Intelligent communication antenna system capable of self-adapting to signal intensity

Abstract

The utility model discloses an intelligent communication antenna system capable of self-adapting to signal intensity, which is characterized by comprising an environment sensing module, a signal processing module and a fuzzy calculation adaptation module, and the environment sensing module, the signal processing module and the fuzzy calculation adaptation module are electrically connected. The environment sensing module comprises a signal state monitoring unit and a physical environment sensing unit; according to the utility model, through the arrangement of the environment sensing module and the fuzzy calculation adaptation module, if the attitude of the unmanned aerial vehicle changes in the driving process, the system can realize the dynamic prediction of the unmanned aerial vehicle according to fuzzy calculation in combination with the effects of multiple sensors such as a radar and a sensor, and can adjust the antenna parameters in time; according to the device, dynamic adaptation is realized through closed-loop feedback and sensing-processing-adjustment, so that the antenna signal strength can be adaptively switched when the attitude of the unmanned aerial vehicle changes quickly.

Description

Technical Field

[0001] This utility model relates to the field of unmanned aerial vehicle (UAV) communication antenna technology, specifically to an intelligent communication antenna system that adapts to signal strength. Background Technology

[0002] The drone antenna is responsible for signal transmission and reception between the drone and the remote controller or ground station. When the drone sends signals to the remote controller, it provides information about its flight status and displays it on the remote controller's screen, such as altitude, speed, battery level, and GPS coordinates. The pilot can monitor the drone's flight and make necessary adjustments.

[0003] Initially, UAV antenna systems used simple antennas (such as monopole and dipole antennas). Due to limitations in size and performance, they gradually evolved towards multi-band, high-gain, and lightweight designs. Lightweight materials, MIMO, and smart antennas were increasingly adopted. Multiple antennas were used to improve channel capacity, and beamforming (such as phased arrays) was combined to dynamically track signal sources and enhance anti-interference capabilities.

[0004] A search revealed an antenna, a wireless signal processing device, and a drone in patent application publication number CN113555680A. The antenna includes: a substrate with a substrate surface; a first radiating portion and a second radiating portion disposed on the substrate surface; and a directing element having a first length within a first preset range to enhance the radiation of the first radiating portion in a target direction. This antenna can deflect high-frequency signals towards the target direction without interfering with the low-frequency signals of the second radiating portion, thereby improving the directivity of the high-frequency signals.

[0005] This drone antenna system primarily maintains normal signal reception and transmission by improving the directivity of high-frequency signals and minimizing signal loss or interference. However, the improvement in signal strength is easily limited when the drone's attitude changes or the environment alters. Utility Model Content

[0006] The purpose of this invention is to provide an intelligent communication antenna system with adaptive signal strength. By setting up a fuzzy calculation adaptation module, the speed and direction of the UAV are calculated in advance, and the signal strength is automatically adjusted according to environmental changes.

[0007] To achieve the above objectives, this utility model provides the following technical solution: an intelligent communication antenna system with adaptive signal strength, comprising an environment sensing module, a signal processing module, and a fuzzy computing adaptation module, wherein the environment sensing module, the signal processing module, and the fuzzy computing adaptation module are all electrically connected;

[0008] The environmental sensing module includes a signal status monitoring unit and a physical environment sensing unit, and the signal status monitoring unit is electrically connected to the physical environment sensing unit.

[0009] The signal processing module includes a channel estimation modeling unit, an intelligent beamforming unit, and an interference management unit. The channel estimation modeling unit is electrically connected to the intelligent beamforming unit and the interference management unit.

[0010] The fuzzy computing adaptation module includes a first RSSI sensor, an inertial unit, a first millimeter-wave radar, and a signal switching unit. The signal switching unit is electrically connected to the first RSSI sensor, the inertial unit, and the first millimeter-wave radar through the main control board.

[0011] Preferably, the inertial unit includes an inertial sensor and an inertial measurement circuit. The inertial measurement circuit includes an ENC03-MB chip. The output terminal of the ENC03-MB chip is connected to a capacitor C1, and the feedback terminal of the ENC03-MB chip is connected to a capacitor C3. A resistor R3 is connected between the capacitor C1 and the capacitor C3.

