation accuracy has improved by about 10 meters.

[0148] Specifically, the application of triangulation further optimizes the location calculation.

[0149] For example, by analyzing the signals from three base stations, it can be determined whether the coordinate point deviation exceeds 50 meters; if it does, the correction is iterated again. This method effectively reduces errors and ensures high-precision coordinate output.

[0150] In one embodiment, iterative correction might find a coordinate point with a deviation of up to 100 meters. By adjusting transmitter parameters and filtering strategies, the deviation can eventually be reduced to 5 meters. This multi-faceted optimization process embodies a complete logical chain from data acquisition to final output, significantly improving the reliability and adaptability of the navigation system.

[0151] Step S108: Based on the enhanced signal data, the reflection signal gain in the multipath propagation characteristics is used to compensate for the low synthesis efficiency by employing an intelligent optimization algorithm. The signal superposition method is adjusted by recursively analyzing the contribution of each path to obtain the final main signal enhancement result.

[0152] By leveraging the multipath propagation characteristics, enhanced signal data is acquired from the receiver. Filtering is used to separate the signal components of each path, resulting in a preliminary signal set. Based on this preliminary signal set, a recursive analysis method is used to calculate the contribution of each path, determining the gain impact of each path on the main signal, thus obtaining a path contribution list. If a path contribution is lower than a preset threshold, the corresponding path signal component is removed from the path contribution list, resulting in an updated signal set. For the updated signal set, a genetic algorithm is used to adjust the signal superposition method, optimizing the phase and amplitude of each path signal, resulting in an optimized signal set. Using the optimized signal set, a weighted superposition method is used to calculate the synthesized signal, and the synthesis efficiency is assessed to determine if it meets a preset standard, resulting in a preliminary main signal. Based on the preliminary main signal, a mean smoothing method is used to process the signal, eliminating noise that may be introduced during superposition, resulting in a smoothed main signal. Using the smoothed main signal, Fourier transform is used to analyze its frequency domain characteristics, determining whether spectral distortion exists, and obtaining the final main signal enhancement result.

[0153] For example, when acquiring enhanced signal data from the receiving end through multipath propagation characteristics.

[0154] It is understandable that signals propagating in complex environments will generate multiple path components due to reflection, refraction, and other factors.

[0155] For example, in an urban environment, signals may form multiple paths due to reflections from tall buildings, and the enhanced signal data captured by the receiver contains these components. When using filtering to separate the signal components of each path, one possible implementation is to use a bandpass filter to separate the signal components in different frequency ranges.

[0156] For example, assuming the signal frequency of a certain path is concentrated around 5kHz, it can be extracted using a filter to obtain a preliminary signal set. Based on the preliminary signal set, the contribution of each path is calculated using a recursive analysis method.

[0157] It should be noted that recursive analysis determines the impact on the main signal by successively comparing the strength and delay of signals along each path.

[0158] Specifically, if the intensity of a path signal is reduced to only 10% of the main signal due to obstruction by obstacles, its contribution is relatively low.

[0159] In one embodiment, a contribution threshold of 20% is set, and path signals below this value will be eliminated.

[0160] For example, if the contributions of the three paths are 30%, 15%, and 10% respectively, then the updated signal set only retains the signal from the first path. When adjusting the signal superposition method using a genetic algorithm for the updated signal set...

[0161] Preferably, the phase and amplitude are optimized by simulating the natural selection process.

[0162] Understandably, phase adjustment can reduce interference between paths.

[0163] For example, assuming the phase difference between two path signals is 90 degrees, adjusting it to near 0 degrees using a genetic algorithm results in better superposition, yielding an optimized signal set. This optimized signal set is then used to calculate the synthesized signal using a weighted superposition method.

[0164] For example, weights can be assigned based on the path's contribution, with paths contributing more significantly receiving a weight of 0.7 and those contributing less receiving 0.3. This results in higher synthesis efficiency and a more stable initial main signal.

