Low-power-consumption bidirectional beacon cooperative unmanned aerial vehicle search and rescue positioning method

Abstract

The invention belongs to the technical field of unmanned aerial vehicle search and rescue positioning, and particularly discloses a low-power-consumption bidirectional beacon cooperative unmanned aerial vehicle search and rescue positioning method, which is characterized in that an unmanned aerial vehicle group is cooperated with a relay to forward an activation signal, a spread spectrum technology is combined, a coverage radius range is improved, a terrain shielding blind area is eliminated, and an azimuth angle and change rate combined positioning algorithm is provided. According to the method, meter-scale positioning is realized in a non-line-of-sight environment without depending on satellite signals, encryption and polar code-OFDM joint modulation are adopted, communication parameters are adaptively adjusted, the packet loss rate is reduced, electromagnetic noise is resisted, a beacon device adopts an intermittent wake-up mechanism, the standby current is reduced to below 10 microamperes, the endurance time is prolonged to 30 days, and the long-period search and rescue requirement is met. The problems of a traditional search and rescue system in the aspects of coverage range, safety, precision and cost are systematically solved.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV search and rescue positioning, and particularly to a UAV search and rescue positioning method with low-power two-way beacon collaboration. Background Art

[0002] At present, the UAV search and rescue positioning technology is showing a development trend of multi-modal fusion. By combining satellite, vision, infrared, and radio signals, the reliability and adaptability of positioning can be improved. In practical applications, it is necessary to select an appropriate technology combination according to different environments. For example, in a forest environment, thermal imaging combined with AI recognition can effectively improve the target detection rate, while in maritime search and rescue, radio signals and wide-area search algorithms can be relied on to achieve large-scale coverage. In the future, with the continuous breakthroughs in autonomous flight, edge computing, and energy technologies, UAVs will play a more core role in the field of emergency rescue. However, in complex terrains, environments with severe signal shielding, or long-term tasks, the existing technologies still face many challenges.

[0003] The beacon activation mechanism of the existing UAV search and rescue system relies on a fixed base station to transmit signals. Due to insufficient signal attenuation and penetration in multi-obstacle scenarios such as mountains and dense forests, the success rate of beacon wake-up is less than 60% due to the limited power and narrow coverage range of the fixed base station. Moreover, the continuous transmission mode of the beacon has high power consumption and prominent battery life limitations. The hardware redundancy design further increases the system cost. In addition, the identity authentication uses plaintext transmission or static encryption, lacking dynamic key management and end-to-end encrypted links, and facing risks of man-in-the-middle attacks and brute-force cracking. In terms of the search and rescue object positioning technology, the errors of GPS and RSSI exceed 10 meters in multi-path effect and non-line-of-sight environments. The traditional TDOA algorithm is not combined with a space diversity reception structure, and the time difference measurement is interfered by multi-path noise, with a positioning fluctuation standard deviation > 3 meters, unable to achieve centimeter-level accuracy.

[0004] The patent with the publication number CN116625376A discloses a tabu bee colony algorithm. This invention patent realizes the efficient solution of large-scale and complex maritime search and rescue path planning problems by constructing a search and rescue target model and a constraint description model, combining tabu search and bee colony algorithm. It relies on a single optimization goal, does not consider multi-objective conflicts, is prone to falling into local optima, has a fixed tabu list length, poor adaptability to dynamic environments, only considers the upper limit of battery power in the constraint conditions, does not design a low-power mechanism, and the battery life depends on the battery capacity, unable to meet long-term tasks. It does not solve the problem of signal occlusion of a single UAV, relies on traditional relays, and the blind area ratio is still > 5%.

[0005] The patent with the publication number CN119440055A discloses an improved multi - objective particle swarm algorithm. By converting the search - and - rescue area into a two - dimensional map, performing grid decomposition, calculating the target distribution probability, establishing a multi - objective collaborative optimization function, and using a hybrid particle swarm optimization algorithm to solve, the optimal UAV search - and - rescue path is generated. The multi - objective function needs to calculate parameters such as grid probability and path overlap in real - time, and the algorithm time consumption increases by more than 50%, making it difficult to meet the timeliness of the golden 72 - hour rescue. At the same time, it requires high - precision positioning and a real - time communication link, and fails in scenarios without infrastructure. Only by shortening the path length can the energy consumption be reduced, the communication protocol and beacon power consumption are not optimized, and the actual endurance improvement is less than 30%. Summary of the Invention