[0012] Preferably, the inertial measurement circuit further includes an operational amplifier MCP601. One end of the resistor R3 is electrically connected to the non-inverting input of the operational amplifier MCP601. The inverting input of the operational amplifier MCP601 is connected to a resistor R2. The other end of the resistor R2 is connected to the connection terminal of the capacitor C3 and the ENC03-MB chip. The output terminal of the operational amplifier MCP601 is connected to a resistor R4 and a capacitor C4. The resistor R4 and the capacitor C4 are connected in parallel. One end of the resistor R4 is connected to the resistor R2.

[0013] Preferably, the signal status monitoring unit includes a second RSSI sensor for detecting the strength of the received signal, a CSI collector for acquiring channel status information, and a spectrum analyzer for scanning the millimeter-wave frequency band. The RSSI sensor is electrically connected to the CSI collector and the spectrum analyzer.

[0014] Preferably, the physical environment sensing unit includes a second millimeter-wave radar and a camera, which are electrically connected via a main control board.

[0015] Preferably, the channel estimation modeling unit includes a processor that uses the OMP algorithm to recover complete channel information from sparse sampling and a CNN-based time-delay Doppler spectrum prediction algorithm for signal processing, and the intelligent wave speed shaping unit includes a GaN-based phase shifter.

[0016] Preferably, the interference management unit includes a signal generator, an NLMS filter, and a power operational amplifier, wherein the signal generator is electrically connected to the NLMS filter and the power operational amplifier.

[0017] Preferably, the signal switching unit includes a microprocessor that receives instructions from the fuzzy computing adaptation module, an RF switch that is responsible for the physical switching of signals from different antenna ports or frequency bands based on the switching control signal generated by the microprocessor, a signal path manager that is responsible for ensuring the continuity of signal path impedance before and after switching and suppressing interference based on the switching control signal generated by the microprocessor, and a power monitor that monitors the switching signal strength in real time and feeds it back to the control unit after dynamically adjusting the switching path signal based on the signal path manager. The microprocessor, RF switch, signal path manager and power monitor are all electrically connected.

[0018] Compared with the prior art, the beneficial effects of this utility model are:

[0019] This invention, by setting up an environmental perception module and a fuzzy computing adaptation module, enables the UAV to dynamically predict changes in attitude during flight based on fuzzy computing and the combined effects of multiple sensors such as radar and other sensors, and adjust antenna parameters in a timely manner. Compared with the static directivity optimization of traditional antennas, this device achieves dynamic adaptation through closed-loop feedback, perception-processing-adjustment, so that the antenna signal strength can adaptively switch when the UAV's attitude changes rapidly. Attached Figure Description

[0020] Figure 1 This is a system diagram of this utility model;

[0021] Figure 2 This is the circuit diagram of the inertial measurement circuit of this utility model. Detailed Implementation

[0022] The technical solutions of the present utility model will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present utility model, and not all embodiments. Based on the embodiments of the present utility model, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present utility model.

[0023] Please see Figure 1-2 This utility model provides a technical solution: including an environmental perception module, a signal processing module, and a fuzzy computing adaptation module, wherein the environmental perception module, the signal processing module, and the fuzzy computing adaptation module are all electrically connected;

[0024] The environmental sensing module includes a signal status monitoring unit and a physical environment sensing unit, and the signal status monitoring unit is electrically connected to the physical environment sensing unit.

[0025] The signal processing module includes a channel estimation modeling unit, an intelligent beamforming unit, and an interference management unit. The channel estimation modeling unit is electrically connected to the intelligent beamforming unit and the interference management unit.

[0026] The system senses the environmental conditions and obstacles around the UAV through the environmental perception module. The channel estimation modeling unit in the signal processing module models the strength of the surrounding signals. When the UAV's attitude changes, the fuzzy computing adaptation module calculates the UAV's current speed, acceleration, and angle, calculates the strength of the environmental signals at the location it is about to fly to, and then controls the signal switching in advance through the main control board.

[0027] The fuzzy computing adaptation module includes a first RSSI sensor, an inertial unit, and a first millimeter-wave radar, all of which are electrically connected.

[0028] The inertial unit includes an inertial sensor and an inertial measurement circuit. The inertial measurement circuit includes an ENC03-MB chip. The output terminal of the ENC03-MB chip is connected to a capacitor C1, and the feedback terminal of the ENC03-MB chip is connected to a capacitor C3. A resistor R3 is connected between capacitor C1 and capacitor C3.

