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

Abstract

The invention discloses a multi-channel-based low-altitude helicopter communication navigation signal enhancement method and system, and the method comprises the steps: extracting characteristic parameters from a first signal set, carrying out the preliminary enhancement processing of a signal through a Kalman filtering algorithm, and carrying out the superposition of a preset attenuation compensation model for a multipath effect caused by the terrain complexity, obtaining an enhanced second signal set; according to the first geographic data set, utilizing a fast Fourier transform algorithm to analyze topographic complexity features, combining real-time requirements, and adopting a parallel computing framework to perform acceleration processing to obtain a second geographic data set containing the topographic features; and extracting obstacle data from the second geographic data set, generating a preliminary three-dimensional model through a voxel grid method, and for spatial distribution characteristics of the obstacle data, superposing a stereoscopic interpolation algorithm to obtain an optimized first three-dimensional model.

Description

Technical Field

[0001] The present invention belongs to the field of information technology, and in particular relates to a multi-channel based low-altitude helicopter communication and navigation signal enhancement method and system. Background Art

[0002] Technological advances in low-altitude helicopter communications and navigation are crucial for ensuring flight safety and improving mission efficiency. With the opening of low-altitude airspace and the increasing use of helicopters, the reliability and accuracy of communication and navigation systems have become critical issues that cannot be ignored. Ensuring stable signal transmission and accurate data processing, particularly in complex terrain and adverse weather conditions, is crucial for successful flight operations. Research in this area not only advances aviation technology but also provides crucial support for low-altitude traffic management.

[0003] However, current communication and navigation enhancement methods still have significant limitations. Traditional single-channel signal processing technologies often struggle to maintain stable performance in areas with severe signal attenuation or multipath interference, such as mountainous areas and urban canyons. Existing solutions often rely on a single data source and lack the ability to integrate diverse geographic information, resulting in reduced navigation accuracy and even signal loss in dynamic environments. These shortcomings expose the inadequacy of existing technologies in adapting to complex low-altitude environments.

[0004] Specifically, the core challenges faced in this field focus on the optimization of multi-channel signal processing technology and the effective integration of geographic information data. First, multi-channel technology needs to solve the problems of data synchronization and interference suppression between different channels, otherwise the effect of signal enhancement will be greatly reduced. Secondly, the multi-source fusion analysis of geographic information faces the difficult problems of data heterogeneity and high real-time requirements. Especially in areas with weak signals or interference, how to quickly integrate terrain, obstacles and meteorological information to form a reliable three-dimensional model is the key to technological breakthroughs. These unresolved technical factors make it difficult to ensure the reliability of navigation systems in complex environments, which in turn leads to the unique problem of improving the safety of low-altitude flights.

[0005] Therefore, how to achieve real-time fusion and signal enhancement of geographic information data through multi-channel technology to overcome interference and data processing bottlenecks in complex terrain environments has become a key issue in improving the performance of low-altitude helicopter communication and navigation systems. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a multi-channel based low-altitude helicopter communication and navigation signal enhancement method and system.

[0007] To achieve the above object, the present invention adopts the following technical solutions:

[0008] A multi-channel-based method for enhancing communication and navigation signals of low-altitude helicopters, comprising:

[0009] Based on the original signal data collected by multi-channel technology, obtain a first signal set that is synchronized and has low interference;

[0010] Extract characteristic parameters from the first signal set, perform preliminary signal enhancement processing on the signals through the Kalman filtering algorithm, and superimpose a preset attenuation compensation model for the multipath effect caused by terrain complexity to obtain an enhanced second signal set;

[0011] Based on the terrain data, obstacle data, and meteorological information flow in the geographical information source, obtain a first geographical data set that is unified;

[0012] Based on the first geographical data set, obtain a second geographical data set that includes terrain features;

[0013] Extract obstacle data from the second geographical data set, generate a preliminary three-dimensional model through the voxel grid method, and superimpose a stereoscopic interpolation algorithm for the spatial distribution characteristics of the obstacle data to obtain an optimized first three-dimensional model;

[0014] Obtain real-time meteorological information flow to obtain a second three-dimensional model that matches the current environment;

[0015] Based on the second three-dimensional model and the second signal set, use the particle filtering algorithm to achieve the fusion processing of signals and geographical information, and adjust the model parameters to optimize the signal coverage range for the low-altitude navigation requirement to obtain a third signal set with fusion enhancement.

[0016] Preferably, obtain the original signal data collected by multi-channel technology, perform time alignment on the data of each channel through a preset time-domain synchronization algorithm, and use an adaptive filter to suppress the cross-interference between channels for the signal synchronization requirement to obtain a first signal set that is synchronized and has low interference.

[0017] Preferably, obtain the terrain data, obstacle data, and meteorological information flow in the geographical information source, convert the heterogeneous data into a unified format through a data preprocessing module, and use a standardization mapping method for the data heterogeneity problem to obtain a first geographical data set that is unified.

[0018] Preferably, based on the first geographical data set, use the fast Fourier transform algorithm to analyze the terrain complexity characteristics, and combine the real-time requirement to accelerate the processing using a parallel computing framework to obtain a second geographical data set that includes terrain features.

[0019] Preferably, obtain real-time meteorological information flow, calculate the influence of meteorology on signal transmission through a hydrodynamic simulation algorithm, and use a time series prediction method for the dynamic change of the meteorological information flow to obtain a second three-dimensional model that matches the current environment.

[0020] The present invention also provides a multi-channel communication and navigation signal enhancement system for low-altitude helicopters, including:

[0021] A first processing module, configured to obtain a first signal set that is synchronized and has low interference based on the original signal data collected by multi-channel technology;

[0022] A second processing module, configured to extract characteristic parameters from the first signal set, perform preliminary enhancement processing on the signal through the Kalman filtering algorithm, and superimpose a preset attenuation compensation model for the multipath effect caused by terrain complexity to obtain an enhanced second signal set;

[0023] A third processing module, configured to obtain a first consistent geographic data set based on the terrain data, obstacle data, and meteorological information flow in the geographic information source;

[0024] A fourth processing module, configured to obtain a second geographic data set containing terrain features based on the first geographic data set;

[0025] A fifth processing module, configured to extract obstacle data from the second geographic data set, generate a preliminary three-dimensional model through the voxel grid method, and superimpose a stereoscopic interpolation algorithm for the spatial distribution characteristics of the obstacle data to obtain an optimized first three-dimensional model;

[0026] A sixth processing module, configured to obtain real-time meteorological information flow to obtain a second three-dimensional model matching the current environment;

[0027] A seventh processing module, configured to perform fusion processing of the signal and geographic information according to the second three-dimensional model and the second signal set by using the particle filtering algorithm, and adjust the model parameters to optimize the signal coverage range for low-altitude navigation requirements to obtain a fused and enhanced third signal set.

