Unmanned aerial vehicle flight path positioning system based on radio detection

Abstract

The invention relates to the technical field of radio signal processing, in particular to an unmanned aerial vehicle flight path positioning system based on radio detection. The method comprises the steps that a signal acquisition unit acquires radio detection information propagated in a space based on an antenna capture module; the information processing unit is based on the radio detection information acquired by the signal acquisition unit and performs data processing on the radio detection information; the signal demodulation unit restores instructions and state information sent by the unmanned aerial vehicle through a Viterbi algorithm based on the radio detection information processed by the information processing unit; the detection positioning unit determines the position and the flight trajectory of the unmanned aerial vehicle based on the trajectory positioning module and the position positioning module. Radio detection information in a space is accurately collected through the antenna capturing module, angle measurement is performed and spatial resolution is improved in combination with a multi-antenna array technology, and high-precision positioning of the position of the unmanned aerial vehicle can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of radio signal processing, in particular to a UAV flight trajectory positioning system based on radio detection. Background Art

[0002] The UAV flight trajectory positioning system based on radio detection is a comprehensive technical solution that determines the position, speed and flight path of the UAV by monitoring and analyzing the radio signals emitted or reflected by the UAV. The system uses advanced wireless communication technology and signal processing algorithms, such as time difference of arrival (TDOA), time of arrival (TOA), angle of arrival (AOA) and received signal strength indication (RSSI), combined with global navigation satellite system (GNSS) data and inertial measurement unit (IMU) information, to achieve high-precision real-time tracking and positioning of the UAV. In addition, the system may also integrate computer vision and image processing technology to enhance target recognition and environmental perception capabilities. In order to ensure the reliability and security of the system, measures such as encrypted communication and spectrum management will be adopted to prevent illegal interference and eavesdropping. The entire system is not only suitable for civilian fields, such as agricultural monitoring, logistics distribution, film and television shooting, etc., but also widely used in military reconnaissance, border patrol, emergency rescue and other national security-related scenes, providing operators with important decision-making support tools. With the continuous development of UAV technology, this positioning system based on radio detection is also evolving, aiming to provide more efficient and accurate services to meet the increasingly complex airspace management and security needs.

[0003] In the existing UAV flight trajectory positioning system based on radio detection, the accuracy and reliability of the system may be affected in a complex electromagnetic environment. For example, the multipath effect caused by high-rise buildings in the city or the existence of strong interference sources may cause missed detection or false alarms for the detection of "low, slow and small" (low-altitude, slow and small) UAVs, especially in densely populated urban areas or forests and other areas with severe obstruction. The positioning method that relies on radio signals has specific requirements for the communication frequency and protocol of the UAV. For UAVs that use non-standard or encrypted communications, the detection and positioning effects will be greatly reduced. The response speed and processing power of the system may also face challenges when facing a large number of UAVs flying at the same time, resulting in insufficient real-time performance. Therefore, a UAV flight trajectory positioning system based on radio detection is designed. Summary of the invention

[0004] The purpose of the present invention is to provide a UAV flight trajectory positioning system based on radio detection to solve the problem raised in the above background technology that in the existing UAV flight trajectory positioning system based on radio detection, the accuracy and reliability of the system may be affected in a complex electromagnetic environment, such as the multipath effect caused by high-rise buildings in the city or the existence of strong interference sources. For the detection of "low, slow and small" (low-altitude, slow and small) UAVs, especially in densely populated urban areas or forests and other areas with severe obstruction, missed detection or false alarms may occur; the positioning method relying on radio signals has specific requirements on the communication frequency and protocol of the UAV. For UAVs using non-standard or encrypted communication, the detection and positioning effects will be greatly reduced; the response speed and processing capacity of the system may also face challenges when facing a large number of UAVs flying at the same time, resulting in insufficient real-time performance.

[0005] To achieve the above object, the present invention aims to provide a UAV flight trajectory positioning system based on radio detection, including a signal acquisition unit, which collects radio detection information propagated in space based on an antenna capture module;

[0006] An information processing unit, which performs data processing on the radio detection information collected by the signal collection unit;

[0007] A signal demodulation unit, wherein the signal demodulation unit restores the command and status information sent by the UAV through a Viterbi algorithm based on the radio detection information processed by the information processing unit;

[0008] A detection and positioning unit is used to determine the position and flight trajectory of the UAV based on the trajectory positioning module and the position positioning module.

[0009] As a further improvement of the technical solution, the specific steps involved in collecting the radio detection information propagated in the space based on the antenna capture module are:

[0010] S2.1. Select the antenna according to the frequency band used by the drone and the expected detection distance;

[0011] S2.2, deploy multiple antennas to form an array for angle measurement and improved spatial resolution;

[0012] S2.3, the received radio signal is processed by radio frequency front end;

[0013] S2.4, converting the analog signal into a digital signal for subsequent digital signal processing.