[0165] In one possible implementation, a synthesis efficiency of 85% or higher is considered to meet the preset standard. This method effectively improves signal clarity. When processing the initial master signal using a mean smoothing method...

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

[0167] For example, a signal value of 10, 12, 11, 9, 13 at a certain point might become 11 after smoothing. This method makes the smoothed main signal more stable and reduces abrupt changes. When analyzing the frequency domain characteristics using Fourier transform after smoothing the main signal...

[0168] In one embodiment, the signal is decomposed into a spectrogram to check for any abnormal peaks.

[0169] For example, the appearance of an unexpected 2kHz peak in the spectrum may indicate spectral distortion, requiring further adjustments to the aforementioned steps. The resulting main signal enhancement is therefore more reliable and suitable for high-precision applications such as navigation. From multiple perspectives, the above method, through steps such as separation, optimization, and smoothing, progressively ensures signal quality.

[0170] For example, filtering lays the foundation, genetic algorithms optimize 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, making it particularly suitable for signal processing needs in complex environments.

[0171] Example 2:

[0172] This invention also provides a multi-path-based low-altitude helicopter communication and navigation signal enhancement system, comprising:

[0173] The first processing module is used to obtain a set of original signals containing multipath propagation characteristics based on the multipath signal data received from the low-altitude helicopter communication and navigation system.

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

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

[0176] The fourth processing module is used to acquire multipath signal groups with consistent phase, and uses beamforming technology to weight and synthesize the signals of each path. In order to improve the synthesis efficiency, the energy concentration of the main signal direction is optimized by adjusting the weighting coefficients to obtain the preliminarily enhanced main signal waveform.

[0177] The fifth processing module is used to obtain optimized main signal data based on the initially enhanced main signal waveform;

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

[0179] The seventh processing module is used to obtain enhanced signal data adapted to complex environments based on the coordinate output after the navigation accuracy is improved;

[0180] The eighth processing module is used to compensate for the low synthesis efficiency by using the reflection signal gain in the multipath propagation characteristics based on the enhanced signal data and employing an intelligent optimization algorithm. It adjusts the signal superposition method by recursively analyzing the contribution of each path to obtain the final main signal enhancement result.

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

[0182] As one embodiment of the present invention, the second processing module uses adaptive filtering technology to extract the signal reflection characteristics and signal scattering characteristics of the reflected and scattered signals in the original signal set, and separates the phase offset value and amplitude attenuation value of each path to determine the phase adjustment requirement and potential synthesis contribution of each path.

[0183] As one embodiment of the present invention, the fifth processing module extracts feature parameters from the initially enhanced main signal waveform, uses a signal quality assessment model to determine the degree of main signal enhancement, and if the enhanced signal-to-noise ratio is lower than a preset standard, it adjusts the weight distribution in beamforming through an iterative optimization algorithm to obtain optimized main signal data.

[0184] As one embodiment of the present invention, the seventh processing module obtains the coordinate output after the navigation accuracy is improved. In response to the phase mismatch problem caused by signal scattering characteristics, the phase calibration parameters are adjusted by real-time monitoring of environmental change trends, and an enhanced signal data adapted to complex environments is generated by adopting a dynamic update mechanism.

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

Claims

1. A method for enhancing low-altitude helicopter communication and navigation signals based on multipath, characterized in that, include: Based on the multipath signal data received from the low-altitude helicopter communication and navigation system, a raw signal set containing multipath propagation characteristics is obtained; For the reflected and scattered signals in the original signal set, determine the phase adjustment requirement and potential synthesis contribution of each path; Based on the phase offset value of each path, the multipath signal is precisely adjusted by a phase calibration algorithm. If the phase offset value exceeds the preset threshold, the phase compensation amount is calculated by fast Fourier transform to obtain a multipath signal group with consistent phase. A multipath signal group with consistent phase is obtained, and beamforming technology is used to weight and synthesize the signals of each path. To improve the synthesis efficiency, the energy concentration of the main signal direction is optimized by adjusting the weighting coefficients to obtain the preliminarily enhanced main signal waveform. Based on the initially enhanced main signal waveform, the optimized main signal data is obtained; For the optimized main signal data, the position and direction information are calculated by the navigation solution module. Combined with the obstacle influence factor in the complex environment, Kalman filtering technology is used to fuse the time delay characteristics of multipath signals to determine the coordinate output after the navigation accuracy is improved. Based on the improved coordinate output after enhanced navigation accuracy, enhanced signal data adapted to complex environments is obtained; Based on the enhanced signal data, the reflection signal gain in the multipath propagation characteristics is utilized, and an intelligent optimization algorithm is employed to compensate for the low synthesis efficiency. By recursively analyzing the contribution of each path and adjusting the signal superposition method, the final main signal enhancement result is obtained.