[0028] Preferably, the first processing module obtains the original signal data collected by multi-channel technology, performs time alignment on the data of each channel through a preset time-domain synchronization algorithm, and uses an adaptive filter to suppress cross-interference between channels for the signal synchronization requirement to obtain a first signal set that is synchronized and has low interference.

[0029] Preferably, the third processing module obtains the terrain data, obstacle data, and meteorological information flow in the geographic information source, converts heterogeneous data into a unified format through a data preprocessing module, and uses a standardization mapping method for the data heterogeneity problem to obtain a first consistent geographic data set.

[0030] Preferably, the fourth processing module analyzes the terrain complexity characteristics based on the first geographic data set by using the fast Fourier transform algorithm, and uses a parallel computing framework to accelerate the processing in combination with the real-time requirement to obtain a second geographic data set containing terrain features.

[0031] Preferably, the sixth processing module acquires real-time meteorological information flow, calculates the influence of meteorology on signal transmission through a hydrodynamic simulation algorithm, and adopts a time series prediction method for the dynamic change of the meteorological information flow to obtain a second three-dimensional model matching the current environment.

[0032] The present invention collects original signal data through multi-channel technology, and uses a time-domain synchronization algorithm and an adaptive filter to achieve time alignment between channels and suppress cross-interference. Subsequently, a Kalman filter and an attenuation compensation model are used to enhance the signal to cope with the influence of multipath effects. At the same time, the present invention integrates geographical information such as terrain, obstacles, and meteorology, analyzes terrain features through fast Fourier transform and a parallel computing framework, and generates an optimized three-dimensional model using a voxel grid method and a stereoscopic interpolation algorithm. Finally, a particle filter algorithm is used to implement the fusion processing of the signal and geographical information, and the signal coverage range is optimized according to the low-altitude navigation requirements. The present invention effectively solves the problem of signal transmission interference in complex terrain environments and improves the accuracy and reliability of low-altitude navigation. Description of the Drawings

[0033] Figure 1 It is a flowchart of the method for enhancing low-altitude helicopter communication and navigation signals based on multi-channels according to the present invention. Detailed Embodiments

[0034] To further understand the content of the present invention, the present invention will be described in detail in combination with the drawings and embodiments. The present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention and are not intended to limit the invention. In addition, it should be noted that for the convenience of description, only parts related to the invention are shown in the drawings.

[0035] Embodiment 1:

[0036] As Figure 1 shown, an embodiment of the present invention provides a method for enhancing low-altitude helicopter communication and navigation signals based on multi-channels, including:

[0037] S101. Acquire the original signal data collected by multi-channel technology, perform time alignment on the data of each channel through a preset time-domain synchronization algorithm, and adopt an adaptive filter to suppress cross-interference between channels according to the signal synchronization requirement, so as to obtain a first signal set with synchronization and low interference.

[0038] The original signal data is obtained from the acquisition device through multi-channel technology, and the preset time-domain synchronization algorithm is used to process the data of each channel to obtain a second signal set with time alignment. According to the second signal set, an adaptive filter is used to analyze the data between channels to suppress cross-interference and obtain a first signal set that is synchronized and has low interference. The data of each channel in the first signal set is obtained, and it is judged whether there is residual interference. If there is, the interference area is smoothed by linear interpolation to obtain a third signal set. For the third signal set, fast Fourier transform is used to analyze the frequency-domain characteristics to determine whether the signal synchronization meets the preset threshold, and a fourth signal set with frequency-domain synchronization is obtained. The time-domain characteristics are extracted from the fourth signal set, and the time-domain data is secondarily optimized by an adaptive filter to obtain a fifth signal set. According to the fifth signal set, it is judged whether the phase differences of the data of each channel are consistent. If they are not, the phase is adjusted by a phase correction algorithm to obtain a sixth signal set. Through the sixth signal set, mean filtering is used to smooth the signal to obtain the final seventh signal set.

[0039] Specifically, the original signal data is obtained from the acquisition device through multi-channel technology, aiming to capture signals simultaneously from multiple channels.

[0040] For example, in the field of audio processing, a microphone array may receive sound signals from different directions, and the original data may be multiple time-domain waveforms with a sampling rate of 48 kHz, including noise and delay differences. The preset time-domain synchronization algorithm is used to process the data of each channel, aiming to eliminate time deviation.

[0041] Specifically, the starting point of the signal of each channel can be detected, for example, by using the peak position, and the time offset can be controlled within 1 ms to obtain a second signal set with time alignment. This alignment lays the foundation for subsequent processing and avoids analysis errors caused by time misalignment. According to the second signal set, an adaptive filter is used to suppress cross-interference.

[0042] For example, in a microphone array, a certain channel may be interfered by the sound waves of adjacent channels. The adaptive filter reduces the interference amplitude by about 20 dB based on the least mean square error principle by adjusting the filter coefficients in real time to obtain a first signal set that is synchronized and has low interference.

[0043] Preferably, this method can dynamically adapt to environmental changes and improve the signal purity. After obtaining the data of each channel in the first signal set, the existence of residual interference is judged.

[0044] For example, if there is still periodic noise in a certain channel, its peak value exceeding the normal range can be found through spectrum analysis. At this time, the interference area is smoothed by the linear interpolation method. Assuming the length of the interference segment is 10 ms, the signal transition is natural after interpolation, and the third signal set is obtained. This step effectively reduces the influence of sudden noise on subsequent analysis. For the third signal set, the fast Fourier transform is used to analyze the frequency domain characteristics.