[0014] As a further improvement of the technical solution, in S2.4, the specific steps involved in converting the analog signal into a digital signal are:

[0015] S2.41, select sampling frequency f s Sampling is performed to ensure that all information of the drone communication signal can be captured;

[0016] Among them, the specific expression for sampling is:

[0017] x[n]=x(t)| t＝nTs ;

[0018] Where x[n] represents the nth sample value of a discrete-time signal; x(t) represents a continuous-time analog signal; T s represents the sampling period; n represents the sampling index; t represents the time;

[0019] S2.42, select the number of quantization bits N for quantization to balance accuracy and hardware cost;

[0020] Among them, the quantified expression is:

[0021]

[0022] Where q(x) represents the quantized discrete value; x represents the original analog signal value; V min Indicates the minimum voltage range of the input signal; Δ indicates the quantization step size; round indicates rounding;

[0023] S2.43, converting the quantized value into a binary code;

[0024] Among them, the expression for converting the quantized value into binary code is:

[0025]

[0026] b(q(x)) means converting the quantized discrete value (q(x)) into a binary string; bin(*) means converting the integer * into an N-bit binary string.

[0027] As a further improvement of the technical solution, the specific steps involved in performing data processing on the radio detection information collected by the signal acquisition unit are:

[0028] S4.1, preliminarily processing the collected digital signals;

[0029] S4.2, measure signal strength, Doppler shift, arrival time and arrival time difference for the estimation of the position and velocity of the UAV.

[0030] As a further improvement of the technical solution, in S4.1, the specific process of performing preliminary processing on the collected digital signal is as follows:

[0031] Use digital filters to remove unwanted frequency components;

[0032] For an input signal x[n], the output y[n] after the filter is expressed as a convolution operation:

[0033] y[n]=x[n]*h[n];

[0034] Where h[n] represents the impulse response of the filter; * represents the convolution operation;

[0035] Frame synchronization and carrier synchronization are achieved by capturing sequences and pilot symbols;

[0036] The received signal r(t) contains the synchronization sequence s(t), and the optimal synchronization point is found through the cross-correlation function:

[0037]

[0038] Among them, τ opt represents the optimal time offset; s * represents the complex conjugate of the synchronization sequence; s * (t-τ) represents the conjugate complex number of s(t); τ represents the time offset.

[0039] As a further improvement of the technical solution, in S4.2, the expressions for measuring signal strength, Doppler shift, arrival time and arrival time difference are:

[0040] Signal strength estimation:

[0041]

[0042] Where P represents the received signal strength; N represents the number of samples;

[0043] Doppler shift estimation:

[0044]

[0045] Among them, f d represents Doppler shift; v represents the speed of the drone relative to the receiving station; c represents the speed of light; f0 represents the transmission frequency;

[0046] Arrival time and arrival time difference estimation:

[0047]

[0048] Δt lp =t l -t p ;

[0049] Among them, t lIndicates the time when the lth receiving station detects the start of the signal; r l (t) represents the signal amplitude at time t; Δt lp represents the time difference between the lth receiving station and the pth receiving station; t p Indicates the time when the pth receiving station detects the end of the signal.

[0050] As a further improvement of the technical solution, the steps involved in restoring the instructions and status information sent by the drone through the Viterbi algorithm are:

[0051] S7.1. Analyze the modulation type and other key parameters of the signal;

[0052] S7.2, using the Viterbi algorithm to decode and restore the received coded signal to the original data to ensure the accuracy and integrity of the information;

[0053] S7.3. According to the UAV communication protocol, parse out the command and status information.

[0054] As a further improvement of the technical solution, in S7.1, the specific process of analyzing the modulation type and other key parameters of the signal is as follows:

[0055] For QPSK signals, the constellation diagram consists of four phase points. The phase angle θ of each symbol is calculated to determine the modulation type:

[0056] θ = arg(r[n]);

[0057] Where θ represents the phase angle of each symbol; r[n] represents the received complex baseband signal sample; arg represents the complex phase angle function;

[0058] Estimate key signal parameters;

[0059] Carrier frequency offset:

[0060]

[0061] Where Δf represents the carrier frequency offset; peak index Indicates the index position corresponding to the maximum peak detected in the frequency domain; FFT size Indicates the total number of points sampled in the frequency domain; sampling rate Indicates the sampling rate;

[0062] Symbol rate:

[0063]

[0064]

[0065] Among them, R s represents the symbol rate; Δt represents the time interval between adjacent symbols; r * [nt] represents the conjugate complex number of r[n].