[0029] The inertial measurement circuit also includes an operational amplifier MCP601. One end of the resistor R3 is electrically connected to the non-inverting input of the operational amplifier MCP601. The inverting input of the operational amplifier MCP601 is connected to a resistor R2. The other end of the resistor R2 is connected to the connection terminal of the capacitor C3 and the ENC03-MB chip. The output terminal of the operational amplifier MCP601 is connected to a resistor R4 and a capacitor C4. The resistor R4 and the capacitor C4 are connected in parallel. One end of the resistor R4 is connected to the resistor R2.

[0030] The ENC03-MB chip, combined with the MCP601 operational amplifier, forms an angular velocity measurement circuit. The signal is filtered and amplified through a network of capacitors C1 and C3 and resistors R3 and R2 to ensure the accuracy of the attitude data.

[0031] The signal status monitoring unit includes a second RSSI sensor for detecting the strength of the received signal, a CSI collector for acquiring channel status information, and a spectrum analyzer for scanning the millimeter-wave frequency band. The RSSI sensor is electrically connected to the CSI collector and the spectrum analyzer.

[0032] The signal strength, channel status, and spectral interference are monitored in real time using a second RSSI sensor, a CSI data acquisition unit, and a spectrum analyzer, providing a data basis for subsequent adjustments.

[0033] The physical environment sensing unit includes a second millimeter-wave radar and a camera, which are electrically connected via a main control board. The second millimeter-wave radar and camera detect environmental factors such as obstacles and terrain changes, assisting in the identification of signal attenuation or interference sources.

[0034] The channel estimation modeling unit includes a compressed sensing algorithm that uses the OMP algorithm to recover complete channel information from sparse sampling and a processor that performs signal processing using a CNN-based time-delay Doppler spectrum prediction algorithm. The intelligent wave speed shaping unit includes a GaN-based phase shifter.

[0035] The OMP algorithm recovers complete channel information from sparse data, reducing sampling complexity; CNN predicts the time-delay Doppler spectrum, adapting to high-speed moving scenarios. The high-frequency characteristics of GaN phase shifters support fast beam steering. The combination of compressed sensing and CNN improves channel prediction accuracy while reducing data volume, adapting to complex multipath environments.

[0036] The channel estimation modeling unit uses the OMP compressed sensing algorithm to reduce sampling requirements and combines it with CNN to predict the time delay Doppler spectrum, thereby improving the efficiency of channel modeling in complex environments.

[0037] The interference management unit includes a signal generator, an NLMS filter, and a power operational amplifier. The signal generator is electrically connected to the NLMS filter and the power operational amplifier. The NLMS filter suppresses noise, the signal generator generates a reverse interference signal, and the power amplifier enhances the effective signal power.

[0038] The signal switching unit includes a microprocessor that receives instructions from the fuzzy computing adaptation module, an RF switch that is responsible for the physical switching of signals at different antenna ports or frequency bands based on the switching control signal generated by the microprocessor, a signal path manager that is responsible for ensuring the continuity of signal path impedance before and after switching and suppressing interference based on the switching control signal generated by the microprocessor, and a power monitor that monitors the switching signal strength in real time and feeds it back to the control unit after dynamically adjusting the switching path signal based on the signal path manager. The microprocessor, RF switch, signal path manager and power monitor are all electrically connected.

[0039] By integrating the first RSSI sensor, inertial unit to measure attitude changes and millimeter-wave radar data, and using fuzzy logic for dynamic decision-making, the antenna parameters are adjusted in a timely manner by the signal switching unit based on the calculated data after fuzzy calculation of the UAV's speed, directional acceleration and other data, thus achieving the adaptive effect of the antenna.