[0045] For example, the time-domain signal is converted to the frequency domain, and the main frequency components in the range of 0 - 5 kHz are observed. If the deviation of the peak positions of the main frequencies of each channel is less than 10 Hz, it is considered that the synchronization meets the threshold, and the fourth signal set is obtained. This process intuitively reflects the frequency domain consistency of the signals and provides a guarantee for high-precision applications. The time-domain characteristics, such as waveform amplitude and period, are extracted from the fourth signal set and optimized twice by an adaptive filter.

[0046] For example, for the area with large amplitude fluctuations, the filter can further weaken the background noise, and the signal-to-noise ratio is increased to 30 dB after optimization, and the fifth signal set is obtained. This step enhances the time-domain stability of the signal. According to the fifth signal set, it is judged whether the phase differences of each channel are consistent.

[0047] For example, if the phase of a certain channel leads by 15 degrees, the phase can be adjusted based on the arctangent function through the phase correction algorithm to reduce the deviation to less than 2 degrees, and the sixth signal set is obtained.

[0048] It should be noted that phase consistency is crucial for multi-channel signal synthesis and directly affects the quality of the final output. Through the sixth signal set, mean filtering is used for smoothing.

[0049] For example, the mean value is calculated with a 5-point sliding window to eliminate minor jitters, and finally the seventh signal set is obtained. This kind of smoothing treatment significantly improves the smoothness and readability of the signal.

[0050] Exemplarily, the above process is particularly useful in the speech enhancement scenario. The original signal may be distorted due to microphone spacing or environmental echoes. After time-domain synchronization, interference suppression, frequency domain analysis, and phase correction, the clarity of the final signal is improved by about 40%, which is suitable for downstream tasks such as speech recognition.

[0051] In a possible implementation, if the number of channels is increased to 8, although the computational complexity of the adaptive filter increases, the interference suppression effect is better, and the signal-to-noise ratio can be increased by another 5 dB.

[0052] It can be understood that each technical means progresses step by step, ensuring the continuous optimization of the signal quality and providing an efficient solution for multi-channel signal processing.

[0053] S102. Extract feature parameters from the first signal set, perform preliminary enhancement processing on the signal through the Kalman filtering algorithm, and superimpose a preset attenuation compensation model for the multipath effect caused by terrain complexity to obtain an enhanced second signal set.

[0054] Obtain time-domain data from the second signal set, perform smoothing processing on the signal using mean filtering to obtain a third signal set. Extract frequency-domain features from the third signal set, and analyze the data through fast Fourier transform to obtain a fourth signal set. For the fourth signal set, if the frequency-domain features deviate from the preset threshold, adjust the abnormal area through linear interpolation to obtain a fifth signal set. According to the fifth signal set, perform secondary optimization on the time-domain data using Kalman filtering to obtain a sixth signal set. Extract phase information from the sixth signal set. If the phase difference exceeds the preset range, perform adjustment through a phase correction algorithm to obtain a seventh signal set. Through the seventh signal set, perform final smoothing processing on the signal using mean filtering to obtain an eighth signal set.

[0055] Specifically, obtain time-domain data from the second signal set and perform smoothing processing on the signal using mean filtering. This process aims to reduce the minor fluctuations in the signal.

[0056] For example, in audio signal processing, the acquisition device may introduce high-frequency noise due to environmental jitter. Mean filtering can effectively weaken such interference by taking the average of adjacent 5 sampling points, making the waveform smoother and suitable for subsequent analysis. Extract frequency-domain features from the third signal set. Fast Fourier transform is commonly used to convert the time-domain signal into a frequency-domain view.

[0057] Specifically, assuming a sampling rate of 44.1 kHz, the spectrum from 0 - 10 kHz can be observed after transformation, and the main frequency peak position and amplitude information are extracted to provide a basis for signal consistency judgment. For the fourth signal set, if the frequency-domain features deviate from the preset threshold, linear interpolation can be used to adjust the abnormal area.

[0058] Exemplarily, if the peak of a certain frequency band exceeds the expected value by 10%, it may be caused by interference between devices. By taking values before and after the abnormal point for smooth transition, such as interpolating an abnormal segment of 20 ms, the naturalness of the spectrum can be restored. This method is simple and efficient, especially suitable for real-time processing scenarios.

[0059] It should be noted that the adjusted signal is more in line with the expected model, facilitating subsequent optimization. According to the fifth signal set, Kalman filtering is used for secondary optimization of the time-domain data.

[0060] In a possible implementation, the signal may be affected by dynamic noise, such as the random fluctuations introduced by wind noise in a microphone array. Kalman filtering gradually corrects the signal trajectory through prediction and update steps, combining historical data and current observations, making it closer to the true value.

[0061] Preferably, this method is particularly effective for non-stationary signals and can improve the stability of data. Phase information is extracted from the sixth signal set. If the phase difference exceeds the preset range, the phase correction algorithm can be used.

[0062] For example, if a certain channel has a phase lead of 20 degrees, the offset can be calculated through the arctangent and adjusted so that the phase difference between each channel is controlled within 5 degrees.

[0063] It can be understood that phase consistency is crucial for the synthesis of multi-channel signals and directly affects the output quality.

[0064] In one embodiment, when processing voice signals, the corrected signals are superimposed more naturally, reducing the sense of reverberation. Through the seventh signal set, mean filtering is used for the final smoothing process.

[0065] For example, calculating the mean with a 7-point sliding window can further eliminate residual jitter and make the waveform more readable.

[0066] Specifically, this processing is particularly useful in visual analysis, and the smoothed signal is more conducive to feature extraction.

[0067] In a possible implementation, if the number of channels increases to 6, mean filtering can still maintain a low computational cost while improving the overall signal quality.

[0068] Exemplarily, this smoothing process lays a foundation for subsequent high-precision applications, especially in scenarios that require long-term stable output. The above process progresses step by step, from time-domain smoothing to frequency-domain adjustment, and then to phase optimization, with each step focusing on improving signal quality.

[0069] For example, in a single scenario of a microphone array, whether it is noise suppression or phase correction, the signal can ultimately be made more suitable for downstream task processing.

[0070] It can be understood that this method has strong adaptability in practical applications, and the technical effects are more solid due to the mutual support between steps.

[0071] S103. Obtain terrain data, obstacle data, and meteorological information flow from the geographical information source, convert the heterogeneous data into a unified format through the data preprocessing module, and adopt a standardized mapping method for the data heterogeneity problem to obtain a unified first geographical data set.