[0066] As a further improvement of the technical solution, in S7.2, the specific process of using the Viterbi algorithm for decoding and restoring the received coded signal to the original data is as follows:

[0067] Maximum likelihood decoding using the Viterbi algorithm:

[0068]

[0069] Among them, γ i,j (k) represents the path metric from state m to state i; λ i,j (k) represents the branch metric of the kth symbol; m represents the index of the previous state; i represents the index of the current state; j represents the index of the next state; and k represents the symbol.

[0070] As a further improvement of the technical solution, the detection and positioning unit includes a trajectory positioning module and a position positioning module;

[0071] The trajectory positioning module is used to track and record the flight trajectory of the drone in real time according to the received drone communication signal;

[0072] The position positioning module is used to analyze the received drone control signal and calculate the actual position of the pilot by combining the angle measurement and distance measurement technology of the multi-antenna array.

[0073] Compared with the prior art, the present invention has the following beneficial effects:

[0074] 1. In the UAV flight trajectory positioning system based on radio detection, the antenna capture module accurately collects radio detection information in space, and combines multi-antenna array technology to measure angles and improve spatial resolution, which can achieve high-precision positioning of the UAV position. Preprocessing, filtering and synchronizing the collected radio detection information can effectively remove noise and unnecessary frequency components and enhance the anti-interference performance of the system.

[0075] 2. In the UAV flight trajectory positioning system based on radio detection, the Viterbi algorithm is used to restore the instructions and status information sent by the UAV, ensuring the integrity and accuracy of the information. The position and flight trajectory of the UAV can be determined in real time, and dynamic tracking and path planning can be supported, which can effectively avoid collisions and other potential risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is the overall flow chart of the present invention;

[0077] The meaning of each number in the figure is:

[0078] 1. Signal acquisition unit; 11. Antenna capture module; 2. Information processing unit; 3. Signal demodulation unit; 4. Detection and positioning unit; 41. Track positioning module; 42. Position positioning module. DETAILED DESCRIPTION

[0079] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0080] Example

[0081] See also Figure 1 As shown, a UAV flight trajectory positioning system based on radio detection is provided, including a signal acquisition unit 1, which collects radio detection information propagated in space based on an antenna capture module 11;

[0082] Specifically, the antenna capture module 11 is a key component in the wireless communication system, which is responsible for receiving electromagnetic wave signals propagating in space and converting them into electrical signals for subsequent processing. The module usually includes one or more carefully designed antennas for signal reception in a specific frequency band, and related radio frequency circuits for amplifying, filtering and preliminarily processing the received weak signals to ensure that their quality and strength are sufficient for further demodulation and analysis.

[0083] Specifically as follows: Use antennas for specific frequency bands (such as dipole antennas, parabolic antennas, microstrip antennas), and select antenna types according to application requirements to achieve high-gain signal reception within a specific frequency range; use single antenna or multi-antenna array design to support multipath signal reception and directivity enhancement, such as using uniform linear arrays or planar arrays for angle measurement; the antenna supports specific polarization modes (such as vertical polarization, horizontal polarization, or circular polarization) to improve sensitivity to target signals.

[0084] Among them, after the antenna receives the signal, it is initially amplified by a low-noise amplifier to enhance the strength of the weak signal while minimizing the introduction of noise. The amplifier is usually designed with a high gain, such as 20-30dB gain and a noise factor of less than 1dB. A bandpass filter is used to filter the frequency of the received signal to filter out irrelevant frequency band noise and interference signals and retain the target frequency band signal. The passband width of the filter is usually designed according to the signal characteristics of the target frequency band, such as a 100MHz bandwidth with a center frequency of 2.4GHz. The RF signal or the down-converted signal is converted into a digital signal, and the sampling rate and resolution are selected according to the signal frequency and accuracy requirements, such as 12-bit resolution and 100MSPS (million samples per second) rate. According to the modulation mode of the communication protocol (such as FSK, QPSK, OFDM, etc.), the digital signal processing algorithm is used for demodulation to extract useful information. The precise position of the drone is calculated through multi-point collaboration using technologies such as time difference of arrival (TDOA), angle of arrival (AOA), or signal strength location (RSSI).

[0085] The performance of the antenna acquisition module directly affects the effectiveness and reliability of the communication system, which is crucial for applications such as drone positioning, radar detection and mobile communications.

[0086] In this example, the specific steps involved in collecting radio detection information propagating in space based on the antenna capture module 11 are:

[0087] S2.1. Select the antenna according to the frequency band used by the drone and the expected detection distance;

[0088] S2.2, deploy multiple antennas to form an array for angle measurement and improved spatial resolution;

[0089] Specifically, the steps of deploying multiple antennas to form an array for angle measurement and improving spatial resolution are as follows:

[0090] The signal arrival angle is calculated using the phase difference between multiple antennas;

[0091] Determine the antenna spacing d, which is usually set to half the wavelength λ, that is

[0092] By measuring the phase difference Δφ of the signals received by different antennas, the MUSIC algorithm is used to estimate Δφ and calculate the signal arrival angle θ;

[0093]

[0094]

[0095] Antenna arrays are structures composed of multiple individual antenna units arranged in a specific geometric arrangement. These units work together through phase and amplitude control to concentrate radiation energy in a specific direction or receive signals from a specific direction, thereby achieving more efficient and directional communication than a single antenna. This configuration can enhance signal strength, improve communication quality, expand coverage, and dynamically change the direction of the beam by adjusting the signal phase between each unit, which is the so-called beamforming technology. It is widely used in radar, wireless communications, satellite communications and other fields.