[0006] The purpose of the present invention is: aiming at the above - mentioned problems, the present invention provides a UAV search - and - rescue positioning method with low - power two - way beacon collaboration. The present invention activates signals through UAV swarm collaborative relay forwarding, combines spread - spectrum technology to increase the coverage radius range, eliminates terrain - occlusion blind areas, proposes an azimuth angle and its rate - of - change joint positioning algorithm, which does not rely on satellite signals and realizes meter - level positioning in non - line - of - sight environments. It uses encryption and polar - code - OFDM joint modulation to adaptively adjust communication parameters, reduce the packet loss rate and resist electromagnetic noise. The beacon device adopts an intermittent wake - up mechanism, with the standby current reduced to less than 10 μA and the endurance extended to 30 days, meeting the long - cycle search - and - rescue requirements, and systematically solving the problems of traditional search - and - rescue systems in terms of coverage, safety, accuracy, and cost.

[0007] The technical solution adopted by the present invention is as follows: A UAV search - and - rescue positioning method with low - power two - way beacon collaboration, the method specifically includes: Beacon activation: sending an activation signal through a UAV, and the activation signal triggers the start of the beacon device of the object to be searched and rescued, completing beacon activation; Identity verification: after beacon activation, the beacon device constructs an encrypted data - packet signal, and then transmits the data - packet signal to the ground control center through the UAV, completing identity verification. The ground control center controls the UAV to start searching for the position of the object to be searched and rescued according to the data - packet signal; Positioning: the UAV collects the data - packet signal sent by the object to be searched and rescued, measures the azimuth angle and the rate of change of the azimuth angle of the object to be searched and rescued through the data - packet signal, and determines the position of the object to be searched and rescued.

[0008] Further, the beacon activation specifically includes: Constructing an activation signal, where the activation signal includes a preamble and a synchronization word matching the beacon device; Signal modulation: modulating the activation signal through BPSK modulation and spread - spectrum processing to obtain the modulated activation signal; Signal forwarding: The modulated activation signal is sent to the UAV via the ground control center, and then the UAV sends the modulated activation signal. Beacon activation: After receiving the activation signal sent by the UAV, the beacon device completes beacon activation after the preamble and sync word match and confirmation.

[0009] Furthermore, the construction process of the preamble and sync word is as follows: The preamble is fixed and used to quickly identify the start of the activation signal. The sync word is a pseudo-random code sequence generated by an improved Logistic chaotic mapping model, as shown in the following formula: (1) In the formula, is the chaotic parameter, is the perturbation factor, x k , x k+1 represent the sequences at adjacent times, k is the time.

[0010] Furthermore, the BPSK modulation is specifically as follows: (2) In the formula, is the binary modulation signal, is the function of the BPSK modulation signal changing with time; The spread spectrum processing is specifically as follows: (3) (4) In the formula, is the function of the activation signal changing with time, is the signal amplitude, is the carrier frequency, is the binary modulation signal, is the spread spectrum code sequence, is the pulse width, t is the time.

[0011] Furthermore, in the signal forwarding, the number of UAVs is several. Several UAVs forward the activation signal to each other, and then several UAVs send the activation signal. During the process of the UAVs receiving the modulated activation signal and forwarding it to each other, according to the gradient distribution of the received signal strength, the optimal relay node is autonomously selected, and the relay decision function is as follows: (5) In the formula, and are the weight coefficients, is the maximum allowable delay, and RSSI is the received signal strength; The drone dynamically adjusts the forwarding gain as follows: (6) In the formula, H is the transmission distance, B is the attenuation constant, G 基准 is the reference gain, G 动态 is the dynamic gain.

[0012] Furthermore, the beacon device is also integrated with a quantum random number generator, which generates true random numbers based on the quantum entropy source of single-photon unilateral. The output rate of the seed entropy source is specifically as follows: (7) In the formula, is the detection efficiency, is the laser pulse frequency, is the wavelength, is the attenuation function of the output rate. The dynamic key is transmitted to the ground control center through the quantum key distribution protocol and is used for auxiliary encryption during the data packet signal transmission.

