Low-altitude helicopter communication and navigation signal enhancement method and system based on intelligent scheduling

Abstract

The application discloses a low-altitude helicopter communication navigation signal enhancement method and system based on intelligent scheduling, which comprises the following steps: acquiring time-frequency characteristic data collected by a sensor array, extracting interference source parameters by using a fast Fourier transform algorithm, and generating an interference source distribution map; if the dynamic prediction result of a deep learning model on the interference source distribution map exceeds a preset threshold value, a space-time change trend map is generated; according to the space-time change trend map, a signal path block point is calculated by using a digital elevation model, and a shielding influence area range is determined; electromagnetic interference intensity data is extracted from the shielding influence area range, and interference frequency principal component components are obtained by decomposition through a spectrum analysis algorithm; if the electromagnetic interference intensity corresponding to the principal component components is higher than a preset threshold value, the parameters of an adaptive filter are adjusted to generate an optimized filter signal; the optimized filter signal is subjected to space-time encoding processing, a two-dimensional convolution algorithm is used to eliminate weather influencing factors, and an anti-interference enhanced signal is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a low-altitude helicopter communication navigation signal enhancement method and system based on intelligent scheduling. BACKGROUND

[0002] Low-altitude helicopter communication navigation signal enhancement technology is an indispensable research direction in the field of modern aviation, and its core is to ensure the safe and efficient operation of helicopters in complex low-altitude environments. With the gradual opening of low-altitude airspace and the expansion of helicopter application scenarios, the stability and reliability of the communication and navigation system have become key factors in determining flight safety. Especially in diverse terrains such as cities and mountains, signal transmission faces severe challenges, and research into targeted enhancement technology has important strategic significance.

[0003] Currently, traditional signal enhancement methods have obvious shortcomings in low-altitude environments. Many solutions rely on static spectrum allocation or simple filtering techniques, which are difficult to cope with dynamic interference sources. In particular, in scenarios where terrain obstructions are frequent and electromagnetic interference is dense, these methods often fail to adjust in real time, resulting in a decline or even interruption of signal quality. In addition, existing technologies have poor adaptability in adverse weather conditions, limiting the operational capabilities of helicopters in all-weather conditions.

[0004] Focusing on specific challenges, the diversity and dynamics of interference sources in low-altitude environments are the primary problem. Terrain obstructions cause signal paths to be blocked, electromagnetic interference is exacerbated due to spectrum congestion, and weather factors such as rain and fog further weaken signal strength. These three core technical factors - the real-time nature of interference source detection, the effective filtering of interference signals, and the anti-interference ability of signal transmission - directly determine system performance. Due to the failure to fully address these factors, helicopter communication and navigation systems are prone to signal distortion or transmission failure in complex environments, which in turn poses a unique technical challenge: how to accurately respond to changing interference with limited resources.

[0005] Therefore, how to use intelligent means to real-time perceive interference sources in low-altitude environments, and combine adaptive filtering and space-time coding techniques to effectively improve the anti-interference ability of signals, has become a key issue in ensuring the stable operation of helicopter communication and navigation systems. The solution to this problem will directly promote the development of low-altitude flight technology and lay the foundation for more extensive applications. SUMMARY

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

[0007] To achieve the above-mentioned purpose, the technical solution adopted by the present application is as follows:

[0008] A low-altitude helicopter communication navigation signal enhancement method based on intelligent scheduling, comprising:

[0009] Obtain time-frequency feature data collected by a sensor array, extract interference source parameters using a fast Fourier transform algorithm, and generate an interference source distribution map;

[0010] If the dynamic prediction result of the deep learning model on the interference source distribution map exceeds a preset threshold, generate a spatiotemporal change trend map;

[0011] According to the spatiotemporal change trend map, calculate signal path block points using a digital elevation model, and determine the range of the shielding influence area;

[0012] Extract electromagnetic interference intensity data from the shielding influence area range, and decompose to obtain principal component components of interference frequency by a spectrum analysis algorithm;

[0013] If the electromagnetic interference intensity corresponding to the principal component component is higher than a preset threshold, adjust the adaptive filter parameter to generate an optimized filter signal;

[0014] Perform space-time encoding processing on the optimized filter signal, eliminate weather influencing factors using a two-dimensional convolution algorithm, and obtain an anti-interference enhanced signal;

[0015] Extract the transmission signal from the adjusted encoded signal, optimize the bandwidth and power parameters using a dynamic resource allocation algorithm, and output a stable communication signal.

[0016] Preferably, the obtaining of the time-frequency feature data collected by the sensor array, the extraction of the interference source parameters using the fast Fourier transform algorithm, and the generation of the interference source distribution map comprise:

[0017] Obtain raw signal data collected by a sensor array, filter out noise through preprocessing to obtain clean time-frequency feature data;

[0018] Process the clean time-frequency feature data using a fast Fourier transform algorithm to extract interference source parameters and obtain a parameter set;

[0019] Classify the parameter set using a clustering algorithm to determine the interference source type distribution;

[0020] If the interference source type distribution exceeds a preset threshold, calculate the interference source position using a weighted average method to obtain a position coordinate set;

[0021] Generate two-dimensional grid data according to the position coordinate set to obtain an interference distribution base map;

[0022] Smooth the two-dimensional grid data using an interpolation algorithm to obtain an optimized interference source distribution map;

[0023] According to the optimized interference source distribution map, a color mapping technology is used to generate a visual interference distribution map.

[0024] As preferred, if the dynamic prediction result of the deep learning model on the interference source distribution map exceeds a preset threshold, a spatio-temporal change trend map is generated, including:

[0025] If the prediction result generated by the deep learning exceeds the preset threshold, time series data in dynamic prediction is extracted through time series analysis to obtain change trend characteristics;

[0026] According to the change trend characteristics, the time series data is processed by a convolution smoothing method to obtain a smoothed trend sequence;

[0027] Through the smoothed trend sequence, the distribution offset of each time point is calculated to determine the spatio-temporal distribution characteristics;

[0028] According to the spatio-temporal distribution characteristics, a grid division technology is used to generate two-dimensional spatio-temporal grid data to obtain a preliminary trend distribution;

[0029] According to the preliminary trend distribution, a color coding technology is used to process the grid data to generate a visual trend map;

[0030] If the distribution offset in the visual trend map exceeds a preset range, the grid data is adjusted through a clustering algorithm to obtain an optimized spatio-temporal change trend map;

[0031] Through the optimized spatio-temporal change trend map, a key change area is extracted to determine the final trend distribution.