[0096] S2.3, the received radio signal is processed by radio frequency front end;

[0097] Specifically, the steps of performing RF front-end processing on the received radio signal are as follows:

[0098] Choose an LNA chip with low noise figure and high gain;

[0099] Design matching networks to ensure maximum power transfer and minimum reflections;

[0100] Design and select the center frequency f c The filter with bandwidth BW adapted to the communication band of the UAV uses a SAW filter to select the signal within the target frequency band and suppress out-of-band interference;

[0101] Select an appropriate local oscillator LO frequency f LO , so that the output intermediate frequency f IF =|f RF -f LO |Located within easy reach;

[0102] Design image rejection circuit to avoid image frequency interference.

[0103] RF front-end processing refers to the process of converting the information signal to be sent into high-frequency radio signals for transmission, as well as receiving and processing the RF signals captured from the air to extract the original information in the wireless communication system. It includes operations such as signal amplification, filtering, mixing, modulation and demodulation, up-conversion and down-conversion, and usually involves components such as antennas, low-noise amplifiers (LNA), power amplifiers (PA), filters, duplexers and RF switches. The design and performance of the RF front-end are crucial to ensuring the reliability and efficiency of the communication system, and directly affect the quality of the signal, the power consumption of the equipment and the overall communication capability.

[0104] S2.4, converting the analog signal into a digital signal for subsequent digital signal processing.

[0105] Specifically, in the UAV flight trajectory positioning system based on radio detection, the antenna capture module receives radio signals propagating from space through a carefully selected and deployed antenna array, optimizes the signal quality through RF front-end processing, and converts the analog signal into a digital signal for subsequent digital signal processing. This process can accurately measure the signal arrival angle and improve the spatial resolution, thereby achieving high-precision positioning and tracking of the UAV position, ensuring that it flies safely and accurately along the predetermined path. It can also be used to monitor the environment around the UAV, avoid collisions and other potential risks, and enhance the safety and reliability of UAV operations.

[0106] In this example, the specific steps involved in converting the analog signal to a digital signal are:

[0107] S2.41, select sampling frequency f s Sampling is performed to ensure that all information of the drone communication signal can be captured;

[0108] Among them, the specific expression for sampling is:

[0109] x[n]=x(t)| t＝nTs ;

[0110] Where x[n] represents the nth sample value of a discrete-time signal; x(t) represents a continuous-time analog signal; T s represents the sampling period; n represents the sampling index; t represents the time;

[0111] S2.42, select the number of quantization bits N for quantization to balance accuracy and hardware cost;

[0112] Among them, the quantified expression is:

[0113]

[0114] Where q(x) represents the quantized discrete value; x represents the original analog signal value; V min Indicates the minimum voltage range of the input signal; Δ indicates the quantization step size; round indicates rounding;

[0115] S2.43, converting the quantized value into a binary code;

[0116] Among them, the expression for converting the quantized value into binary code is:

[0117]

[0118] b(q(x)) means converting the quantized discrete value (q(x)) into a binary string; bin(*) means converting the integer * into an N-bit binary string.

[0119] Specifically, in the UAV flight trajectory positioning system based on radio detection, the process of converting analog signals into digital signals is a crucial step, which ensures that the information captured from the physical world can be understood and used by the digital processing system. By selecting the appropriate sampling frequency and quantization bit number, it can be ensured that the key information in the UAV communication signal is not missed, and the hardware cost and computing resources can be effectively managed. The sampling process converts the continuous-time analog signal into a discrete-time signal, so that the signal strength at each time point can be recorded; the quantization step further maps these discrete analog values ​​to a limited number of discrete levels, so that they can be represented by binary codes. Finally, the signal converted into binary code can be efficiently processed, stored and transmitted by the computer, and then used to accurately analyze the position and motion trajectory of the UAV, support real-time monitoring and automatic navigation functions, improve the system's response speed and positioning accuracy, and ensure the safety and reliability of UAV operations.