[0072] Obtain topographic data, obstacle data, and meteorological information from a geographic information source. Transform the heterogeneous data through a data preprocessing module, and use a standardized mapping method to obtain a unified first geographic data set. Through the data preprocessing module, extract the spatial distribution characteristics of the topographic data and obstacle data, and use a grid division method to obtain a second geographic data set. According to the spatial distribution characteristics in the second geographic data set, if the height difference between the topographic data and the obstacle data exceeds a preset threshold, adjust the abnormal area through linear interpolation to obtain a third geographic data set. Obtain the dynamic change trend of the meteorological information from the third geographic data set, and use mean filtering to smooth the time series data to obtain a fourth geographic data set. For the fourth geographic data set, if the time series fluctuation of the meteorological information exceeds a preset range, optimize and adjust the data through Kalman filtering to obtain a fifth geographic data set. Through the fifth geographic data set, extract the correlation characteristics between the topographic data and the meteorological information, and use a clustering analysis method to group the data to obtain a sixth geographic data set. According to the grouping results of the sixth geographic data set, judge the matching degree between the obstacle data and the meteorological information. If the matching degree is lower than the preset threshold, adjust it through a data fusion method to obtain a seventh geographic data set.

[0073] Specifically, when obtaining topographic data, obstacle data, and meteorological information from a geographic information source.

[0074] Exemplarily, topographic data such as mountain heights and river courses can be extracted from satellite remote sensing images, obstacle data such as building distributions and road networks can be obtained from urban planning databases, and real-time meteorological information such as rainfall and wind speed can be obtained from meteorological stations simultaneously.

[0075] In a possible implementation, the data preprocessing module will convert these heterogeneous data into a unified format. For example, unify the topographic height unit to meters and the wind speed to meters per second. Through a standardized mapping method, normalize the data range to between 0 and 1 to form a first geographic data set. This helps to eliminate the dimension difference in subsequent analysis. When extracting spatial distribution characteristics through the data preprocessing module.

[0076] Specifically, the slope change in the topographic data and the density distribution of the obstacle data can be used as key indicators. Use a grid division method. For example, divide the area into grid cells of 10 meters by 10 meters, and mark the average height and the number of obstacles in each cell to form a second geographic data set. This division can more precisely reflect the spatial heterogeneity. If the height difference exceeds a preset threshold, such as the height difference between adjacent grids exceeding 50 meters, adjust the abnormal area through linear interpolation.

[0077] For example, at the junction of valleys and ridges, after interpolation, the sudden change in height can be smoothly transitioned to form a third geographical dataset, which helps to reduce data noise interference. When obtaining the dynamic change trend of meteorological information from the third geographical dataset.

[0078] It can be understood that rainfall may show periodic fluctuations over time. Mean filtering is used to smooth the time series data. For example, calculating the average rainfall with a 5-hour window to form a fourth geographical dataset can effectively weaken short-term abnormal fluctuations. If the fluctuation of meteorological information exceeds the preset range, such as the wind speed suddenly increasing from 5 meters per second to 15 meters per second within 1 hour, then it is optimized and adjusted through Kalman filtering. This method combines prediction and observation to generate a more stable fifth geographical dataset and enhance the reliability of the data. When extracting the correlation features between terrain data and meteorological information.

[0079] In one embodiment, the correlation between mountain height and rainfall can be analyzed. For example, for every 100-meter increase in height, the rainfall may increase by 10 millimeters. Using the clustering analysis method, the data is divided into groups such as high-altitude heavy rainfall and low-altitude light rainfall to form a sixth geographical dataset. This grouping can clearly reveal the influence law of terrain on meteorology. When judging the matching degree between obstacle data and meteorological information.

[0080] Preferably, it can be checked whether the building-dense area coincides with the low-wind-speed area. If the matching degree is lower than the preset threshold, such as the correlation coefficient being less than 0.6, then it is adjusted through the data fusion method. For example, the wind speed data and the building height are weighted and fused to form a seventh geographical dataset. This adjustment can improve the coordination between data and provide a more accurate basis for subsequent applications.

[0081] For example, in the mountainous area at the edge of the city, satellite data shows that the height of a certain grid is 300 meters, the number of buildings is 5, and the rainfall is 20 millimeters per hour. After grid division, it is found that the height of the adjacent grid is only 50 meters, and after interpolation adjustment, it is smoothly transitioned to a gradually changing height. After mean filtering the rainfall time series, the fluctuation drops from ±5 millimeters to ±2 millimeters, and Kalman filtering further optimizes the wind speed data to make it more consistent with the terrain distribution. After clustering analysis, the high-altitude group is highly correlated with heavy rainfall, and after fusion adjustment, the wind speed data in the building-dense area is more in line with the actual situation. This multi-step processing can provide more reliable data support for signal analysis in complex terrain areas.

[0082] It should be noted that the application of linear interpolation and Kalman filtering can smooth data anomalies and improve prediction accuracy, while clustering analysis and data fusion enhance the integration ability of multi-source data. These methods support each other in a single scenario to ensure data consistency and practicality.

[0083] S104. According to the first geographic dataset, use the fast Fourier transform algorithm to analyze the terrain complexity characteristics, and combined with the real-time requirement, adopt a parallel computing framework to accelerate the processing to obtain a second geographic dataset containing terrain features.

[0084] Obtain the terrain complexity information from the first geographic dataset, process it using the fast Fourier transform algorithm to obtain a second geographic dataset. Accelerate the processing of the terrain features in the second geographic dataset through a parallel computing framework to obtain a third geographic dataset. For the terrain features in the third geographic dataset, use the clustering analysis method to group the data to obtain a fourth geographic dataset. According to the grouping results of the fourth geographic dataset, extract the relevant features of the terrain complexity and the real-time requirement to obtain a fifth geographic dataset. If the distribution of the terrain features in the fifth geographic dataset exceeds the preset threshold, perform smoothing processing through mean filtering to obtain a sixth geographic dataset. Obtain the matching features of the terrain complexity and the computing framework from the sixth geographic dataset, and adjust them using the data fusion method to obtain a seventh geographic dataset. Through the seventh geographic dataset, judge the correlation degree between the terrain features and the real-time requirement to obtain the final processing result.

[0085] Specifically, when obtaining the terrain complexity information from the first geographic dataset.