[0032] As preferred, according to the spatio-temporal change trend map, a digital elevation model is used to calculate a signal path block point to determine the range of the shielding influence area, including:

[0033] Through the digital elevation model, elevation data is obtained to calculate the distribution of the signal path in the spatio-temporal change trend map to obtain path change characteristics;

[0034] The path change characteristics are used to analyze the height difference on the signal path to determine the position of the block point and determine a set of potential shielding points;

[0035] The shielding influence data in the block point set is obtained, and the boundary of the shielding area is calculated through the elevation data to obtain the range of the shielding area;

[0036] The terrain characteristics within the influence range are extracted for the shielding area range to determine whether the signal path is completely blocked and determine the blocking degree;

[0037] The distribution law of the shielding influence is analyzed through the blocking degree to obtain the accurate boundary of the influence range;

[0038] The random forest algorithm is used to classify the shielding area in the influence range, to determine the shielding severity of different areas, and to determine the final shielding influence area range.

[0039] According to the final shielding influence area range, the optimized layout of the signal path is adjusted through the elevation data to obtain the adjusted path distribution.

[0040] As preferred, the electromagnetic interference intensity data is extracted from the shielding influence area range, and the interference frequency principal component is obtained by spectral analysis algorithm decomposition, including:

[0041] The shielding influence determination area range is obtained, and the electromagnetic interference corresponding intensity data is obtained by using a measuring tool;

[0042] The interference frequency characteristics are extracted from the intensity data, and the initial component result is obtained by spectral analysis algorithm decomposition;

[0043] The frequency analysis is performed on the initial component result, and the distribution characteristics of the principal component are determined;

[0044] If the principal component exceeds the preset threshold, the independent characteristics of the interference frequency are obtained by component decomposition;

[0045] The source direction of the electromagnetic interference is determined according to the independent characteristics, and the interference source coordinates are obtained by using a positioning algorithm;

[0046] The boundary range of the shielding influence is determined by comparing the interference source coordinates with the area range;

[0047] The intensity data in the boundary range is obtained, and the optimized interference frequency component is obtained by using a filtering tool.

[0048] As preferred, if the electromagnetic interference intensity corresponding to the principal component is higher than the preset threshold, the adaptive filter parameters are adjusted to generate an optimized filtering signal, including:

[0049] If the principal component of the electromagnetic interference exceeds the preset threshold, the component analysis result is determined by analyzing the interference intensity;

[0050] The characteristic data of the interference intensity is obtained by the component analysis result, and it is determined whether the filter needs to be adjusted;

[0051] If the characteristic data exceeds the threshold judgment standard, the parameters of the adaptive filter are adjusted to obtain a preliminary filtering signal;

[0052] According to the preliminary filtering signal, an optimized signal is generated by using a signal generation technology;

[0053] The residual component of the electromagnetic interference is extracted from the optimized signal, and it is determined whether the residual component is lower than the preset threshold;

[0054] If the residual component is higher than the preset threshold, the filter parameters are adjusted iteratively to obtain a final optimized signal;

[0055] According to the final optimized signal, the suppression degree of electromagnetic interference is determined, and a stable output signal is generated.

[0056] As a preferred, the optimized filter signal is processed by space-time coding, and a two-dimensional convolution algorithm is used to eliminate weather influencing factors to obtain an anti-interference enhanced signal, including:

[0057] The input signal is processed by an optimized filter to obtain a filter signal using preset filter parameters;

[0058] The filter signal is processed by space-time coding to generate a coded signal through coding technology;

[0059] A two-dimensional convolution algorithm is used to perform convolution operation on the coded signal to obtain a convolution result;

[0060] The weather influencing factors are removed through the convolution result to obtain a preliminary enhanced signal;

[0061] If there is residual interference in the preliminary enhanced signal, an anti-interference detection method is used to determine the interference degree to obtain a detection result;

[0062] The preliminary enhanced signal is adjusted according to the detection result, and a final enhanced signal is obtained by using a weighting process;

[0063] The effectiveness of the processing flow is verified through the final enhanced signal to determine the anti-interference enhanced signal.

[0064] As a preferred, the transmission signal is extracted from the adjusted coded signal, and a dynamic resource allocation algorithm is used to optimize the bandwidth and power parameters to output a stable communication signal, including:

[0065] The transmission signal is obtained from the coded signal through a decoder to obtain initial transmission data;

[0066] The integrity of the initial transmission data is determined by using a preset threshold to obtain an effective transmission signal;

[0067] The bandwidth parameter of the effective transmission signal is adjusted by using a dynamic resource allocation algorithm to obtain an optimized bandwidth signal;

[0068] The power parameter of the optimized bandwidth signal is adjusted by using a dynamic resource allocation algorithm to obtain an optimized power signal;

[0069] According to the characteristics of the optimized power signal, a signal processing technology is used to smooth the noise to obtain a smooth communication signal;

[0070] The stability of the smooth communication signal is detected by using a channel simulation tool to obtain a stable communication signal.

[0071] The application also provides a low-altitude helicopter communication navigation signal enhancement system based on intelligent scheduling, comprising:

[0072] A time-frequency feature extraction module is configured to acquire time-frequency feature data collected by the sensor array.

[0073] An interference source parameter extraction module is configured to extract interference source parameters by using a fast Fourier transform algorithm.

[0074] An interference source distribution map generation module is configured to generate an interference source distribution map.

[0075] A space-time change trend map generation module is configured to generate a space-time change trend map if a dynamic prediction result of the deep learning model on the interference source distribution map exceeds a preset threshold.

[0076] A signal path block point calculation module is configured to calculate signal path block points by using a digital elevation model according to the space-time change trend map.

[0077] An electromagnetic interference intensity extraction module is configured to determine a shielding influence area range and extract electromagnetic interference intensity data from the shielding influence area range.

[0078] An optimized filtered signal generation module is configured to decompose interference frequency principal component components by using a spectrum analysis algorithm, and adjust adaptive filter parameters to generate an optimized filtered signal if the electromagnetic interference intensity corresponding to the principal component components is higher than a preset threshold.