[0120] The UAV flight trajectory positioning system based on radio detection also includes an information processing unit 2, which processes the radio detection information collected by the signal collection unit 1;

[0121] In this example, the specific steps involved in performing data processing on the radio detection information collected by the signal collection unit 1 are as follows:

[0122] S4.1, preliminarily processing the collected digital signals;

[0123] In this example, the process of preliminary processing of the collected digital signal is as follows:

[0124] Use digital filters to remove unwanted frequency components;

[0125] For an input signal x[n], the output y[n] after the filter can be expressed as a convolution operation:

[0126] y[n]=x[n]*h[n];

[0127] Where h[n] represents the impulse response of the filter; * represents the convolution operation;

[0128] Frame synchronization and carrier synchronization are achieved by capturing sequences or pilot symbols;

[0129] The received signal r(t) contains the known synchronization sequence s(t), and the optimal synchronization point can be found through the cross-correlation function:

[0130]

[0131] Among them, τopt represents the optimal time offset; s * represents the complex conjugate of the synchronization sequence; s * (t-τ) represents the conjugate complex number of s(t); τ represents the time offset.

[0132] Specifically, in the UAV flight trajectory positioning system based on radio detection, the collected digital signals are preliminarily processed to ensure the accuracy and reliability of subsequent analysis and positioning. By using digital filters to remove unnecessary frequency components, noise and interference can be reduced and signal quality can be improved. The application of convolution operations enables the filtering process to effectively smooth the signal and retain useful information. In addition, frame synchronization and carrier synchronization are achieved by capturing sequences or pilot symbols to ensure that the received signals can be correctly decoded. Finding the optimal synchronization point is crucial for accurately parsing the position of the UAV. This process not only helps to improve positioning accuracy, but also enhances the system's anti-interference ability, ensures the stable operation of the UAV in a complex electromagnetic environment, and supports it to complete its scheduled tasks safely and accurately.

[0133] S4.2, measure signal strength, Doppler shift, arrival time and arrival time difference for the estimation of the position and velocity of the UAV.

[0134] In this example, the expressions for measuring signal strength, Doppler shift, arrival time, and arrival time difference are:

[0135] Signal strength estimation:

[0136]

[0137] Where P represents the received signal strength; N represents the number of samples;

[0138] Doppler shift estimation:

[0139]

[0140] Among them, f d represents Doppler shift; v represents the speed of the drone relative to the receiving station; c represents the speed of light; f0 represents the transmission frequency;

[0141] Arrival time and arrival time difference estimation:

[0142]

[0143] Δt lp =t l -t p ;

[0144] Among them, t l Indicates the time when the lth receiving station detects the start of the signal; r l(t) represents the signal amplitude at time t; Δt lp represents the time difference between the lth receiving station and the pth receiving station; t p Indicates the time when the pth receiving station detects the end of the signal.

[0145] Specifically, in the UAV flight trajectory positioning system based on radio detection, measuring signal strength, Doppler shift, arrival time and arrival time difference is the core step to achieve accurate position and speed estimation. By calculating the received signal strength, the distance and path loss between the UAV and the receiving station can be evaluated; the estimation of Doppler shift provides information on the relative speed of the UAV, which is crucial for dynamic tracking; and the measurement of arrival time and arrival time difference can be used for triangulation or multilateral measurement to accurately determine the position of the UAV. The accurate measurement of these parameters not only enhances the positioning accuracy of the system, but also improves the reliability of speed estimation, allowing the UAV to navigate safely in complex environments, and supports functions such as real-time monitoring and automatic obstacle avoidance, ensuring the efficiency and safety of mission execution.

[0146] The UAV flight trajectory positioning system based on radio detection also includes a signal demodulation unit 3, which restores the command and status information sent by the UAV through the Viterbi algorithm based on the radio detection information processed by the information processing unit 2;

[0147] In this example, the steps involved in restoring the instructions and status information sent by the drone through the Viterbi algorithm are:

[0148] S7.1. Analyze the modulation type and other key parameters of the signal;

[0149] In this example, the specific process of analyzing the modulation type and other key parameters of the signal is as follows:

[0150] For QPSK signals, the constellation diagram consists of four phase points. The phase angle θ of each symbol is calculated to determine the modulation type:

[0151] θ = arg(r[n]);

[0152] Where θ represents the phase angle of each symbol; r[n] represents the received complex baseband signal sample; arg represents the complex phase angle function;

[0153] Estimate key signal parameters;

[0154] Carrier frequency offset:

[0155]

[0156] Where Δf represents the carrier frequency offset; peak indexIndicates the index position corresponding to the maximum peak detected in the frequency domain; FFT size Indicates the total number of points sampled in the frequency domain; sampling rate Indicates the sampling rate;

[0157] Symbol rate:

[0158]

[0159]

[0160] Among them, R s represents the symbol rate; Δt represents the time interval between adjacent symbols; r * [nt] represents the conjugate complex number of r[n].