[0086] It can be understood that the terrain complexity usually reflects the diversity and change frequency of the surface undulation.

[0087] For example, in a mountainous area scene, the terrain complexity may be manifested as frequent and large-amplitude elevation changes, while in a plain area, it is relatively flat. When using the fast Fourier transform algorithm for processing, its core lies in converting the terrain data from the spatial domain to the frequency domain to extract high- and low-frequency features.

[0088] Exemplarily, in a 1000-meter × 1000-meter area, assuming that the terrain elevation data is sampled every 10 meters, the fast Fourier transform can identify sharp peaks with higher frequencies and gentle slope areas with lower frequencies, thereby generating a second geographic dataset. This method can quickly separate the main fluctuation patterns of the terrain, facilitating subsequent analysis. When accelerating the processing of the second geographic dataset through a parallel computing framework.

[0089] It should be noted that parallel computing can significantly improve the processing efficiency of large-scale data.

[0090] Specifically, on a terrain dataset containing 100,000 sampling points, traditional serial computing may take several minutes, while with the help of a parallel framework, such as GPU-based computing, the processing time can be shortened to several seconds. The resulting third geographical dataset retains the details of the terrain features while improving real-time performance, laying the foundation for subsequent grouping. When using the clustering analysis method for grouping the terrain features in the third geographical dataset, a possible implementation is to classify the terrain features into several categories according to complexity through the K-means algorithm.

[0091] For example, in an area with large terrain undulations, it may be divided into three groups: high complexity, medium complexity, and low complexity. Assume that those with a standard deviation of elevation change greater than 50 meters are classified as high complexity, 20 to 50 meters as medium complexity, and less than 20 meters as low complexity. The resulting fourth geographical dataset can clearly reflect the spatial distribution characteristics of the terrain features. When extracting the relevant features of terrain complexity and real-time requirements according to the grouping results of the fourth geographical dataset.

[0092] In one embodiment, the computing resource requirements of different complexity regions can be analyzed.

[0093] Preferably, high complexity regions may require denser sampling and faster processing speeds.

[0094] For example, the real-time requirement for a high complexity mountainous area may be to complete feature extraction within 1 second, while for a low complexity plain, it may be relaxed to 5 seconds. The fifth geographical dataset can thus reflect the priority differences of different regions. If the distribution of terrain features in the fifth geographical dataset exceeds a preset threshold, such as a standard deviation exceeding 60 meters, mean filtering is performed for smoothing.

[0095] Exemplarily, for an area containing steep cliffs, the original data may show abrupt elevation changes. Through mean filtering, the abnormal spikes can be smoothed into a more continuous slope change, generating the sixth geographical dataset. This processing helps to reduce noise interference and improve the usability of the data. When obtaining the matching features of terrain complexity and computing framework from the sixth geographical dataset, adjustment can be made through data fusion methods.

[0096] For example, in a high complexity region, if the processing power of the computing framework is insufficient, it may lead to delays. At this time, additional elevation smoothing data can be fused to optimize resource allocation, ultimately obtaining the seventh geographical dataset. This adjustment can better match the actual computing requirements. When judging the degree of association between terrain features and real-time requirements through the seventh geographical dataset.

[0097] In one embodiment, it can be statistically determined whether the processing delay in high complexity regions is less than 2 seconds. If the requirement is met, it indicates a high degree of matching between terrain features and real-time performance.

[0098] For example, a real-time navigation system can quickly plan a route based on this to avoid calculation bottlenecks caused by complex terrain. This kind of analysis can provide a key basis for system optimization.

[0099] S105. Extract obstacle data from the second geographical dataset, generate a preliminary three-dimensional model through the voxel grid method, and superimpose the stereo interpolation algorithm according to the spatial distribution characteristics of the obstacle data to obtain an optimized first three-dimensional model.

[0100] Extract obstacle data through the second geographical dataset, generate an initial three-dimensional model using the voxel grid method to obtain a first three-dimensional model. According to the spatial distribution characteristics in the first three-dimensional model, superimpose the stereo interpolation algorithm for processing to obtain a second three-dimensional model. Obtain distribution characteristic data from the second three-dimensional model, separate the obstacle distribution information through a data extraction method to obtain a first distribution dataset. If the distribution characteristics in the first distribution dataset exceed a preset threshold, perform smoothing processing using mean filtering to obtain a second distribution dataset. According to the obstacle distribution information in the second distribution dataset, use the clustering analysis method to group the data to obtain a first grouped dataset. Through the grouping results in the first grouped dataset, obtain the matching characteristics between the spatial distribution and the obstacle data to obtain a first matching dataset. For the matching characteristics in the first matching dataset, use the data fusion method for adjustment to obtain a second matching dataset.

[0101] Specifically, when extracting obstacle data through the second geographical dataset.

[0102] It can be understood that obstacles may include topographic elements such as vegetation, buildings, or rocks.

[0103] Exemplarily, in a mountainous scene, the second geographical dataset may contain elevation information and surface cover data. By analyzing elevation mutation points and cover types, the rock distribution area can be identified.

[0104] In one embodiment, assume that the elevation change rate in a certain area exceeds 20%, and the cover type shows bare rock, then mark it as an obstacle, extract the relevant coordinates and ranges to form initial obstacle data. When generating the first three-dimensional model using the voxel grid method.

[0105] It should be noted that the voxel grid divides the space into regular cube units, which is convenient for representing complex terrain.

[0106] Specifically, the space can be divided at a resolution of 1 meter × 1 meter × 1 meter.

[0107] Exemplarily, if a hillside area contains 1000 voxels, and 300 of them are marked as obstacles, an initial three-dimensional model can be constructed by connecting these voxels to intuitively reflect the obstacle distribution. The stereoscopic interpolation algorithm is superimposed on the first three-dimensional model to generate the second three-dimensional model.

[0108] It can be understood that stereoscopic interpolation infers the characteristics of unknown regions through existing data points.

[0109] For example, in a sparse vegetation area, the first three-dimensional model may only mark the positions of trees, and a continuous vegetation density distribution map can be generated after interpolation.

[0110] Preferably, if the voxel spacing in a certain area is large, interpolation can fill the gaps to make the second three-dimensional model closer to the real terrain. When obtaining distribution characteristic data from the second three-dimensional model and separating obstacle information.