[0079] An anti-interference enhanced signal generation module is configured to perform space-time encoding processing on the optimized filtered signal, eliminate weather influencing factors by using a two-dimensional convolution algorithm, and obtain an anti-interference enhanced signal.

[0080] A stable communication signal output module is configured to extract a transmission signal from the adjusted encoded signal, optimize bandwidth and power parameters by using a dynamic resource allocation algorithm, and output a stable communication signal.

[0081] Firstly, the application acquires time-frequency feature data by using a sensor array, extracts interference source parameters by using a fast Fourier transform, and generates a distribution map. When the prediction result exceeds a threshold, a space-time change trend map is generated, and a shielding influence area is determined in combination with a digital elevation model. Then, electromagnetic interference intensity data is extracted from the area, principal component components are obtained by using spectrum analysis, and adaptive filter parameters are adjusted if the interference intensity is too high. The space-time encoding processing is performed to eliminate weather influences. Finally, a transmission signal is extracted from the optimized encoded signal, bandwidth and power parameters are optimized by using a dynamic resource allocation algorithm, and a stable anti-interference communication signal is output. This method can effectively cope with complex electromagnetic environments, and improve the anti-interference ability and signal quality of a communication system. BRIEF DESCRIPTION OF DRAWINGS

[0082] Figure 1 A flowchart of a low-altitude helicopter communication navigation signal enhancement method based on intelligent scheduling. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be described in detail below with reference to the drawings and specific embodiments.

[0084] As Figure 1 shown, the embodiment of the present application is a low-altitude helicopter communication navigation signal enhancement method based on intelligent scheduling, which comprises:

[0085] S101, acquiring time-frequency feature data collected by a sensor array, extracting interference source parameters by using a fast Fourier transform algorithm, and generating an interference source distribution map.

[0086] The original signal data collected by the sensor array is obtained, and noise is filtered out through preprocessing to obtain clean time-frequency feature data. The clean time-frequency feature data is processed by a fast Fourier transform algorithm to extract interference source parameters and obtain a parameter set. The parameter set is classified by using a clustering algorithm to determine the interference source type distribution. If the interference source type distribution exceeds a preset threshold, the position of the interference source is calculated by using a weighted average method to obtain a position coordinate set. A two-dimensional grid data is generated according to the position coordinate set to obtain an interference distribution base map. The two-dimensional grid data is smoothed by using an interpolation algorithm to obtain an optimized interference source distribution map. For the optimized interference source distribution map, a color mapping technology is used to generate a visual interference distribution map.

[0087] Specifically, when acquiring the original signal data collected by the sensor array, it can be understood that the electromagnetic signals in the environment are monitored in real time by forming an array with multiple sensors.

[0088] For example, near a wireless communication base station, the sensor array may capture radio frequency signals mixed with noise, and the data appears as a waveform of voltage changing with time, with an amplitude range of-5V to 5V. When filtering out noise,

[0089] In one possible implementation, a low-pass filter can be used to remove high-frequency noise with a frequency higher than 1000Hz and retain the main signal components.

[0090] Exemplarily, after filtering, the jitter of the signal waveform is significantly reduced, and the peak value is more clearly visible, effectively improving the accuracy of subsequent analysis. When processing the clean time-frequency feature data by using a fast Fourier transform algorithm,

[0091] Preferably, the time domain signal is converted to the frequency domain to extract the spectral features.

[0092] For example, assume the signal sampling rate is 2000 Hz, and the main frequency components are observed to be concentrated at 200 Hz and 500 Hz, corresponding to the characteristic frequencies of two potential interference sources. When extracting the interference source parameters.

[0093] Specifically, the peak frequency, amplitude, and bandwidth can be identified from the spectrum to form parameter sets, such as {200 Hz, 0.8 V, 10 Hz} and {500 Hz, 1.2 V, 15 Hz}. This provides a reliable basis for subsequent classification.

[0094] In an embodiment, the K-means algorithm can be used to classify the parameters into two categories, corresponding to low-frequency and high-frequency interference sources, respectively.

[0095] It should be noted that if the number of sample points is 100, 80% of them may be found to belong to low-frequency interference near 200 Hz, and 20% to high-frequency interference near 500 Hz, reflecting the distribution of interference source types. If the preset threshold is 70%, the low-frequency interference exceeds the threshold, triggering position calculation.

[0096] For example, by using the weighted average method, combined with the spatial layout of the sensor array, assume that the three sensors are located at (0, 0), (5, 0), and (0, 5), and according to the signal intensity weighting, the low-frequency interference source position coordinates are approximately (2, 1). When generating a two-dimensional grid data set from the position coordinates, it can be understood that the coordinates are mapped to a 10x10 grid to form a basic diagram of interference distribution. When smoothing.

[0097] In a possible implementation, the linear interpolation algorithm is used to make the values in the blank areas of the grid smoothly transition based on the adjacent points.

[0098] For example, the values near (2, 1) are smoothly transitioned from 1.0 to 0.8 at (3, 2), and the optimized interference source distribution map has more continuity. When using color mapping technology for the optimized distribution map.

[0099] Specifically, red can be used to represent areas with intensity greater than 1.0, and green can be used to represent areas with intensity between 0.5 and 1.0 to generate a visualization map. This visualization effect intuitively reflects the distribution of interference intensity and helps quickly locate problem areas.

[0100] For example, the distribution map generated by the above method can significantly improve the efficiency of interference source troubleshooting, especially in dense signal environments, it can clearly distinguish multiple source interference and reduce false positives.

[0101] Preferably, this technique can also provide data support for subsequent optimization of sensor layout, enhancing the robustness of the system.

[0102] It can be understood that the implementation of each step is closely linked, and together it builds a complete link from signal acquisition to visualization, with logical rigor and strong practicality.

[0103] S102, if the dynamic prediction result of the deep learning model on the interference source distribution map exceeds the preset threshold, a spatio-temporal change trend map is generated.