[0161] Specifically, in the UAV flight trajectory positioning system based on radio detection, analyzing the modulation type and other key parameters of the signal is the basis for ensuring effective communication and data parsing. By calculating the phase angle of the QPSK signal to determine the modulation type, the received signal format can be accurately identified, providing the necessary information for subsequent decoding. Estimating the carrier frequency offset helps to correct the frequency error in the received signal and ensure the accuracy of signal synchronization and demodulation. The determination of the symbol rate provides key information about the signal transmission speed, which is essential for correctly parsing the instructions and status information sent by the drone. These steps work together to ensure that accurate and complete data is extracted from the received radio signal, support the precise positioning, status monitoring and safety control of the drone, and enhance the reliability and anti-interference capability of the system, ensuring the efficiency and safety of the drone mission execution.

[0162] S7.2, using the Viterbi algorithm to decode and restore the received coded signal to the original data to ensure the accuracy and integrity of the information;

[0163] In this example, the Viterbi algorithm is used for decoding, and the specific process of restoring the received coded signal to the original data is as follows:

[0164] Use the Viterbi algorithm for maximum likelihood decoding:

[0165]

[0166] Among them, γ i,j (k) represents the path metric from state m to state i; λ i,j (k) represents the branch metric of the kth symbol; m represents the index of the previous state; i represents the index of the current state; j represents the index of the next state; and k represents the symbol.

[0167] Specifically, in the UAV flight trajectory positioning system based on radio detection, decoding using the Viterbi algorithm is a key step to ensure that the received coded signal can be accurately restored to the original data. The Viterbi algorithm performs maximum likelihood decoding, calculates the optimal path metric from one state to another, selects the most likely transmission sequence, and can effectively correct errors even in the presence of noise or interference. Specifically, it recursively calculates the branch metric and cumulative path metric of each symbol to find the transmission path with the highest probability, thereby restoring the original data stream. This process not only improves the accuracy and integrity of information transmission, but also enhances the system's noise resistance and reliability, ensuring that the instructions and status information sent by the drone can be accurately parsed, supporting the drone's safe navigation, real-time monitoring and mission execution, and ensuring the safety and efficiency of operations.

[0168] S7.3. According to the UAV communication protocol, parse out the command and status information.

[0169] Specifically, the steps to parse out the instructions and status information are:

[0170] (i) A specific bit sequence used to mark the beginning of a frame;

[0171] Read the frame header field:

[0172] Frame type: distinguish whether it is a command frame, data frame or status report frame.

[0173] Frame length: determines the size of the payload.

[0174] Command type or operation code (Opcode): identifies the specific command or operation.

[0175] Source address and destination address: Identify the sender and receiver.

[0176] Timestamp: records the time when a frame is generated, which is used for timing analysis and synchronization.

[0177] (ii) parsing specific data in the payload according to the frame type;

[0178] Command frame analysis:

[0179] Command ID: Extract the unique identifier of the command to determine the specific operation (such as takeoff, landing, change course, etc.).

[0180] Command parameters: parse the parameters related to the command, such as speed, altitude, angle, waypoint coordinates, etc.

[0181] Data frame parsing:

[0182] Sensor Data: Parse data from various sensors such as accelerometer, gyroscope, GPS, barometer, etc.

[0183] Flight status information: Extract the current status of the drone, such as battery level, flight mode, fault codes, etc.

[0184] Status report frame analysis:

[0185] System status: Analyze the status of each subsystem of the drone, such as motor speed, temperature, pressure, etc.

[0186] Health status: Extract error codes, warning information, etc. to monitor the health status of the drone.

[0187] (3) parsing the decoded binary data into specific instructions and status information according to the protocol format;

[0188] In the UAV flight trajectory positioning system based on radio detection, restoring the instructions and status information sent by the UAV through the Viterbi algorithm is a key link to ensure the accuracy and reliability of communication. First, the modulation type and other key parameters of the signal are analyzed to understand the signal structure; then, the powerful error correction capability of the Viterbi algorithm is used to decode the coded signal, and the original data can be restored even in the presence of noise or interference to ensure the integrity and accuracy of the information; finally, the specific instructions and status information, such as flight mode, speed, altitude, etc., are parsed according to the UAV communication protocol. This process not only supports the precise control and status monitoring of the UAV, but also enhances the system's anti-interference ability and communication stability, which plays an indispensable role in achieving safe flight and mission execution of the UAV.

[0189] The UAV flight trajectory positioning system based on radio detection further includes a detection and positioning unit 4 , which determines the position and flight trajectory of the UAV based on a trajectory positioning module 41 and a position positioning module 42 .

[0190] In this example, the detection and positioning unit 4 includes a trajectory positioning module 41 and a position positioning module 42;

[0191] The trajectory positioning module 41 is used to track and record the flight trajectory of the drone in real time according to the received drone communication signal;

[0192] The position positioning module 42 is used to analyze the received drone control signal and calculate the actual position of the pilot by combining the angle measurement and distance measurement technology of the multi-antenna array.