[0013] Furthermore, the data packet signal needs to be sequentially subjected to polar code encoding and OFDM multi-carrier modulation before transmission, specifically as follows: (8) (9) In the formula, P is the pre-designed polar code sequence, is the AES encryption operation on the polar code sequence, is the data bit, is the encrypted data bit, i is the code sequence. The encoded data is modulated through OFDM multi-carrier modulation, and the modulation function is as follows: (10) In the formula, is the modulation symbol on the subcarrier, L is the total number of subcarriers, is the subcarrier spacing, j is the imaginary unit, f m is the carrier frequency of different subcarriers, m is a natural number, is the initial carrier frequency. The data packet signal after encoding and modulation is transmitted to the ground control center through the data transmission link established by the drone platform for positioning.

[0014] Furthermore, the positioning specifically includes: deploying three omnidirectional antennas on the UAV in an equilateral triangle distribution to receive the data packet signals sent by the search and rescue target. The specific positioning process is as follows: Utilize the phase difference of the signals received by adjacent antennas Calculate the azimuth angle of the search and rescue target: (11) In the formula, is the wavelength, is the baseline length, is the baseline direction angle, is the phase noise, h , g are the serial numbers of the three omnidirectional antennas, and the azimuth angle of the search and rescue target is as follows: (12); Measure the change rate of the azimuth angle of the search and rescue target. Using the Doppler frequency shift method, calculate the change rate of the azimuth angle through the Doppler frequency shift difference of the three antennas: (13) In the formula, is the Doppler frequency difference between antennas h , g , is the wavelength. At the same time, perform Kalman filtering on the azimuth angles at consecutive moments to extract the change rate ; Suppose the spatial position relationship between the search and rescue target and the UAV at the k th moment is: (14) In the formula, is the azimuth angle of the search and rescue target at the k th moment, is the true position coordinate of the search and rescue target, is the position coordinate of the UAV. Take the derivative and conversion of the above formula to obtain the distance k between the UAV and the search and rescue target at the th moment, which is specifically as follows: (15) (16) In the formula, is the straight-line distance between the UAV and the search and rescue target at the th moment, , are the detected The azimuth and elevation angles of the search and rescue target at a certain moment; the distance between the UAV and the search and rescue target is calculated using the azimuth and elevation angles, so as to obtain the estimated position of the search and rescue target.

[0015] Furthermore, the positioning is optimized as follows: The filtered azimuth angle is obtained through Kalman filtering and its rate of change , and substituting them into Equations (15) and (16) to obtain the optimized ranging result and the preliminary positioning result : (17) (18).

[0016] Furthermore, the positioning is optimized for the second time to obtain the final positioning result, as follows: The target relationship between the UAV and the search and rescue target is: (19) In the formula, represents the estimated position coordinates of the search and rescue target, represents the observation position of the UAV, , respectively represent the azimuth angle and elevation angle of the search and rescue target at the th observation moment, represents the geometric relationship of the search and rescue target at the k moment, as follows: (20) Using the above formula, we can get: (21) Then the iterative formula is derived. First, the initial position coordinates of the search and rescue target , can be set to any value, and then the azimuth angle and the elevation angle are used to iteratively estimate the position of the search and rescue target, and the th iteration result of the position of the search and rescue target at the th observation moment is: (22) Combined with V the iterative results of observation moments, the positioning result of the search and rescue target at the th observation moment is calculated using the centroid calculation formula: (23) Wherein, , represents the position of the search and rescue target obtained in the th iteration, which is converted into a three-dimensional space coordinate system. The specific calculation formula is: (24) Using the iteration result of the previous point as the initial value of the next point's iteration. When the difference between the iteration results of two adjacent iterations is less than the set convergence threshold, it is determined that the iteration converges. At this time, the iteration result is the final position of the search and rescue target. Set the iteration upper limit to , and the convergence threshold is , to obtain the iteration termination condition: (25) In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: A low-power two-way beacon collaborative UAV search and rescue positioning method of the present invention forms a complete low-power two-way beacon collaborative UAV search and rescue positioning system, that is, the overall collaborative working mode of the low-power beacon device, the UAV platform and the ground control center. The beacon device is awakened by receiving an activation signal that has been BPSK modulated and spread spectrum processed and contains a preamble and a synchronization word in the standby state. It internally integrates a quantum random number generator, an encryption unit and a data processing module, and generates and sends a 128-byte encrypted data packet containing beacon ID, RSSI and CRC check, etc., to ensure the security and reliability of identity information; the UAV platform serves as a relay and data forwarding node, extends the coverage of the activation signal through collaborative relay, selects the optimal relay node according to the signal strength gradient, and at the same time uses the multi-antenna array carried to collect the narrowband signal emitted by the search and rescue target, realizes angle measurement, phase difference calculation and Doppler frequency shift detection, and transmits the angle and rate of change data to the ground control center in real time; the ground control center decrypts and verifies the received encrypted data and positioning information, and combines the multi-moment observation data to use the Kalman filter and iteration convergence algorithm to finally accurately determine the precise position of the search and rescue target.