[0104] If the prediction result generated by deep learning exceeds the preset threshold, the time series data in the dynamic prediction is extracted through time series analysis to obtain the change trend characteristics. According to the change trend characteristics, the time series data is processed by convolution smoothing method to obtain the smoothed trend sequence. Through the smoothed trend sequence, the distribution offset of each time point is calculated to determine the spatio-temporal distribution characteristics. For the spatio-temporal distribution characteristics, a two-dimensional spatio-temporal grid data is generated by using grid division technology to obtain the preliminary trend distribution. According to the preliminary trend distribution, the grid data is processed by using color coding technology to generate a visual trend map. If the distribution offset in the visual trend map exceeds the preset range, the grid data is adjusted by using clustering algorithm to obtain the optimized spatio-temporal change trend map. Through the optimized spatio-temporal change trend map, the key change area is extracted to determine the final trend distribution.

[0105] Specifically, if the prediction result generated by deep learning exceeds the preset threshold, the data characteristics can be further mined through time series analysis.

[0106] For example, in the wireless communication signal monitoring scene, it is assumed that the deep learning model predicts that the interference intensity in a certain area is 0.9, which exceeds the threshold 0.7. When performing time series analysis.

[0107] Specifically, the time series can be extracted from the monitoring data of 24 consecutive hours, and the data shows the interference intensity value every minute, such as 0.85, 0.89, 0.92, etc., showing a gradually rising trend.

[0108] Exemplarily, this rise may be related to the increase of base station load, reflecting the dynamic change characteristics. When processing the time series by using convolution smoothing method.

[0109] In one possible implementation, a convolution kernel with a length of 5 can be used, and the sliding window covers the data of the adjacent 5 time points.

[0110] For example, the original sequence 0.85, 0.89, 0.92, 0.90, 0.87 may be smoothed to 0.87, 0.89, 0.90, 0.89, 0.88, and the fluctuation is reduced, and the trend is more gentle.

[0111] It can be understood that this helps to eliminate short-term noise interference and highlight long-term change rules. When calculating the distribution offset through the smoothed trend sequence.

[0112] Preferably, the deviation of the value of each time point from the average value can be compared.

[0113] For example, the average value is 0.89, the value of a certain point is 0.92, and the deviation is 0.03. If the deviation of multiple points in succession increases, such as 0.03, 0.05, and 0.07, it indicates that the interference is distributed in space-time and deviates.

[0114] It should be noted that this deviation may be related to the movement of the signal source or the change of the environment, and provides a basis for subsequent analysis. For the space-time distribution characteristics, a two-dimensional grid data is generated by using a grid division technique.

[0115] Specifically, the monitoring area can be divided into a 10x10 grid, and each grid corresponds to the average value of the deviation within one hour.

[0116] For example, the value of a certain grid is 0.05, and the adjacent grid is 0.03, forming a preliminary trend distribution.

[0117] In one embodiment, the grid division can also be combined with the sensor position to further refine the spatial accuracy. When processing the grid data, a color coding technique is applied.

[0118] For example, a deviation greater than 0.06 can be set as red, 0.03 to 0.06 as yellow, and less than 0.03 as green. In the generated trend chart, the red area is concentrated in the center of the grid, indicating that the interference is enhanced.

[0119] It can be understood that this visualization facilitates rapid identification of abnormal areas. If the distribution deviation exceeds the preset range, such as greater than 0.08, the grid data is adjusted by using a clustering algorithm.

[0120] For example, the K-means algorithm is used to divide the grid into two categories: areas with high and low deviations. After adjustment, the boundary of the red area is clearer, and the optimized trend chart reflects that the interference is concentrated in a certain direction.

[0121] In one embodiment, this optimization can improve the positioning accuracy. When extracting the key change area from the optimized trend chart.

[0122] Specifically, the coordinates corresponding to the red area can be locked, such as (3, 4) to (5, 6).

[0123] For example, through analysis from multiple aspects, the coordinate (4, 5) has a deviation of 0.09 for three consecutive hours, and the adjacent area also has a high value, indicating that this is the main change source.

[0124] Preferably, this provides a clear direction for interference source tracking. When determining the final trend distribution.

[0125] In one possible implementation, a panoramic view containing dynamic changes can be generated by combining time and space characteristics.

[0126] For example, the trend chart shows that the interference spreads from (4, 5) to (6, 7) at a speed of about 1 grid per hour.

[0127] It should be noted that such distribution not only reveals the change path, but also provides support for predicting future trends, enhancing the practicality of the system.

[0128] S103, according to the spatio-temporal change trend chart, using digital elevation model to calculate signal path blocking point, determine the range of shielding influence area.

[0129] Through the digital elevation model to obtain the elevation data, calculate the distribution of signal path in the spatio-temporal change trend chart, get the path change characteristics. Using path change characteristics to analyze the height difference on the signal path, determine the blocking point position, determine the potential shielding point set. Get the shielding influence data in the blocking point set, calculate the boundary of the shielding area through the elevation data, get the shielding area range. For the shielding area range, extract the terrain features within the influence range, judge whether the signal path is completely blocked, determine the blocking degree. Through the blocking degree analysis of the distribution law of shielding influence, get the accurate boundary of the influence range. Using random forest algorithm to classify the shielding area within the influence range, judge the shielding severity of different areas, determine the final shielding influence area range. According to the final shielding influence area range, adjust the optimization layout of the signal path through the elevation data, get the adjusted path distribution.

[0130] Specifically, when obtaining the elevation data through the digital elevation model.

[0131] It can be understood that this method relies on the elevation information of the terrain to construct the basic data of the signal path.

[0132] For example, in a mountainous area scene, assuming that the elevation data range of a certain area is between 500 meters and 1200 meters above sea level, after extraction through the elevation model, the elevation value of each grid point can be obtained for subsequent path analysis. When calculating the distribution of signal path in the spatio-temporal change trend chart.

[0133] Exemplarily, the propagation trajectory of the signal from the transmitting point to the receiving point can be drawn based on the elevation data, and the change characteristics of the path with the terrain fluctuation are judged in combination with the time dimension.

[0134] Specifically, if the signal path passes through a peak with an elevation of 800 meters, the path change characteristics may show a sharp rise in height followed by a drop, which directly affects subsequent analysis. When using path change characteristics to analyze the height difference on the signal path.

[0135] In one possible implementation, significant changes can be identified by comparing the elevation values of points on the path.