[0193] Specifically, the angle measurement and distance measurement technology combined with the multi-antenna array is as follows:

[0194] The distance is measured as:

[0195]

[0196] Where d′ represents the measurement distance;

[0197] In the UAV flight trajectory positioning system based on radio detection, the trajectory positioning module 41 and the position positioning module 42 work together to ensure the safety and accuracy of the UAV operation. The trajectory positioning module 41 can accurately depict the flight path of the UAV by tracking and recording the communication signal of the UAV in real time, and supports dynamic monitoring and historical trajectory playback, which is crucial for mission planning and post-analysis;

[0198] The steps for the trajectory positioning module 41 to achieve real-time tracking and recording of communication signals are as follows: utilize the wireless communication link (such as Wi-Fi, LoRa, 5G or other dedicated frequency bands) between the UAV and the ground station or controller to capture communication data packets in real time through the signal receiving module. These data packets usually contain information such as the GPS coordinates, altitude, speed and direction of the UAV; and extract the real-time location information and other status data of the UAV through the decoding protocol stack (such as MAVLink, DJIOSDK, etc.); use precise clock synchronization technology (such as NTP or PTP) to add a timestamp to each recorded data to ensure the temporal continuity and accuracy of the trajectory.

[0199] The trajectory positioning module 41 continuously displays the current position and trajectory of the drone through the ground control station (GCS) software or dedicated monitoring equipment, allowing the user to view the flight status in real time; if the signal is interrupted, the trajectory display is maintained using the last received valid position data, and the user is prompted that the signal is lost.

[0200] Furthermore, the flight data is stored in a local or cloud database in JSON, CSV, or binary format; historical flight data is retrieved by time range or mission ID through the database retrieval function; the flight process is reproduced on the monitoring interface, supporting speed adjustment, trajectory pause, and rollback functions; the commonly used implementation method is animation playback based on time series data (such as using WebGL or GIS dynamic visualization tools).

[0201] The position positioning module 42 uses the angle measurement and distance measurement technology of the multi-antenna array, combined with the received control signal, to accurately calculate the actual position of the pilot, using a uniform linear array (ULA), a planar array (UPA) or other structured arrays, and the spacing between antennas is usually half the wavelength of the signal wavelength to avoid spatial aliasing;

[0202] Specifically, by measuring the distance based on time delay Calculate the distance between the drone and the receiving station, and combine the angle information to achieve high-precision three-dimensional positioning, as follows:

[0203] Use multiple receiving stations to work together, each receiving station provides distance and angle information, and three-dimensional spatial positioning is achieved through geometric calculations; use triangulation or multilateral measurement (such as TDOA positioning method) to calculate the intersection between the drone and multiple receiving stations as the three-dimensional position;

[0204] The measured drone position is converted from the local coordinate system to the WGS84 geographic coordinate system to facilitate association with the pilot's position; combined with the inertial navigation system (INS) data, the Kalman filter is used to further improve the positioning accuracy.

[0205] This process not only enhances the positioning accuracy of the system, but also improves the coordination ability between the drone and the pilot, ensuring that the drone can perform tasks safely and stably in complex environments, while providing reliable support for remote control and automatic navigation.

[0206] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. The UAV flight trajectory positioning system based on radio detection is characterized by: include A signal acquisition unit (1), wherein the signal acquisition unit (1) acquires radio detection information propagating in space based on an antenna capture module (11); An information processing unit (2), wherein the information processing unit (2) performs data processing based on the radio detection information collected by the signal collection unit (1); A signal demodulation unit (3), wherein the signal demodulation unit (3) restores the command and status information sent by the drone through a Viterbi algorithm based on the radio detection information processed by the information processing unit (2); A detection and positioning unit (4), wherein the detection and positioning unit (4) determines the position and flight trajectory of the drone based on a trajectory positioning module (41) and a position positioning module (42).

2. The UAV flight trajectory positioning system based on radio detection according to claim 1 is characterized in that: The specific steps involved in collecting the radio detection information propagated in space based on the antenna capture module (11) are: S2.

1. Select the antenna according to the frequency band used by the drone and the expected detection distance; S2.2, deploy multiple antennas to form an array for angle measurement and improved spatial resolution; S2.3, the received radio signal is processed by radio frequency front end; S2.4, converting the analog signal into a digital signal for subsequent digital signal processing.