[0017] After adopting the multi-dimensional collaborative optimization mechanism in the present invention, through actual measurement, the system coverage range of the UAV platform has been expanded by 3 to 5 times through dynamic relay and collaborative signal enhancement technologies, and the communication blind area under complex terrain can be reduced by more than 50%; at the same time, after integrating multi-source perception data and intelligent optimization algorithms, the positioning error in non-line-of-sight environments has been reduced from 10 - 50 meters in traditional solutions to only 5 - 20 meters, with the accuracy improved by 80% to 90%. In addition, by adopting adaptive channel enhancement and dynamic anti-interference strategies, even in a noise environment above 30 dB, the bit error rate can be reduced from more than 15% to less than 10%; the ultra-low power consumption design extends the device's battery life from the original 72 hours to more than 5 days, providing reliable and economical technical support for wide-area search and rescue missions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 FIG. is the system schematic diagram of a low-power two-way beacon collaborative UAV search and rescue positioning method of the present invention; Figure 2 FIG. is the flowchart of a low-power two-way beacon collaborative UAV search and rescue positioning method of the present invention; Figure 3 FIG. is the schematic flowchart of the beacon activation stage in a low-power two-way beacon collaborative UAV search and rescue positioning method of the present invention; Figure 4 FIG. is the schematic assembly structure diagram of the UAV in a low-power two-way beacon collaborative UAV search and rescue positioning method of the present invention; Figure 5 FIG. is the algorithm flowchart of the positioning process in a low-power two-way beacon collaborative UAV search and rescue positioning method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] The present invention will be described in detail below with reference to the accompanying drawings.

[0020] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] EMBODIMENT This embodiment provides a low-power two-way beacon collaborative UAV search and rescue positioning method, as shown in Figure 1 and Figure 2As shown in the figure, the method of this embodiment constructs a search and rescue positioning system composed of a low-power beacon device, a drone platform including multiple drones, and a ground control center. Specifically, the low-power beacon device is in a standby state and is awakened by an activation signal sent by the drone platform. Its main task is to generate and send identity information and positioning data. A quantum random number generator, an encryption unit, and a data processing module are integrated inside the beacon to ensure the security and reliability of the identity information. The drone platform serves as a relay and data forwarding node. The drone swarm expands the coverage range of the activation signal through cooperative relaying, overcomes the problem of terrain occlusion, and determines the optimal relay node according to the signal strength gradient. The drone not only serves as a transfer station for beacon data but also carries a multi-antenna array to collect narrowband signals emitted by the target, performs angle measurement, phase difference calculation, and Doppler frequency shift detection, providing raw data for subsequent precise positioning. The ground control center receives the beacon return data and positioning information relayed by the drone, decrypts and verifies the identity verification data, and executes an iterative convergence algorithm in combination with multi-moment observation results to finally determine the precise position of the search and rescue target. The entire system is divided into three stages, mainly including three stages: beacon activation, identity verification, and precise positioning. The specific details of each stage are as follows: First, in the beacon activation stage, the drone platform sends an activation signal that has been BPSK modulated and spread spectrum processed, which includes a preamble and a synchronization word. The activation signal carries pseudo-random sequence information for fast matching and anti-interception. After signal enhancement and relay forwarding, it ensures a larger coverage range in complex terrains. Second, in the identity verification stage, after the beacon is activated, it constructs a 128-byte encrypted data packet signal. The data packet includes auxiliary information such as a frame header, beacon ID, RSSI, and CRC check. This data packet signal is encoded by a polar code and modulated by OFDM multi-carrier, and then transmitted to the ground control center through the drone platform. At the same time, the drone also participates in data verification and two-way identity verification during the transmission process. Third, in the positioning stage, the drone platform collects narrowband signals emitted by the search and rescue target through an omnidirectional antenna array assembled on it, measures the phase difference, Doppler frequency shift, and signal change rate of the signals. The drone uploads the angle and change rate data to the ground control center, and the ground control center uses the Kalman filter and iterative convergence algorithm to obtain the final positioning result. The specific algorithm process is as follows: Beacon activation, as Figure 3 shown, the specific steps are as follows: Setting of the activation signal. The activation signal includes a preamble and a synchronization word. The preamble is designed and fixed to quickly identify the start of the signal. The synchronization word uses an improved Logistic chaotic mapping model to generate a pseudo-random code sequence to enhance the anti-interception ability of the signal. Specifically, as shown in the following formula: (1) In the formula, is the chaos parameter, is the perturbation factor, x k and x k+1 represent sequences at adjacent times, k is the time.