[0136] For example, the path start elevation is 600 meters, and after a period of time, it rises to 900 meters, and then falls to 700 meters. This height difference suggests that there may be a blocking point. When determining the location of the blocking point,

[0137] Preferably, a height difference threshold value, such as 50 meters, can be set. If the value exceeds the threshold, it is marked as a potential blocking point, and then a set of blocking points is formed.

[0138] It should be noted that the determination of the blocking point not only depends on the height, but also considers factors such as terrain slope, which can more accurately reflect the actual situation. When obtaining the blocking impact data in the blocking point set,

[0139] For example, the terrain shadow range near the blocking point can be analyzed through elevation data. Assuming that the elevation of a certain blocking point is 1000 meters, its impact range may cover the area within 200 meters below. When calculating the boundary of the blocking area,

[0140] In one embodiment, the gradient change of the elevation data can be used to determine the shadow boundary, such as the area with a slope less than 10 degrees as the boundary line. This method helps to accurately divide the blocking range and facilitates subsequent terrain feature extraction. When extracting terrain features for the blocking area range,

[0141] It can be understood that the terrain features may include slope direction, slope, and other information.

[0142] For example, the slope of a certain blocking area is 30 degrees, and the slope direction is north, which may cause the signal to be completely blocked. When analyzing the distribution of the blocking impact through the blocking degree,

[0143] Specifically, the blocking degree can be divided into three categories: slight, moderate, and severe. If 80% of the points on the path are blocked, it is determined as severe blocking. This classification helps to quantify the accurate boundary of the impact range. When classifying the blocking area using a random forest algorithm,

[0144] In one embodiment, slope, elevation difference, and other feature data can be input, and the blocking severity of different areas can be output.

[0145] For example, a certain area has an elevation difference of 100 meters and a steep slope, which is classified as a high blocking area. This classification method can effectively distinguish the strength of the blocking impact. After determining the final blocking impact area range, the optimization layout of the signal path is adjusted through the elevation data.

[0146] For example, the path can be adjusted from the blocking area with an elevation of 900 meters to the open area with an elevation of 700 meters, avoiding the main blocking point. This adjustment can significantly improve the stability of signal transmission.

[0147] In one possible implementation, the distribution of the adjusted path can further verify its effect.

[0148] For example, the elevation change of the new path is controlled within 50 meters, and the shielding points are reduced to 20% of the original, thereby ensuring wider signal coverage. The implementation of such an optimized layout not only improves the rationality of path planning, but also provides more favorable topographic conditions for signal transmission.

[0149] S104, extract electromagnetic interference intensity data from the shielding influence area range, and decompose the interference frequency principal component by a spectrum analysis algorithm.

[0150] By determining the range of the shielding influence, the intensity data corresponding to the electromagnetic interference is obtained by using a measurement tool. The interference frequency characteristics are extracted from the intensity data, and the initial component result is obtained by decomposing the spectrum analysis algorithm. The frequency analysis is performed on the initial component result, and the distribution characteristics of the principal component are determined. If the principal component exceeds the preset threshold, the independent characteristics of the interference frequency are obtained by component decomposition. The source direction of the electromagnetic interference is determined according to the independent characteristics, and the interference source coordinates are obtained by using a positioning algorithm. The boundary range of the shielding influence is determined by comparing the interference source coordinates with the range. The intensity data in the boundary range is obtained, and the optimized interference frequency component is obtained by using a filtering tool.

[0151] Specifically, after determining the range of the shielding influence, the intensity data corresponding to the electromagnetic interference is obtained by using a measurement tool.

[0152] For example, in a mountainous communication scenario, an electromagnetic wave intensity meter can be used to collect signal intensity at different locations to obtain a set of data reflecting the interference distribution.

[0153] For example, suppose the intensity value measured in a certain area is -70dBm to -50dBm, which indicates that there may be an interference source. When extracting the interference frequency characteristics from the intensity data.

[0154] It can be understood that the interference often appears as abnormal fluctuations of specific frequencies.

[0155] In one possible implementation, the time domain signal is converted to the frequency domain by using fast Fourier transform to decompose the frequency component. For example, a significant peak value is detected in the 2.4GHz frequency band, indicating that there may be Wi-Fi interference. The frequency analysis on the initial component result is the basis for subsequent analysis.

[0156] Specifically, whether the main interference frequency is concentrated in a certain range can be observed through a spectrum diagram. Assuming that the threshold is set to -60dBm, if the intensity of a certain frequency component reaches -55dBm, it exceeds the threshold and needs to be further decomposed.

[0157] Preferably, independent features are extracted by principal component analysis, such as separating two independent interference sources at 2.4 GHz and 5 GHz. This method can clearly distinguish the contribution of multi-source interference. When determining the source direction of electromagnetic interference according to independent features, the positioning algorithm plays an important role.

[0158] In one embodiment, a triangular positioning method can be used to calculate the coordinates of the interference source by measuring the intensity difference at three points in the area.

[0159] For example, the measurement points A, B, and C record the intensity and angle, respectively, and finally calculate that the interference source is located about 500 meters northeast of the area. This not only determines the location of the interference, but also facilitates subsequent troubleshooting. By comparing the coordinates of the interference source with the range of the shielding area, the boundary range can be determined.

[0160] For example, if the interference source is close to the ridge, the shielding boundary may extend along the mountain, and the influence range will be adjusted accordingly. After obtaining the intensity data in the boundary range, it is crucial to use filtering tools to optimize the interference frequency components.

[0161] It should be noted that a low-pass filter can remove high-frequency noise and retain the main interference characteristics.

[0162] For example, after filtering, it is found that the 2.4 GHz component is still prominent, proving that it is the core interference frequency. This processing can improve data quality and provide a reliable basis for subsequent analysis.

[0163] In one embodiment, the optimized frequency components can be used to draw an interference heat map to visually display the interference distribution in the area and help technicians quickly locate the problem area.

[0164] For example, when refining the positioning algorithm from multiple aspects, the accuracy can be verified in combination with a directional antenna. If the signal is strongest when the antenna is pointing northeast, consistent with the results of triangular positioning, the positioning is more reliable.

[0165] Preferably, a time dimension analysis can also be introduced to observe the change of interference intensity over time and ensure that the results are not affected by transient fluctuations.