3. The UAV flight trajectory positioning system based on radio detection according to claim 2 is characterized in that: In S2.4, the specific steps involved in converting the analog signal into a digital signal are: S2.41, select sampling frequency f s Sampling is performed to ensure that all information of the drone communication signal can be captured; Among them, the specific expression for sampling is: x[n]=x(t)| t＝nTs ; Where x[n] represents the nth sample value of a discrete-time signal; x(t) represents a continuous-time analog signal; T s represents the sampling period; n represents the sampling index; t represents the time; S2.42, select the number of quantization bits N for quantization to balance accuracy and hardware cost; Among them, the quantified expression is: Where q(x) represents the quantized discrete value; x represents the original analog signal value; V min Indicates the minimum voltage range of the input signal; Δ indicates the quantization step size; round indicates rounding; S2.43, converting the quantized value into a binary code; Among them, the expression for converting the quantized value into binary code is: b(q(x)) means converting the quantized discrete value (q(x)) into a binary string; bin(*) means converting the integer * into an N-bit binary string.

4. The UAV flight trajectory positioning system based on radio detection according to claim 1 is characterized in that: The specific steps involved in performing data processing on the radio detection information collected by the signal collection unit (1) are as follows: S4.1, preliminarily processing the collected digital signals; S4.2, measure signal strength, Doppler shift, arrival time and arrival time difference for the estimation of the position and velocity of the UAV.

5. The UAV flight trajectory positioning system based on radio detection according to claim 4 is characterized in that: In S4.1, the specific process of performing preliminary processing on the collected digital signal is as follows: Use digital filters to remove unwanted frequency components; For an input signal x[n], the output y[n] after the filter is expressed as a convolution operation: y[n]=x[n]*h[n]; Where h[n] represents the impulse response of the filter; * represents the convolution operation; Frame synchronization and carrier synchronization are achieved by capturing sequences and pilot symbols; The received signal r(t) contains the synchronization sequence s(t), and the optimal synchronization point is found through the cross-correlation function: Among them, τ opt represents the optimal time offset; s * represents the complex conjugate of the synchronization sequence; s * (t-τ) represents the conjugate complex number of s(t); τ represents the time offset.

6. The UAV flight trajectory positioning system based on radio detection according to claim 4 is characterized in that: In S4.2, the expressions for measuring signal strength, Doppler shift, arrival time and arrival time difference are: Signal strength estimation: Where P represents the received signal strength; N represents the number of samples; Doppler shift estimation: Among them, f d represents Doppler shift; v represents the speed of the drone relative to the receiving station; c represents the speed of light; f0 represents the transmission frequency; Arrival time and arrival time difference estimation: Δt lp =t l -t p ; Among them, t l Indicates the time when the lth receiving station detects the start of the signal; r l (t) represents the signal amplitude at time t; Δt lp represents the time difference between the lth receiving station and the pth receiving station; t p Indicates the time when the pth receiving station detects the end of the signal.

7. The UAV flight trajectory positioning system based on radio detection according to claim 1 is characterized in that: The steps involved in restoring the command and status information sent by the drone through the Viterbi algorithm are: S7.

1. Analyze the modulation type and other key parameters of the signal; S7.2, using the Viterbi algorithm to decode and restore the received coded signal to the original data to ensure the accuracy and integrity of the information; S7.

3. According to the UAV communication protocol, parse out the command and status information.

8. The UAV flight trajectory positioning system based on radio detection according to claim 7 is characterized in that: In S7.1, the specific process of analyzing the modulation type and other key parameters of the signal is as follows: For QPSK signals, the constellation diagram consists of four phase points. The phase angle θ of each symbol is calculated to determine the modulation type: θ = arg(r[n]); Where θ represents the phase angle of each symbol; r[n] represents the received complex baseband signal sample; arg represents the complex phase angle function; Estimate key signal parameters; Carrier frequency offset: Where Δf represents the carrier frequency offset; peak index Indicates the index position corresponding to the maximum peak detected in the frequency domain; FFT size Indicates the total number of points sampled in the frequency domain; sampling rate Indicates the sampling rate; Symbol rate: Among them, R s represents the symbol rate; Δt represents the time interval between adjacent symbols; r * [nt] represents the conjugate complex number of r[n].

9. The UAV flight trajectory positioning system based on radio detection according to claim 7 is characterized in that: In S7.2, the specific process of using the Viterbi algorithm to decode and restore the received coded signal to the original data is as follows: Maximum likelihood decoding using the Viterbi algorithm: Among them, γ i,j (k) represents the path metric from state m to state i; λ i,j (k) represents the branch metric of the kth symbol; m represents the index of the previous state; i represents the index of the current state; j represents the index of the next state; and k represents the symbol.

10. The UAV flight trajectory positioning system based on radio detection according to claim 1 is characterized in that: The detection and positioning unit (4) comprises a track positioning module (41) and a position positioning module (42); The trajectory positioning module (41) is used to track and record the flight trajectory of the drone in real time according to the received drone communication signal; The position positioning module (42) is used to analyze the received drone control signal and calculate the actual position of the pilot by combining the angle measurement and distance measurement technology of the multi-antenna array.

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