[0022] Perform BPSK modulation and spread spectrum processing on the activation signal to complete the construction of the activation signal, as follows: Improve the anti-interference ability and recognition accuracy of the signal by increasing the number of bits, and then perform BPSK modulation on the activation signal: (2) In the formula, is the binary modulation signal, is the function of the BPSK modulation signal changing with time; the modulation process has low power consumption and is suitable for short-pulse signal transmission. Adopt the spreading factor SF. The unmanned aerial vehicle is equipped with a spectrum sensing module to collect the environmental noise power spectral density in real time, dynamically select the spreading factor according to the noise intensity, improve the anti-interference ability by increasing the spreading factor, and at the same time balance the transmission efficiency of the activation signal, and spread the activation signal: (3) (4) In the formula, is the function of the activation signal changing with time, is the signal amplitude, is the carrier frequency, is the binary modulation signal, is the spreading code sequence, is the pulse width, t is the time. The spread spectrum technology expands the activation signal in the frequency domain, improves the anti-interference ability, and is beneficial to signal capture in a multipath environment.

[0023] Activation signal forwarding. After the unmanned aerial vehicle receives the modulated activation signal, it autonomously selects the optimal relay node according to the received signal strength gradient distribution. The relay decision function is as follows: (5) In the formula, and are the weight coefficients, is the maximum allowable delay, and RSSI is the received signal strength; The unmanned aerial vehicle dynamically adjusts the forwarding gain according to the transmission link loss model, as follows: (6) In the formula, H is the transmission distance, B is the attenuation constant, G 基准is the reference gain, G 动态 is the dynamic gain.

[0024] The beacon device of the object to be searched and rescued receives the activation signal from the UAV. After the preamble and sync word are matched and confirmed, it immediately wakes up from the standby state and enters the authentication stage.

[0025] The authentication stage is as follows: In the authentication stage, the beacon device constructs an encrypted identity data packet signal and transmits it through the communication link between the UAV platform and the ground control center to ensure secure data transmission and establish a trusted link. The encrypted data packet signal constructed by the beacon device is a 128-byte encrypted data packet, which includes a frame header, a beacon ID, RSSI, CRC check, and other auxiliary information.

[0026] The beacon device is also integrated with a quantum random number generator, which generates true random numbers based on the quantum entropy source of single-photon unilateral. The output rate of the seed entropy source is as follows: (7) In the formula, is the detection efficiency, is the laser pulse frequency, is the wavelength, is the attenuation function of the output rate. The dynamic key is transmitted to the ground control center through the quantum key distribution protocol and is used for auxiliary encryption during the data packet signal transmission to provide a dynamic secret key.

[0027] The encrypted data packet signal is successively subjected to polar code encoding and OFDM multi-carrier modulation, as follows: (8) (9) In the formula, P is the pre-designed polar code sequence, is the AES encryption operation on the polar code sequence, is the data bit, is the encrypted data bit, i is the code sequence. This process further improves the transmission reliability of data in a harsh channel; the encoded data is modulated through OFDM multi-carrier: (10) In the formula, is the modulation symbol on the sub-carrier, L is the total number of sub-carriers, is the sub-carrier interval, j is the imaginary unit, f m is the carrier frequency of different sub-carriers,m is a natural number, is the initial carrier frequency. The data packet signal after encoding and modulation is transmitted to the ground control center for positioning through the data transmission link established by the UAV platform. At the same time, the UAV participates in data verification and two-way authentication as a relay node.