[0111] In a possible implementation, it can be completed by threshold screening. Assume that the area where the vegetation density is greater than 0.8 is regarded as a dense obstacle area, and the first distribution data set is formed after extraction.

[0112] For example, in a forest area, 40% of the voxels with a density higher than 0.8 can clearly divide the obstacle and non-obstacle areas after separation. If the first distribution data set exceeds the preset threshold, smoothing processing is required.

[0113] Specifically, mean filtering can reduce the influence of noise.

[0114] Exemplarily, if the density changes violently in the edge area of an obstacle and exceeds the threshold of 0.9, the mean value of the surrounding 5×5 voxel range is taken to generate the second distribution data set, making the distribution smoother and more coherent, which is helpful for subsequent analysis. When performing clustering analysis according to the second distribution data set.

[0115] In one embodiment, the K-means method can be used for grouping.

[0116] For example, the obstacle distribution is divided into three categories: high, medium, and low according to density, and the first grouping data set is obtained. Assume that the proportion of high-density obstacles in a certain area is 30% and the medium density is 50%. After grouping, it can reflect the characteristics of different terrain areas. When obtaining matching features through the first grouping data set.

[0117] In a possible implementation, the correlation between obstacles and spatial distribution can be analyzed.

[0118] For example, in a valley, low-density obstacles are concentrated in flat areas, and high-density obstacles are located on steep slopes, generating the first matching data set to reveal the distribution law. When adjusting the first matching data set to generate the second matching data set, the data fusion method can integrate multi-source information.

[0119] Preferably, if there is a deviation between the elevation data and the density data, it can be adjusted by weighted fusion.

[0120] For example, the elevation weight is set to 0.6 and the density weight is set to 0.4. After fusion, the second matching data set can more accurately reflect the matching relationship between the terrain and the obstacles, which helps to improve the accuracy of spatial analysis.

[0121] S106. Obtain the real-time meteorological information flow, calculate the influence of meteorology on signal transmission through the hydrodynamic simulation algorithm, and adopt the time series prediction method for the dynamic changes of the meteorological information flow to obtain the second three-dimensional model that matches the current environment.

[0122] Obtain the real-time meteorological information flow, collect dynamic change data through the sensor network to obtain the first meteorological data set. Process the first meteorological data set through the hydrodynamic simulation algorithm to calculate the signal transmission parameters and obtain the first influence data set. Analyze the dynamic change characteristics in the first influence data set by adopting the time series prediction method to obtain the first prediction data set. Match the current environmental parameters according to the prediction results in the first prediction data set to obtain the second three-dimensional model. If the signal transmission parameters in the second three-dimensional model exceed the preset threshold, then process the first influence data set through mean filtering to obtain the second influence data set. Adopt the clustering analysis method to group the data in the second influence data set to obtain the first grouped data set. Adjust the spatial distribution characteristics of the second three-dimensional model according to the grouping results in the first grouped data set to obtain the third three-dimensional model.

[0123] Specifically, when obtaining the real-time meteorological information flow, dynamic change data can be collected through the sensor network.

[0124] For example, multiple meteorological sensors arranged over the city record parameters such as temperature, humidity, and wind speed every second to form the first meteorological data set.

[0125] Exemplarily, these data may show that the temperature in a certain area fluctuates between 25 degrees and 30 degrees, and the wind speed is 5 meters per second.

[0126] In a possible implementation, the coverage area of the sensor network is 10 square kilometers, and the collection frequency is once per minute to ensure data real-time. When processing the first meteorological data set through the hydrodynamic simulation algorithm, the signal transmission parameters can be calculated.

[0127] Specifically, assuming that the wind speed and humidity affect signal attenuation, the simulation results may show that the signal attenuation rate in a certain area is 2 decibels per kilometer to obtain the first influence data set.

[0128] It should be noted that hydrodynamic simulations take into account air flow characteristics, such as the deflection effect of wind direction on signal paths. This method can help analyze the impact of meteorological conditions on transmission. For the dynamic change characteristics in the first impact dataset, time series prediction methods are used for analysis.

[0129] For example, based on the wind speed data in the past 1 hour, it is predicted that the wind speed may rise to 7 meters per second in the next 30 minutes, generating the first prediction dataset.

[0130] In one embodiment, time series prediction can anticipate meteorological changes in advance through historical trend and periodic fluctuation analysis. This prediction helps optimize subsequent model adjustments. According to the prediction results in the first prediction dataset, the current environmental parameters are matched to generate the second 3D model.

[0131] Preferably, the environmental parameters include terrain height and vegetation density. After combining with the predicted wind speed, the model may show that the signal coverage area in a certain region is reduced by 10%.

[0132] It can be understood that this matching can reflect the interaction characteristics between meteorology and space, providing a dynamic basis for the model. If the signal transmission parameters in the second 3D model exceed the preset threshold, such as the attenuation rate exceeding 3 decibels per kilometer, the first impact dataset is processed by mean filtering.

[0133] For example, the attenuation rate at a certain point in the original data is 4 decibels per kilometer, and after filtering, it drops to 2.5 decibels per kilometer, generating the second impact dataset.

[0134] Specifically, mean filtering improves the stability of the data by smoothing outliers, which helps the accuracy of subsequent analysis. When using the clustering analysis method to group the data in the second impact dataset, it can be divided into 3 groups according to the level of attenuation rate.

[0135] For example, the low attenuation group is 0 - 1 decibel per kilometer, the medium attenuation group is 1 - 2 decibels per kilometer, and the high attenuation group is above 2 decibels per kilometer, obtaining the first grouped dataset.

[0136] In one possible implementation, the clustering results reflect the signal characteristic differences in different regions, which is convenient for targeted optimization. Based on the grouping results in the first grouped dataset, the spatial distribution characteristics of the second 3D model are adjusted to generate the third 3D model.

[0137] For example, the area corresponding to the high attenuation group may be marked as a signal blind area, and the model will adjust the coverage range to reduce the blind area by 15%.

[0138] Exemplarily, this adjustment can improve the adaptability of the model to complex environments and ensure a more reasonable signal distribution. This method optimizes the spatial characteristics through data-driven, which has high practical value.