2. The method for enhancing low-altitude helicopter communication and navigation signals based on multipath as described in claim 1, characterized in that, The system acquires multipath signal data received by the low-altitude helicopter communication and navigation system. It collects the signal strength, phase information and propagation delay of each path through a sensor array. It then uses time-domain analysis to separate the reflected and scattered signals caused by terrain and buildings, thus obtaining the original signal set containing multipath propagation characteristics.

3. The method for enhancing low-altitude helicopter communication and navigation signals based on multipath as described in claim 2, characterized in that, For the reflected and scattered signals in the original signal set, adaptive filtering technology is used to extract the signal reflection characteristics and signal scattering characteristics, and the phase offset value and amplitude attenuation value of each path are separated from them to determine the phase adjustment requirement and potential synthesis contribution of each path.

4. The method for enhancing low-altitude helicopter communication and navigation signals based on multipath as described in claim 3, characterized in that, Feature parameters are extracted from the initially enhanced main signal waveform, and the degree of main signal enhancement is judged by a signal quality assessment model. If the enhanced signal-to-noise ratio is lower than the preset standard, the weight distribution in beamforming is adjusted by an iterative optimization algorithm to obtain the optimized main signal data.

5. The method for enhancing low-altitude helicopter communication and navigation signals based on multipath as described in claim 4, characterized in that, The system acquires coordinate outputs with improved navigation accuracy. To address the phase mismatch issue caused by signal scattering characteristics, it adjusts phase calibration parameters by monitoring environmental changes in real time and uses a dynamic update mechanism to generate enhanced signal data that adapts to complex environments.

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

7. The low-altitude helicopter communication and navigation signal enhancement system based on multipath as described in claim 6, characterized in that, The first processing module acquires multipath signal data received from the low-altitude helicopter communication and navigation system. It collects the signal strength, phase information, and propagation delay of each path through a sensor array, and uses time-domain analysis to separate the reflected and scattered signals caused by terrain and buildings, thus obtaining a raw signal set containing multipath propagation characteristics.

8. The low-altitude helicopter communication and navigation signal enhancement system based on multipath as described in claim 7, characterized in that, The second processing module uses adaptive filtering technology to extract the reflection and scattering characteristics of the reflected and scattered signals in the original signal set, and separates the phase offset and amplitude attenuation values ​​of each path to determine the phase adjustment requirements and potential synthesis contribution of each path.

9. The low-altitude helicopter communication and navigation signal enhancement system based on multipath as described in claim 8, characterized in that, The fifth processing module extracts feature parameters from the initially enhanced main signal waveform, uses a signal quality assessment model to determine the degree of main signal enhancement, and if the enhanced signal-to-noise ratio is lower than the preset standard, it adjusts the weight distribution in beamforming through an iterative optimization algorithm to obtain the optimized main signal data.

10. The low-altitude helicopter communication and navigation signal enhancement system based on multipath as described in claim 9, characterized in that, The seventh processing module acquires the coordinate output after the navigation accuracy is improved. To address the phase mismatch problem caused by signal scattering characteristics, it adjusts the phase calibration parameters by monitoring the environmental change trend in real time and uses a dynamic update mechanism to generate enhanced signal data that adapts to complex environments.

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