[0028] In the positioning stage, as Figure 4 shown, three omnidirectional antennas are deployed on the UAV platform, arranged in an equilateral triangle with side length d, and the side length is optimized to λ / 2, where λ is the signal wavelength. The position coordinates of the antennas relative to the centroid of the UAV are u1, u2, and u3, forming a space diversity reception structure. The antenna unit uses a microstrip patch antenna; the encrypted data packet signal emitted by the search and rescue target is a narrowband signal with a carrier frequency , and the UAV moves at a constant speed flight speed. The positioning process is as follows: Use the phase difference of the signals received by adjacent antennas to calculate the azimuth angle of the search and rescue target: (11) In the formula, is the wavelength, is the baseline length, is the baseline direction angle, is the phase noise, h and g are the serial numbers of the three omnidirectional antennas. The azimuth angle of the search and rescue target is as follows: (12); Measure the change rate of the azimuth angle of the search and rescue target. Using the Doppler frequency shift method, calculate the change rate of the azimuth angle through the Doppler frequency shift difference of the three antennas: (13) In the formula, is the Doppler frequency difference between antennas h and g , is the wavelength. At the same time, perform Kalman filtering on the azimuth angle at consecutive moments to extract the change rate ; Suppose the spatial position relationship between the search and rescue target and the UAV at the k th moment is: (14) In the formula, is the azimuth angle of the search and rescue target at the k th moment, is the true position coordinate of the search and rescue target, is the position coordinate of the UAV. Take the derivative and conversion of the above formula to obtain the kThe distance between the UAV and the search and rescue target at a certain moment Specifically, the formula is as follows: (15) (16) In the formula, is the straight-line distance between the UAV and the search and rescue target at the moment of , is the azimuth angle and pitch angle of the search and rescue target detected at the moment of ; The distance between the UAV and the search and rescue target is calculated by using the azimuth angle and pitch angle, so as to obtain the estimated position of the search and rescue target.

[0029] Optimize the above positioning. The optimization process is to optimize the azimuth angle and the azimuth angle change rate through Kalman filtering, and use the optimized data to obtain the preliminary positioning result ; The estimated position of the search and rescue target is obtained by using the optimized data. Specifically as follows: Use Kalman filtering to obtain the optimized azimuth angle and its change rate , and then import the optimized result into the azimuth angle change rate positioning to obtain the estimated value of the search and rescue target position . The following analyzes the azimuth angle change model and establishes the corresponding state equation: (26) In the formula, represents the detected azimuth angle, represents the detected azimuth angle change rate, represents the process noise.

[0030] In Kalman filtering, the innovation value is used to dynamically adjust the measurement noise covariance : (27) In the formula, is the smoothing factor.

[0031] During the Kalman filtering process, is the process noise covariance, is adjusted based on the long-term performance of the prediction error, and the adjustment form is basically similar to , and the improvement is achieved by adjusting and .

[0032] Use the adjusted to obtain the covariance matrix of the innovation : (28) Wherein, represents the measurement matrix of the system.

[0033] Calculate the updated Kalman gain as: (29) Using the innovation and the updated Kalman gain, the updated state equation and covariance matrix can be obtained, and iterative processing is performed step by step to complete the filtering process.

[0034] Substitute the filtered azimuth angle and its rate of change into the above formula to obtain the optimized ranging result and the preliminary positioning result : (17) (18) Perform secondary optimization on the positioning to obtain the final estimated positioning result, specifically as follows: The target relationship between the UAV and the search and rescue target is: (19) Wherein, represents the estimated position coordinates of the search and rescue target, represents the observed position of the UAV, , respectively represent the azimuth angle and pitch angle of the search and rescue target at the th observation moment, represents the geometric relationship of the search and rescue target at the k th moment, as shown in the following formula: (20) Using the above formula, we can obtain: (21) Then, an iterative formula is derived. First, obtain the initial position coordinates of the search and rescue target, can be set to any value, and then use the azimuth angle and pitch angle to perform iterative estimation of the position of the search and rescue target, and obtain the th iteration result of the position of the search and rescue target at the th observation moment as: (22) CombineV Iteration results at a certain observation moment , and the centroid calculation formula is used to calculate the positioning result of the search and rescue target at the th observation moment: (23) In the formula, , represents the position of the search and rescue target obtained in the th iteration, which is transformed into a three-dimensional space coordinate system. The specific calculation formula is: (24) Using the iteration result of the previous point as the iteration initial value of the next point, when the difference between the iteration results of two adjacent iterations is less than the set convergence threshold, it is determined that the iteration converges. At this time, the iteration result is the final position of the search and rescue target. The iteration upper limit is set to , and the convergence threshold is , obtaining the iteration termination condition: (25).