[0139] S107. Based on the second 3D model and the second signal set, the particle filter algorithm is used to implement the fusion processing of signals and geographical information. For the low-altitude navigation requirement, the model parameters are adjusted to optimize the signal coverage range, and the fused and enhanced third signal set is obtained.

[0140] The characteristic data in the signal set is extracted through the second model to obtain the first characteristic set. Based on the first characteristic set and geographical information, the particle filter algorithm is used for fusion processing to obtain the first fusion set. According to the data distribution characteristics in the first fusion set, the parameters related to low-altitude navigation are adjusted to obtain the first optimized set. The spatial distribution information of the enhanced signal is obtained from the first optimized set, and the boundary value of the coverage range is determined. If the boundary value exceeds the preset threshold, the first optimized set is processed by mean filtering to obtain the second optimized set. According to the data characteristics in the second optimized set, the clustering analysis method is used for grouping to obtain the first grouped set. The spatial distribution of the enhanced signal is adjusted through the first grouped set to obtain the third signal set.

[0141] Specifically, the characteristic data in the signal set is extracted through the second model to obtain the first characteristic set. This process aims to extract key information from complex signal data.

[0142] For example, in a low-altitude navigation scenario, the signal set may include raw data such as electromagnetic wave intensity, delay time, and multipath effect. After feature extraction, the first characteristic set may include the peak intensity of the signal and the direction angle of the main propagation path.

[0143] Specifically, through the time-domain analysis method, the waveform of the signal can be decomposed into multiple characteristic components. For example, the maximum amplitude value within every 10 milliseconds, such as 5 volts, is extracted as one of the characteristics. Based on the first characteristic set and geographical information, the particle filter algorithm is used for fusion processing to obtain the first fusion set. This link is the key to combining signal features with environmental elements. The particle filter optimizes the data fusion result by simulating multiple possible state distributions.

[0144] Exemplarily, in a mountainous environment, the geographical information may include terrain undulations with a height of 500 meters, and the signal direction angle in the first characteristic set is 30 degrees. The particle filter can generate a set of distributed particles based on this information, estimate the true propagation path of the signal in this area, and finally fuse the first fusion set, which may show that the optimal estimated intensity of the signal at a certain point is 4.8 volts. According to the data distribution characteristics in the first fusion set, the parameters related to low-altitude navigation are adjusted to obtain the first optimized set. This adjustment is to adapt to the actual application requirements.

[0145] In a possible implementation, the first fusion set may show that the signal strength is low in some areas, such as below 3 volts. At this time, the parameters can be optimized by increasing the transmission power or adjusting the antenna angle to form the first optimization set, ensuring more uniform signal coverage and effectively improving the navigation accuracy. Obtain the spatial distribution information of the enhanced signal from the first optimization set and determine the boundary value of the coverage range. This process focuses on the effective range of the signal.

[0146] It can be understood that the optimized signal may maintain a strength higher than 4 volts within a horizontal distance of 10 kilometers, and the boundary value may be set at 3.5 volts. If this threshold is exceeded, it indicates insufficient coverage.

[0147] For example, in the urban fringe area, the signal strength may drop below 3 volts due to building obstruction, exceeding the threshold. If the boundary value exceeds the preset threshold, the first optimization set is processed by mean filtering to obtain the second optimization set. This method is used to smooth data fluctuations.

[0148] Preferably, if the signal strength in a certain area frequently jumps between 3 and 4 volts, the mean filtering can take the average of the previous and next 5 sampling points. For example, smoothing 3.2 volts, 3.8 volts, and 3.5 volts to 3.5 volts to form a more stable second optimization set and improve signal consistency. According to the data characteristics in the second optimization set, clustering analysis is used for grouping to obtain the first grouping set. This grouping helps to identify the pattern of signal distribution.

[0149] In one embodiment, the second optimization set may contain multiple signal strength intervals, such as 3.5 to 4 volts and 4 to 4.5 volts. Through clustering analysis, it can be divided into two groups. The first grouping set may show that the low-strength group is concentrated in the urban fringe and the high-strength group is close to the base station, which helps with subsequent layout optimization. Adjust the spatial distribution of the enhanced signal through the first grouping set to obtain the third signal set. This adjustment further improves signal coverage.

[0150] For example, for the low-strength group, a relay station can be added to increase the signal strength from 3.5 volts to above 4 volts. Finally, the spatial distribution of the third signal set is more balanced, ensuring the stable operation of the low-altitude navigation device. This method significantly improves the reliability and coverage of the signal.

[0151] Embodiment 2:

[0152] The present invention also provides a multi-channel-based low-altitude helicopter communication and navigation signal enhancement system, including:

[0153] A first processing module, configured to obtain a first signal set that is synchronized and has low interference according to the original signal data collected by multi-channel technology;

[0154] The second processing module is used to extract feature parameters from the first signal set, perform preliminary enhancement processing on the signals through the Kalman filtering algorithm, and superimpose a preset attenuation compensation model for the multipath effect caused by terrain complexity to obtain an enhanced second signal set;

[0155] The third processing module is used to obtain a unified first geographical data set according to the terrain data, obstacle data, and meteorological information flow in the geographical information source;

[0156] The fourth processing module is used to obtain a second geographical data set containing terrain features according to the first geographical data set;

[0157] The fifth processing module is used to extract obstacle data from the second geographical data set, generate a preliminary three-dimensional model through the voxel grid method, and superimpose a stereoscopic interpolation algorithm for the spatial distribution characteristics of the obstacle data to obtain an optimized first three-dimensional model;

[0158] The sixth processing module is used to obtain real-time meteorological information flow to obtain a second three-dimensional model matching the current environment;

[0159] The seventh processing module is used to perform fusion processing of signals and geographical information according to the second three-dimensional model and the second signal set by using the particle filtering algorithm, and adjust the model parameters to optimize the signal coverage range for low-altitude navigation requirements to obtain a fusion-enhanced third signal set.

[0160] As an implementation manner of an embodiment of the present invention, the first processing module acquires original signal data collected by multi-channel technology, performs time alignment on the data of each channel through a preset time-domain synchronization algorithm, and suppresses cross-interference between channels by using an adaptive filter for the signal synchronization requirement to obtain a synchronized and low-interference first signal set.