[0166] For example, the data measured in the morning and evening is consistent, indicating that the interference source is stable. This multi-angle verification enhances the rigor of the scheme and provides accurate basis for interference management, which helps to improve communication stability.

[0167] S105, if the electromagnetic interference intensity corresponding to the principal component component is higher than the preset threshold, adjust the adaptive filter parameter to generate an optimized filtered signal.

[0168] If the principal component of the electromagnetic interference exceeds the preset threshold, the component analysis result is determined by analyzing the interference intensity. The characteristic data of the interference intensity is obtained through the component analysis result, and it is judged whether the filter needs to be adjusted. If the characteristic data exceeds the threshold judgment standard, the parameters of the adaptive filter are adjusted to obtain a preliminary filtered signal. According to the preliminary filtered signal, an optimized signal is generated by using a signal generation technology. The residual component of the electromagnetic interference is extracted from the optimized signal, and it is judged whether the residual component is lower than the preset threshold. If the residual component is higher than the preset threshold, the filter parameters are adjusted by iteration to obtain a final optimized signal. According to the final optimized signal, the suppression degree of the electromagnetic interference is determined, and a stable output signal is generated.

[0169] In a possible implementation, when the principal component of the electromagnetic interference exceeds the preset threshold, the interference intensity is analyzed.

[0170] For example, it is assumed that the interference intensity reaches 100 units in a certain frequency band, and the threshold is set to 80 units, which indicates that further processing is needed. The peak characteristics of the interference can be extracted from the spectrum data to observe the distribution law on the time axis, such as 3 obvious intensity peaks per second. This analysis helps to judge whether the interference has periodic characteristics, which provides a basis for subsequent filtering.

[0171] Specifically, when the characteristic data of the interference intensity is obtained through the component analysis result, the frequency range and amplitude variation of the interference can be focused on.

[0172] Exemplarily, if the characteristic data shows that the interference is concentrated in the interval of 10 kHz to 15 kHz, and the amplitude fluctuation is between 50 and 120 units, which exceeds the threshold standard of 20 units, the filter needs to be adjusted. At this time, the cutoff frequency of the adaptive filter can be dynamically adjusted to below 15 kHz according to the interference range to preliminarily weaken the interference signal. This adjustment method can quickly respond to the change of interference characteristics and maintain the stability of the system.

[0173] It should be noted that after the preliminary filtered signal is generated, the process of optimizing the signal by using the signal generation technology is crucial.

[0174] For example, by superimposing a reverse compensation signal, the interference peak value is reduced from 100 units to 30 units, close to the threshold requirement. This technique can effectively smooth the signal waveform and avoid the influence of interference on subsequent processing. The optimized signal can also provide clearer basic data for residual component analysis.

[0175] In an embodiment, when the residual component of the electromagnetic interference is extracted from the optimized signal, it can be judged by spectrum comparison.

[0176] For example, the residual component in the post-optimized signal still has a strength of 20 units at 12 kHz, which is higher than the preset threshold of 10 units. At this time, it is necessary to iteratively adjust the filter parameters. One possible implementation is to reduce the gain of the filter by 0.5 times and simultaneously narrow the bandwidth to 2 kHz, gradually reducing the residual interference. This iterative process can significantly improve the signal quality and ensure that the interference is effectively suppressed.

[0177] For example, after the final optimized signal is generated, the degree of suppression of electromagnetic interference can be determined by observing the stability of the signal. Assuming that the original interference signal fluctuates in the range of 80 to 120 units, and after processing, it is reduced to 5 to 10 units, indicating that the degree of suppression is more than 90%. At this time, the generated stable output signal can be directly used for downstream equipment to avoid misjudgment or failure caused by interference. This improvement in stability is obviously helpful to the overall performance of the system.

[0178] Preferably, in actual scenarios, the parameter adjustment of the adaptive filter can be combined with real-time monitoring data.

[0179] For example, when the environmental noise suddenly increases, causing the interference strength to rise from 100 units to 150 units, the filter can automatically increase the cutoff frequency to 18 kHz and enhance the attenuation effect. This flexibility enables the system to maintain high efficiency in complex environments and reduces the need for human intervention.

[0180] It can be understood that the reliability of the signal will be significantly improved when the residual component is below the preset threshold.

[0181] For example, in a communication device, after the interference is reduced from 50 units to 8 units, the bit error rate of data transmission is reduced from 5% to 0.1%, directly improving the communication quality. This effect is particularly important in high-precision application scenarios.

[0182] In one embodiment, after generating the stable output signal, the long-term stability thereof can be further verified.

[0183] For example, after 24 hours of continuous operation, the signal strength fluctuation is maintained within 5 units, proving the persistence of the interference suppression scheme. This reliability provides a guarantee for the continuous operation of the device and reduces maintenance costs.

[0184] S106, performing space-time encoding processing on the optimized filter signal, using a two-dimensional convolution algorithm to eliminate weather influencing factors, and obtaining an anti-interference enhanced signal.

[0185] The input signal is optimally filtered to obtain a filtered signal using preset filter parameters. The filtered signal is subjected to space-time coding to generate a coded signal through coding techniques. A two-dimensional convolution algorithm is used to perform convolution operation on the coded signal to obtain a convolution result. The weather influencing factors are removed through the convolution result to obtain a preliminary enhanced signal. If there is residual interference in the preliminary enhanced signal, an anti-interference detection method is used to determine the interference degree to obtain a detection result. The preliminary enhanced signal is adjusted according to the detection result, and a weighting process is used to obtain a final enhanced signal. The effectiveness of the processing flow is verified through the final enhanced signal to determine the anti-interference enhanced signal.

[0186] Specifically, when the input signal is optimally filtered, using preset filter parameters is a key step.

[0187] For example, in a wireless communication system, the input signal may be affected by the superposition of multipath effects and noise.

[0188] It can be understood that the preset filter parameters are usually designed based on historical data or channel characteristics, such as setting a low-pass filter with a cutoff frequency of 5 kHz to preliminarily weaken high-frequency noise and obtain a filtered signal. This way can preserve the main features of the signal while reducing the ambiguity caused by interference.

[0189] In one possible implementation, the filtered signal is subjected to space-time coding to further improve the spatial and temporal resolution of the signal.

[0190] Specifically, space-time block coding techniques can be used to divide the filtered signal into multiple time slots and assign them to different antenna units.