[0035] After the multi-dimensional collaborative optimization mechanism is adopted in the present invention, through actual measurement, the UAV platform uses dynamic relay and cooperative signal enhancement technology, enabling the system coverage range to be expanded by 3 to 5 times, and the communication blind area area under complex terrain can be reduced by more than 50%; at the same time, after fusing multi-source perception data and intelligent optimization algorithms, the positioning error in a non-line-of-sight environment is reduced from 10 - 50 meters of the traditional solution to only 5 - 20 meters, and the accuracy is improved by 80% to 90%. In addition, by adopting the adaptive channel strengthening and dynamic anti-interference strategy, even in a noise environment above 30 dB, the bit error rate can be reduced from more than 15% to less than 10%; the ultra-low power consumption design extends the device battery life from the original 72 hours to more than 5 days. The above specific effects are significantly improved, providing reliable and economical technical support for wide-area search and rescue missions.

[0036] Specific embodiments are applied in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present invention, several improvements and modifications can still be made to the present invention, and these improvements and modifications also fall within the protection scope of the claims of the present invention.

Claims

1. A method for UAV search and rescue positioning with low-power bidirectional beacon collaboration, characterized in that, The method specifically includes the following: Beacon activation: An activation signal is sent by the drone, and the activation signal triggers the start of the beacon device of the object to be searched and rescued, completing the beacon activation. Identity verification: After the beacon is activated, the beacon device constructs an encrypted data packet signal, and then transmits the data packet signal to the ground control center through the drone, completing the identity verification. The ground control center controls the drone to start searching for the position of the object to be searched and rescued according to the data packet signal. Positioning: The drone collects the data packet signal sent by the object to be searched and rescued, measures the azimuth angle and the change rate of the azimuth angle of the object to be searched and rescued through the data packet signal, and determines the position of the object to be searched and rescued.

2. The method for UAV search and rescue positioning with low-power bidirectional beacon collaboration according to claim 1, characterized in that, The beacon activation specifically includes the following: Constructing the activation signal: The activation signal includes a preamble and a synchronization word that match the beacon device. Signal modulation: The activation signal is modulated by BPSK and spread spectrum processed to obtain the modulated activation signal. Signal forwarding: The modulated activation signal is sent to the drone through the ground control center, and then the drone sends the modulated activation signal. Beacon activation: After the beacon device receives the activation signal sent by the drone, it completes the beacon activation after the preamble and synchronization word are matched and confirmed.

3. The method for UAV search and rescue positioning with low-power bidirectional beacon collaboration according to claim 2, characterized in that, The construction process of the preamble and the synchronization word is as follows: The preamble is fixed and used to quickly identify the start of the activation signal. The synchronization word is a pseudo-random code sequence generated by an improved Logistic chaotic mapping model, as shown in the following formula: （1） Wherein, is a chaotic parameter, is a perturbation factor, x k , x k+1 represent sequences at adjacent times, k is the time.

4. The method for UAV search and rescue positioning with low-power bidirectional beacon collaboration according to claim 2, characterized in that, The BPSK modulation is specifically as follows: （2） In the formula, is a binary modulation signal, is a function of the BPSK modulation signal changing with time; The spread spectrum processing is specifically as follows: （3） （4） wherein, is a function of the activation signal varying with time, is the signal amplitude, is the carrier frequency, is the binary modulation signal, is the spread spectrum code sequence, is the pulse width, t is the time.

5. The method for UAV search and rescue positioning with low-power bidirectional beacon collaboration according to claim 2, characterized in that, In the signal forwarding, the number of drones is several. Several drones forward the activation signal to each other, and then several drones send the activation signal. During the process of the drones receiving the modulated activation signal and forwarding it to each other, the optimal relay node is autonomously selected according to the received signal strength gradient distribution. The relay decision function is as follows: （5） Wherein, and are weighting coefficients, is the maximum allowable delay, and RSSI is the received signal strength; The drone dynamically adjusts the forwarding gain, as follows: （6） In the formula, H is the transmission distance, B is the attenuation constant, G 基准 is the reference gain, G 动态 is the dynamic gain.

6. The method for UAV search and rescue positioning with low-power bidirectional beacon collaboration according to claim 1, characterized in that, The beacon device is also integrated with a quantum random number generator, which generates true random numbers based on the quantum entropy source on one side of a single photon. The output rate of the seed entropy source is specifically as follows: （7） Wherein, is the detection efficiency, is the laser pulse frequency, is the wavelength, is the attenuation function of the output rate. The dynamic key is transmitted to the ground control center through the quantum key distribution protocol and is used for auxiliary encryption during the data packet signal transmission process.