[0161] As an implementation manner of an embodiment of the present invention, the third processing module acquires terrain data, obstacle data, and meteorological information flow in the geographical information source, converts heterogeneous data into a unified format through a data preprocessing module, and uses a standardization mapping method for the data heterogeneity problem to obtain a unified first geographical data set.

[0162] As an implementation manner of an embodiment of the present invention, the fourth processing module analyzes terrain complexity features according to the first geographical data set by using the fast Fourier transform algorithm, and accelerates the processing by using a parallel computing framework in combination with real-time requirements to obtain a second geographical data set containing terrain features.

[0163] As an implementation manner of an embodiment of the present invention, the sixth processing module acquires real-time meteorological information flow, calculates the influence of meteorology on signal transmission through a hydrodynamic simulation algorithm, and uses a time series prediction method for the dynamic change of the meteorological information flow to obtain a second three-dimensional model matching the current environment.

[0164] The above are only the preferred embodiments of one or more embodiments of this specification, and are not intended to limit one or more embodiments of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of protection of one or more embodiments of this specification.

Claims

1. A multi-channel-based method for enhancing communication and navigation signals of low-altitude helicopters, characterized in that, Including: Based on the original signal data collected by multi-channel technology, obtain a first signal set that is synchronized and has low interference; Extract feature parameters from the first signal set, perform preliminary enhancement processing on the signal through the Kalman filtering algorithm, and superimpose a preset attenuation compensation model for the multipath effect caused by terrain complexity to obtain an enhanced second signal set; Based on the terrain data, obstacle data, and meteorological information flow in the geographic information source, obtain a unified first geographic data set; Based on the first geographic data set, obtain a second geographic data set containing terrain features; Extract obstacle data from the second geographic data set, generate a preliminary three-dimensional model through the voxel grid method, and superimpose a stereoscopic interpolation algorithm for the spatial distribution characteristics of the obstacle data to obtain an optimized first three-dimensional model; Obtain real-time meteorological information flow to obtain a second three-dimensional model that matches the current environment; Based on the second three-dimensional model and the second signal set, use the particle filtering algorithm to implement the fusion processing of the signal and geographic information, and adjust the model parameters to optimize the signal coverage range for low-altitude navigation requirements to obtain a fused and enhanced third signal set.

2. The multi-channel based low-altitude helicopter communication and navigation signal enhancement method according to claim 1, wherein Obtain the original signal data collected by multi-channel technology, perform time alignment on the data of each channel through a preset time-domain synchronization algorithm, and use an adaptive filter to suppress the cross-interference between channels for the signal synchronization requirement to obtain a first signal set that is synchronized and has low interference.

3. The multi-channel based low-altitude helicopter communication and navigation signal enhancement method according to claim 2, characterized in that Obtain the terrain data, obstacle data, and meteorological information flow in the geographic information source, convert the heterogeneous data into a unified format through a data preprocessing module, and use a standard mapping method for the data heterogeneity problem to obtain a unified first geographic data set.

4. The multi-channel-based low-altitude helicopter communication and navigation signal enhancement method according to claim 3, wherein, Based on the first geographic data set, analyze the terrain complexity characteristics using the fast Fourier transform algorithm, and use a parallel computing framework to accelerate the processing in combination with the real-time requirement to obtain a second geographic data set containing terrain features.

5. The multi-channel based method for enhancing communication and navigation signals of a low-altitude helicopter according to claim 4, wherein Obtain real-time meteorological information flow, calculate the impact of meteorology on signal transmission through the hydrodynamic simulation algorithm, and use a time series prediction method for the dynamic change of the meteorological information flow to obtain a second three-dimensional model that matches the current environment.

6. A multi-channel low-altitude helicopter communication and navigation signal enhancement system, characterized in that, Including: A first processing module for obtaining a first signal set that is synchronized and has low interference based on the original signal data collected by multi-channel technology; A second processing module for extracting feature parameters from the first signal set, performing preliminary enhancement processing on the signal through the Kalman filtering algorithm, and superimposing a preset attenuation compensation model for the multipath effect caused by terrain complexity to obtain an enhanced second signal set; A third processing module for obtaining a unified first geographic data set based on the terrain data, obstacle data, and meteorological information flow in the geographic information source; A fourth processing module for obtaining a second geographic data set containing terrain features based on the first geographic data set; A fifth processing module for extracting obstacle data from the second geographic data set, generating a preliminary three-dimensional model through the voxel grid method, and superimposing a stereoscopic interpolation algorithm for the spatial distribution characteristics of the obstacle data to obtain an optimized first three-dimensional model; A sixth processing module for obtaining real-time meteorological information flow to obtain a second three-dimensional model that matches the current environment; The seventh processing module is used to implement the fusion processing of signals and geographic information according to the second 3D model and the second signal set, and adjust the model parameters to optimize the signal coverage range for low-altitude navigation requirements, so as to obtain a third signal set with enhanced fusion.

7. The multi-channel-based low-altitude helicopter communication and navigation signal enhancement system according to claim 6, characterized in that, The first processing module acquires the original signal data collected by the multi-channel technology, performs time alignment on the data of each channel through a preset time-domain synchronization algorithm, and suppresses the cross-interference between channels by using an adaptive filter for the signal synchronization requirement, so as to obtain a first signal set with synchronization and low interference.

8. The multi-channel-based low-altitude helicopter communication and navigation signal enhancement system according to claim 7, wherein The third processing module acquires the terrain data, obstacle data and meteorological information flow in the geographic information source, converts the heterogeneous data into a unified format through the data preprocessing module, and adopts a standard mapping method for the data heterogeneity problem, so as to obtain a first consistent geographic data set.

9. The multi-channel based low-altitude helicopter communication and navigation signal enhancement system according to claim 8, wherein The fourth processing module analyzes the terrain complexity features according to the first geographic data set by using the fast Fourier transform algorithm, and accelerates the processing by using a parallel computing framework in combination with the real-time requirement, so as to obtain a second geographic data set containing terrain features.

10. The multi-channel based low-altitude helicopter communication and navigation signal enhancement system according to claim 9, characterized in that, The sixth processing module acquires the real-time meteorological information flow, calculates the influence of meteorology on signal transmission through the hydrodynamic simulation algorithm, and adopts a time series prediction method for the dynamic change of the meteorological information flow, so as to obtain a second 3D model matching the current environment.

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