A method for tracking fixed-wing UAVs in a satellite-denied environment

Abstract

The present invention discloses a method for monitoring the track of a fixed-wing UAV in a satellite-denied environment. The present invention divides the entire autonomous navigation process into two stages. In the first stage, TOA positioning is solved with N flight moments as a group, and preliminary trajectory fitting is performed. When the distance between the next moment position predicted by the trajectory and the anchor node and the TOA measurement distance of the actual next moment position are less than the set threshold, the second stage is performed. The actual measured distance information is corrected using the position on the fitting trajectory, and the distances between multiple trajectory prediction positions and corresponding anchor nodes are converted into one position and the positions of all anchor nodes, thereby achieving more accurate TOA positioning. The present invention realizes high-precision autonomous navigation during the movement of the UAV under satellite-denied conditions; and the anchor nodes deployed on the ground do not need to communicate with each other, do not need time synchronization between the anchor nodes, and do not need time synchronization between the anchor nodes involved in positioning and the UAV.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) positioning technology, and in particular to a method for monitoring the trajectory of a fixed-wing UAV in a satellite-denied environment. Background Art

[0002] In a satellite-denied environment, when the ground-deployed anchor nodes cannot communicate with each other and cannot synchronize time, the TOA (Time of Arrival) method is generally used to locate the UAV. By communicating with each anchor node in turn to obtain the propagation time of the measurement signal between the anchor node and the UAV, the measured time parameter is converted into the distance required for positioning, thereby achieving positioning. The schematic diagram of its positioning principle is shown below. Figure 1 shown. Figure 1 The distances between the three anchor nodes BS1, BS2, and BS3 and the target node MS are calculated as R1, R2, and R3 respectively. The intersection of the three circles is the position of the target node MS to be located. That is, formula (1):

[0003]

[0004] Traditional time-of-arrival (TOA) methods require strict time synchronization between the anchor node and the drone involved in positioning. TOA methods calculate position by measuring the time it takes for the signal to travel from the anchor node to the positioning target. This time reflects the difference in distance the signal traveled through space. If the time between the anchor node and the positioning target is out of sync, the measured time will be inaccurate, leading to inaccurate positioning results.

[0005] For a stationary (hovering) drone, although the drone can only communicate with one anchor node at a time to obtain a distance, if the drone remains stationary during the time required to obtain the distance information required for a TOA, multiple ranging measurements of the drone are at the same position, which can also ensure good positioning accuracy.

[0006] However, for fixed-wing drones, their positions in the air are constantly changing and they cannot hover in the air. When TOA is used to measure the distance to multiple anchor nodes in sequence, the drone position corresponding to each distance measurement is different. If the traditional TOA method is used for positioning, the drone position during these distance measurements is generally regarded as the same position, which increases the positioning error.

[0007] In 2017, Peng Shusheng, He Ye and others from Nanjing University of Science and Technology proposed a method based on TDOA (time difference of An indoor three-dimensional positioning method based on arrival time difference (TDOA) and time of arrival (TOA) is specifically as follows: three reference nodes A, B, and C are placed at the same height, and reference node D is placed on a plane higher than the first three nodes to establish a three-dimensional coordinate system; reference node A is used as the base node, and signals are sent to other reference nodes to obtain a first time difference, and the other reference nodes respectively send the first time difference to the reference node; the node to be measured sends signals to all reference nodes respectively, and after receiving the signals, the reference nodes respectively obtain a second time difference, and the reference nodes respectively send the second time difference to the reference nodes; the reference node obtains the time difference between the reference node and other reference nodes and between the node to be measured and all reference nodes based on the first and second time differences, and the time difference is multiplied by the speed of light to obtain distance information, the distances between the reference nodes A, B, C, and D and the node to be measured are Ri, i = 1, 2, 3, 4, and the distance differences between the reference nodes B and C and the reference node A to the node to be measured are R2, 1, and R3, 1; the reference node calculates the position coordinates of the node to be measured M based on the TDOA and TOA information. Although the TDOA method does not require strict time synchronization between the positioning target and the anchor node, it still requires time synchronization between the anchor nodes, and the accuracy of time synchronization will affect the positioning accuracy. Summary of the Invention

[0008] In view of this, the present invention provides a method for tracking fixed-wing UAVs in a satellite-denied environment, which does not require time synchronization between anchor nodes and UAVs, nor does it require time synchronization between anchor nodes. It can realize the positioning and track monitoring of fixed-wing UAVs in a satellite-denied environment.

[0009] The method for monitoring the track of a fixed-wing UAV in a satellite-denied environment of the present invention comprises:

[0010] Step 1: During flight, the UAV communicates with each ground anchor node in turn and uses the Time of Observation (TOA) method to measure the distance between each flight time and the corresponding anchor node. The UAV positioning solution is performed using N adjacent flight times as a group to obtain the UAV positioning results corresponding to each group.