7. A method for UAV search and rescue positioning with low-power two-way beacon collaboration according to claim 6, characterized in that, Before the data packet signal is transmitted, it needs to be encoded with a polar code and modulated with OFDM multi-carriers in sequence, specifically as follows: （8） （9） In the formula, P is a pre-designed polar code sequence, is to perform AES encryption operation on the polar code sequence, is the data bit, is the encrypted data bit, i is the code sequence. The encoded data is modulated by OFDM multi-carrier modulation, and the modulation function is as follows: （10） Wherein, is the modulation symbol on the subcarrier, L is the total number of subcarriers, is the subcarrier spacing, j is the imaginary unit, f m is the carrier frequency of different subcarriers, m is a natural number, is the initial carrier frequency. The data packet signal after coding and modulation is transmitted to the ground control center for positioning through the data transmission link established by the UAV platform.

8. A method for UAV search and rescue positioning with low-power two-way beacon collaboration according to claim 1, characterized in that, The positioning specifically includes: Deploying three omnidirectional antennas on the drone in an equilateral triangle distribution to receive the data packet signal sent by the object to be searched and rescued. The specific positioning process is as follows: Using the phase difference of signals received by adjacent antennas Calculate the azimuth of the object to be searched and rescued: （11） Wherein, is the wavelength, is the baseline length, is the baseline direction angle, is the phase noise, h , g are the serial numbers of three omnidirectional antennas, and the azimuth angle of the object to be searched and rescued is as follows: （12）； Measuring the change rate of the azimuth angle of the object to be searched and rescued, using the Doppler frequency shift method, and calculating the change rate of the azimuth angle through the Doppler frequency shift difference of the three antennas: （13） In the formula, is the antenna h , g is the Doppler frequency difference, is the wavelength. At the same time, the azimuth angles at consecutive moments are subjected to Kalman filtering to extract the change rate ; Suppose at the k time, the spatial position relationship between the object to be searched and rescued and the UAV is as follows: （14） Wherein, is the azimuth angle of the object to be searched and rescued at the k th moment, is the true position coordinate of the object to be searched and rescued, is the position coordinate of the UAV. By taking the derivative and conversion of the above formula, the distance k between the UAV and the object to be searched and rescued at the th moment is obtained as follows: （15） （16） Wherein, is the straight-line distance between the UAV and the search and rescue target at time , are the azimuth angle and pitch angle of the search and rescue target detected at time ; the distance between the UAV and the search and rescue target is calculated using the azimuth angle and pitch angle, so as to obtain the estimated position of the search and rescue target.

9. A method for UAV search and rescue positioning with low-power two-way beacon collaboration according to claim 8, characterized in that, Optimizing the positioning, specifically as follows: Obtain the filtered azimuth angle through Kalman filtering and its rate of change , substitute them into equations (15) and (16) to obtain the optimized ranging result and the preliminary positioning result : （17） （18）。 10. A method for UAV search and rescue positioning with low-power two-way beacon collaboration according to claim 8, characterized in that, Performing secondary optimization on the positioning to obtain the final positioning result, specifically as follows: The target relationship between the drone and the object to be searched and rescued is: （19） In the formula, represents the estimated position coordinates of the object to be searched and rescued, represents the observation position of the UAV, , respectively represent the azimuth angle and elevation angle of the object to be searched and rescued at the th observation moment, represents the geometric relationship of the object to be searched and rescued at the k th moment, as shown in the following formula: （20） It can be obtained by using the above formula: （21） Then, an iterative formula is derived. First, the initial position coordinates of the object to be searched and rescued are obtained. , which can be set to any value. Then, using the azimuth angle and the elevation angle to iteratively estimate the position of the object to be searched and rescued, the position of the object to be searched and rescued at the th observation time and the th iteration result are as follows: （22） Combined with V the iterative results at observation times, the centroid calculation formula is used to calculate the positioning result of the object to be searched and rescued at the th observation time: （23） In the formula, , represents the position of the search and rescue target obtained in the th iteration, which is converted into a three-dimensional space coordinate system. The specific calculation formula is as follows: （24） Use the iterative result of the previous point as the initial value of the next point. When the difference between the iterative results of two adjacent iterations is less than the set convergence threshold, it is determined that the iteration converges. At this time, the iterative result is the final position of the object to be searched and rescued. Set the iteration upper limit to , and the convergence threshold is , to obtain the iteration termination condition: （25）。

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