[0191] For example, a 4-antenna system can divide the signal into 4 time slots, each time slot being 2 milliseconds apart, and generate a coded signal through coding techniques. This coding helps to distinguish different path signal components in subsequent processing.

[0192] Preferably, when a two-dimensional convolution algorithm is used to perform convolution operation on the coded signal, the spatial and temporal characteristics of the signal are extracted.

[0193] For example, in radar signal processing, a 3x3 convolution kernel can be used to perform sliding window operation on the coded signal to obtain a convolution result. This method can highlight the local patterns of the signal and remove the effects of random disturbances.

[0194] It should be noted that the size and weight of the convolution kernel can be adjusted according to the signal characteristics, such as increasing the kernel size to 5x5 in a strong interference scenario. Removing weather influencing factors through the convolution result is an important link in practical applications.

[0195] In an embodiment, if the signal is affected by rain fade, the weather-induced attenuation characteristics can be identified by analyzing the low-frequency components in the convolution result.

[0196] For example, when detecting an abnormally enhanced component with a frequency lower than 1 kHz, it can be classified as weather interference and filtered out to obtain a preliminary enhanced signal. This processing can effectively reduce the masking of environmental factors on the signal.

[0197] For example, if there is residual interference in the preliminary enhanced signal, an anti-interference detection method is used to determine the interference level.

[0198] Specifically, by calculating the peak-to-mean ratio of the signal, for example, the peak value reaches 10 and the mean value is 2, it can be concluded that the interference ratio is high. When the detection result shows that the interference level exceeds the standard, the signal needs to be further adjusted. This detection method is intuitive and easy to implement, and can quickly locate the problem. According to the detection result, adjusting the preliminary enhanced signal, weighted processing is a common means.

[0199] In an embodiment, different weights can be assigned to different frequency bands of the signal, for example, a weight of 0.8 is assigned to the low frequency band and a weight of 0.3 is assigned to the high frequency band, to suppress residual interference and obtain the final enhanced signal. This weighting method can balance the clarity and integrity of the signal.

[0200] It can be understood that verifying the effectiveness of the processing flow through the final enhanced signal usually requires comparing the differences between the input signal and the output signal.

[0201] For example, if the signal-to-noise ratio of the input signal is 5 dB and the final enhanced signal is improved to 15 dB, it indicates that the anti-interference ability is significantly enhanced. This verification method can intuitively reflect the practical value of the flow and provide a basis for subsequent optimization.

[0202] S107, extract the transmission signal from the adjusted encoded signal, optimize the bandwidth and power parameters using a dynamic resource allocation algorithm, and output a stable communication signal.

[0203] The transmission signal is obtained from the encoded signal by the decoder to obtain the initial transmission data. A preset threshold is used to judge the integrity of the initial transmission data to obtain an effective transmission signal. The bandwidth parameter of the effective transmission signal is adjusted by a dynamic resource allocation algorithm to obtain an optimized bandwidth signal. The power parameter of the optimized bandwidth signal is adjusted by a dynamic resource allocation algorithm to obtain an optimized power signal. According to the characteristics of the optimized power signal, a signal processing technique is used to smooth the noise to obtain a smoothed communication signal. The stability of the smoothed communication signal is detected by a channel simulation tool to obtain a stable communication signal.

[0204] Specifically, the transmission signal is obtained from the encoded signal by the decoder to obtain the initial transmission data, which is a key link in the communication system.

[0205] Exemplarily, in wireless communication, a decoder can extract a bit stream from a received encoded signal based on a preset modulation mode, such as QPSK or 16QAM. Assuming that the transmission rate is 10 Mbps, the decoder recovers the initial transmission data containing data frames by identifying the amplitude and phase of the signal. A preset threshold is used to judge the integrity of the initial transmission data, and an effective transmission signal is obtained, which ensures the data quality.

[0206] In a possible implementation, the bit error rate threshold can be set to 10-5, and if the bit error rate of the initial transmission data is lower than the threshold, the data is considered complete.

[0207] For example, in a scenario where the data packet size is 1024 bits, if no more than 1 bit of error is detected, the effective transmission signal is determined. This method can effectively filter out reliable data and improve the accuracy of subsequent processing. The bandwidth parameters of the effective transmission signal are adjusted by a dynamic resource allocation algorithm to obtain an optimized bandwidth signal, which is suitable for resource-constrained environments.

[0208] Specifically, the dynamic resource allocation algorithm can adjust the bandwidth in real time according to the channel state information (CSI). Assuming that the current channel capacity is 20 MHz, the algorithm detects a high load period and expands the bandwidth from 5 MHz to 10 MHz to ensure transmission efficiency. This flexibility can significantly improve the throughput of the system. The power parameters of the optimized bandwidth signal are adjusted by a dynamic resource allocation algorithm to obtain an optimized power signal, which further optimizes resource utilization.

[0209] Preferably, the algorithm adjusts the transmit power according to the signal strength and interference level.

[0210] For example, when the signal attenuation is large at a distance of 500 meters from the base station, the power is increased from 10 dBm to 15 dBm to ensure signal coverage. This method can save energy consumption and maintain communication quality. According to the characteristics of the optimized power signal, a signal processing technique is used to smooth the noise to obtain a smoothed communication signal, which enhances the clarity of the signal.

[0211] In an embodiment, a low-pass filter can be used to filter out high-frequency noise by setting the cutoff frequency to 1.2 times the signal bandwidth, such as 12 MHz. For example, if the noise power spectral density is -174 dBm / Hz, after smoothing, the signal-to-noise ratio is increased from 10 dB to 15 dB, which helps to reduce the error code. The stability of the smoothed communication signal is detected by a channel simulation tool to obtain a stable communication signal, which ensures the reliability of the system.

[0212] It can be understood that the channel simulation tool simulates scenarios such as multipath fading and Doppler shift.

[0213] For example, in a moving environment at a speed of 60 km / h, the simulation result shows that the signal amplitude fluctuation is less than 2 dB, proving its stability. This detection provides a reliable basis for actual deployment.

[0214] It should be noted that the implementation method of each link is closely connected, from decoding to stability detection, forming a complete signal processing chain.