[0040] In operation, when the drone's flight attitude changes or the environment ahead changes, the system uses a second millimeter-wave radar and camera to perceive the surrounding environment and obstacles. The channel estimation modeling unit uses the OMP compressed sensing algorithm to model the channel in the foreground environment, sensing the strength of surrounding signals. The fuzzy computing adaptation module calculates the drone's current speed, acceleration, and angle, and fuzzily calculates the antenna signal strength in the foreground location. Then, the microprocessor of the signal switching unit receives instructions from the fuzzy computing adaptation module, and the RF switch signal is responsible for the physical switching of signals at different antenna ports or frequency bands. Simultaneously, the impedance matching network in the signal path manager ensures the continuity of the signal path impedance before and after switching, reducing reflection loss. The filter bank suppresses out-of-band interference, and the digital step attenuator HMC624ALP5E dynamically adjusts the signal power of the switching path to control signal switching. After switching, the signal strength is monitored in real time by the power monitor and fed back to the control unit to optimize the switching strategy, achieving dynamic switching. Compared to the static directivity optimization of traditional antennas, this device is more conducive to the adaptive switching of antenna signal strength when the drone's attitude changes rapidly.

[0041] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent communication antenna system with adaptive signal strength, characterized in that: It includes an environment perception module, a signal processing module, and a fuzzy computing adaptation module, all of which are electrically connected. The environmental sensing module includes a signal status monitoring unit and a physical environment sensing unit, and the signal status monitoring unit is electrically connected to the physical environment sensing unit. The signal processing module includes a channel estimation modeling unit, an intelligent beamforming unit, and an interference management unit. The channel estimation modeling unit is electrically connected to the intelligent beamforming unit and the interference management unit. The fuzzy computing adaptation module includes a first RSSI sensor, an inertial unit, a first millimeter-wave radar, and a signal switching unit. The signal switching unit is electrically connected to the first RSSI sensor, the inertial unit, and the first millimeter-wave radar through the main control board.

2. The intelligent communication antenna system with adaptive signal strength according to claim 1, characterized in that: The inertial unit includes an inertial sensor and an inertial measurement circuit. The inertial measurement circuit includes an ENC03-MB chip. The output terminal of the ENC03-MB chip is connected to a capacitor C1, and the feedback terminal of the ENC03-MB chip is connected to a capacitor C3. A resistor R3 is connected between capacitor C1 and capacitor C3.

3. The intelligent communication antenna system with adaptive signal strength according to claim 2, characterized in that: The inertial measurement circuit also includes an operational amplifier MCP601. One end of the resistor R3 is electrically connected to the non-inverting input of the operational amplifier MCP601. The inverting input of the operational amplifier MCP601 is connected to a resistor R2. The other end of the resistor R2 is connected to the connection terminal of the capacitor C3 and the ENC03-MB chip. The output terminal of the operational amplifier MCP601 is connected to a resistor R4 and a capacitor C4. The resistor R4 and the capacitor C4 are connected in parallel. One end of the resistor R4 is connected to the resistor R2.

4. The intelligent communication antenna system with adaptive signal strength according to claim 3, characterized in that: The signal status monitoring unit includes a second RSSI sensor for detecting the strength of the received signal, a CSI collector for acquiring channel status information, and a spectrum analyzer for scanning the millimeter-wave frequency band. The RSSI sensor is electrically connected to the CSI collector and the spectrum analyzer.

5. The intelligent communication antenna system with adaptive signal strength according to claim 4, characterized in that: The physical environment sensing unit includes a second millimeter-wave radar and a camera, which are electrically connected through the main control board.

6. The intelligent communication antenna system with adaptive signal strength according to claim 5, characterized in that: The channel estimation modeling unit includes a compressed sensing algorithm that uses the OMP algorithm to recover complete channel information from sparse sampling and a processor that performs signal processing using a CNN-based time-delay Doppler spectrum prediction algorithm. The intelligent wave speed shaping unit includes a GaN-based phase shifter.

7. The intelligent communication antenna system with adaptive signal strength according to claim 6, characterized in that: The interference management unit includes a signal generator, an NLMS filter, and a power operational amplifier, wherein the signal generator is electrically connected to the NLMS filter and the power operational amplifier.

8. The intelligent communication antenna system with adaptive signal strength according to claim 1, characterized in that: The signal switching unit includes a microprocessor that receives instructions from the fuzzy computing adaptation module, an RF switch that is responsible for the physical switching of signals at different antenna ports or frequency bands based on the switching control signal generated by the microprocessor, a signal path manager that is responsible for ensuring the continuity of signal path impedance before and after switching and suppressing interference based on the switching control signal generated by the microprocessor, and a power monitor that monitors the switching signal strength in real time and feeds it back to the control unit after dynamically adjusting the switching path signal based on the signal path manager. The microprocessor, RF switch, signal path manager and power monitor are all electrically connected.

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