[0215] For example, in bandwidth and power adjustment, the dynamic algorithm considers both user density and channel congestion degree, and assumes that the number of users increases from 50 to 100, the bandwidth and power will be optimized simultaneously. This multi-faceted support design not only ensures the comprehensiveness of the scheme, but also enriches the feasibility of implementation through specific scenarios.

[0216] Preferably, this method can also adapt to different communication environments and improve the robustness of the system.

[0217] Embodiment 2:

[0218] The application also provides a low-altitude helicopter communication navigation signal enhancement system based on intelligent scheduling, comprising:

[0219] A time-frequency feature extraction module is configured to acquire time-frequency feature data collected by a sensor array.

[0220] An interference source parameter extraction module is configured to extract interference source parameters using a fast Fourier transform algorithm.

[0221] An interference source distribution map generation module is configured to generate an interference source distribution map.

[0222] A spatio-temporal change trend map generation module is configured to generate a spatio-temporal change trend map if the dynamic prediction result of the deep learning model on the interference source distribution map exceeds a preset threshold.

[0223] A signal path block point calculation module is configured to calculate signal path block points using a digital elevation model based on the spatio-temporal change trend map.

[0224] An electromagnetic interference intensity extraction module is configured to determine a shielding influence area range and extract electromagnetic interference intensity data from the shielding influence area range.

[0225] An optimized filtered signal generation module is configured to decompose interference frequency principal component components through a spectral analysis algorithm, and adjust adaptive filter parameters to generate an optimized filtered signal if the electromagnetic interference intensity corresponding to the principal component components is higher than a preset threshold.

[0226] The anti-interference enhanced signal generation module is used for space-time encoding processing on the optimized filter signal, adopts a two-dimensional convolution algorithm to eliminate weather influencing factors, and obtains an anti-interference enhanced signal.

[0227] The stable communication signal output module is used for extracting a transmission signal from the adjusted encoded signal, adopting a dynamic resource allocation algorithm to optimize bandwidth and power parameters, and outputting a stable communication signal.

[0228] It should be noted that the above enumeration is only several specific embodiments of the present application. Apparently, the present application is not limited to the above embodiments, and there are many variations. All variations that can be directly derived or inferred from the disclosed content by those of ordinary skill in the art should be considered as the protection scope of the present application.

Claims

1. A method for enhancing communication and navigation signals for low-altitude helicopters based on intelligent scheduling, characterized in that, include: Acquire time-frequency characteristic data collected by the sensor array, extract interference source parameters, and generate an interference source distribution map; If the dynamic prediction result of the interference source distribution map exceeds a preset threshold, a spatiotemporal change trend map is generated. Based on the spatiotemporal trend map, calculate the signal path blocking points and determine the range of the obstruction-affected area; Electromagnetic interference intensity data is extracted from the area affected by the blockage, and the main components of the interference frequency are decomposed to obtain the main components of the interference frequency. If the electromagnetic interference intensity corresponding to the principal component is higher than a preset threshold, the adaptive filter parameters are adjusted to generate an optimized filtered signal. The optimized filtered signal is subjected to space-time coding processing to obtain an anti-interference enhanced signal; The transmission signal is extracted from the adjusted encoded signal, the bandwidth and power parameters are optimized, and a stable communication signal is output.

2. The method for enhancing low-altitude helicopter communication and navigation signals based on intelligent scheduling according to claim 1, characterized in that, The time-frequency characteristic data collected by the sensor array are acquired, and the interference source parameters are extracted using the fast Fourier transform algorithm to generate an interference source distribution map.

3. The method for enhancing low-altitude helicopter communication and navigation signals based on intelligent scheduling according to claim 1, characterized in that, Based on the spatiotemporal trend map, the signal path blocking point is calculated using a digital elevation model to determine the range of the obstruction area.

4. The method for enhancing low-altitude helicopter communication and navigation signals based on intelligent scheduling according to claim 1, characterized in that, Electromagnetic interference intensity data is extracted from the area affected by the blockage, and the main components of the interference frequency are obtained by decomposition using a spectrum analysis algorithm.

5. The method for enhancing low-altitude helicopter communication and navigation signals based on intelligent scheduling according to claim 1, characterized in that, The optimized filtered signal is subjected to space-time coding processing, and a two-dimensional convolution algorithm is used to eliminate weather-related factors to obtain an anti-interference enhanced signal.

6. The method for enhancing low-altitude helicopter communication and navigation signals based on intelligent scheduling according to claim 1, characterized in that, The transmission signal is extracted from the adjusted encoded signal, and the bandwidth and power parameters are optimized using a dynamic resource allocation algorithm to output a stable communication signal.

7. The method for enhancing low-altitude helicopter communication and navigation signals based on intelligent scheduling according to claim 1, characterized in that, If the dynamic prediction result of the interference source distribution map using a deep learning model exceeds a preset threshold, a spatiotemporal change trend map is generated.

8. A low-altitude helicopter communication and navigation signal enhancement system based on intelligent scheduling, characterized in that, include: The time-frequency feature extraction module is used to acquire time-frequency feature data collected by the sensor array; The interference source parameter extraction module is used to extract interference source parameters using the Fast Fourier Transform algorithm. The interference source distribution map generation module is used to generate interference source distribution maps; The spatiotemporal change trend map generation module is used to generate a spatiotemporal change trend map if the prediction result of the deep learning model on the dynamics of the interference source distribution map exceeds a preset threshold. The signal path blocking point calculation module is used to calculate the signal path blocking point based on the spatiotemporal change trend map using a digital elevation model. The electromagnetic interference intensity extraction module is used to determine the range of the area affected by the obstruction and extract electromagnetic interference intensity data from the range of the area affected by the obstruction. The optimized filter signal generation module is used to decompose the interference frequency principal component components through a spectrum analysis algorithm. If the electromagnetic interference intensity corresponding to the principal component components is higher than a preset threshold, the adaptive filter parameters are adjusted to generate an optimized filter signal. An anti-interference enhancement signal generation module is used to perform spatiotemporal coding processing on the optimized filtered signal, and to use a two-dimensional convolution algorithm to eliminate weather influence factors and obtain an anti-interference enhancement signal. The stable communication signal output module is used to extract the transmission signal from the adjusted encoded signal, optimize the bandwidth and power parameters using a dynamic resource allocation algorithm, and output a stable communication signal.

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