[0011] Step 2: Based on the positioning results of the corresponding drones in each group, the motion trajectory is fitted and the flight position at the next moment is predicted;

[0012] Step 3: Calculate the distance between the predicted flight position at the next moment and the corresponding anchor node, and compare it with the distance between the flight position and the anchor node actually measured using the TOA method at the next moment; if the difference between the two distances is less than the set threshold, proceed to step 4; otherwise, return to step 1;

[0013] Step 4: Based on the fitted motion trajectory, estimate the flight position at the next N moments; calculate the distances d_initial1 to d_initialN between the estimated flight position at the next N moments and the corresponding anchor nodes; at the same time, calculate the distances d_last1 to d_lastN between the estimated flight position at the next Nth moment and each anchor node; subtract d_initial from d_last to obtain the distance correction deta_d between the estimated flight position at each moment in the next N moments and the corresponding anchor node;

[0014] Step 5: At the next N flight times, use TOA to measure the distance between the flight time and the corresponding anchor node, and use the distance correction value obtained in step 4 to compensate the TOA measurement distance; perform drone positioning based on the compensated TOA measurement distance, update the fitted trajectory, and return to step 4 until the navigation ends.

[0015] Preferably, in step 1, the Chan algorithm is used to perform drone positioning calculation for a group of N TOA measurement distances to obtain the drone positioning result corresponding to the group.

[0016] Preferably, in step 2, polynomial fitting, Bezier curve, Kalman filtering or least squares method is used to fit the motion trajectory.

[0017] Preferably, in step 3, when three consecutive differences are all smaller than the set threshold, step 4 is executed; otherwise, the process returns to step 1.

[0018] Preferably, N is the number of ground communication anchor nodes; the number of ground communication anchor nodes is greater than or equal to 4.

[0019] Beneficial effects:

[0020] The present invention divides the entire autonomous navigation process into two phases. In the first phase, TOA positioning is performed using N flight times as a group, and a preliminary trajectory fitting is performed. By comparing the distance between the predicted trajectory position and the anchor node and the actual measured distance, when the distance is less than a set threshold, the second phase, i.e., the precise positioning and navigation phase, is carried out. In the second phase, the actual measured distance information is corrected using the position on the fitted trajectory, and the distances between multiple trajectory predicted positions and corresponding anchor nodes are converted into the distance between one position and all anchor nodes. This improves the accuracy of the distance used for TOA positioning and achieves more accurate TOA positioning. The present invention achieves high-precision autonomous navigation during the movement of unmanned aerial vehicles under satellite denial conditions. Furthermore, the anchor nodes deployed on the ground do not need to communicate with each other, nor do they require time synchronization between the anchor nodes, nor do they require time synchronization between the anchor nodes involved in positioning and the unmanned aerial vehicles. The present invention can achieve autonomous navigation of multiple unmanned aerial vehicles simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a TOA positioning diagram.

[0022] Figure 2 This is a principle block diagram of the UAV autonomous navigation method in a satellite denial environment of the present invention.

[0023] Figure 3 This is a flow chart of the autonomous navigation method of a UAV in a satellite-denied environment according to the present invention.

[0024] Figure 4 This is a schematic diagram of the first stage scenario.

[0025] Figure 5 This is a schematic diagram of the second stage scenario.

[0026] Figure 6 This is the simulation result of the UAV autonomous navigation method based on ranging in a satellite denial environment. DETAILED DESCRIPTION

[0027] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0028] This invention provides a method for tracking fixed-wing UAVs in a satellite-denied environment. Under satellite-denied conditions, multiple anchor nodes are deployed on the ground that do not require mutual communication (a UAV can only communicate with one anchor node at a time). The UAV in the air sequentially communicates with different anchor nodes, using the Time of Arrival (TOA) method to obtain distance information and perform positioning calculations. The entire autonomous positioning and navigation process can be divided into two stages. In the first stage, a preliminary navigation route is obtained through position approximation and trajectory fitting. The obtained navigation route includes the TOA results and the interpolation of the TOA results during trajectory fitting. Then, after a state transition, the second stage is entered, where the TOA ranging is compensated to correct the positioning results, enabling the UAV to navigate autonomously.

[0029] The principle block diagram of the present invention is as follows Figure 2 As shown in the flow chart Figure 3 For the traditional TOA positioning method, at least 4 anchor nodes need to be deployed on the ground. The present invention is improved based on the TOA method and also requires at least 4 anchor nodes to be deployed on the ground. The following description takes the deployment of 5 anchor nodes on the ground as an example.

[0030] The scene diagrams of the first and second stages are as follows: Figure 4 、 Figure 5 The dotted line represents the predicted flight trajectory of the drone, the solid airplane icon and the five-pointed star represent the actual location of the drone, and the hollow airplane icon represents the predicted location of the drone based on the trajectory.

[0031] In the first phase, the distance between the drone and the anchor nodes is continuously measured using the Time of Arrival (TOA) algorithm to perform a positioning solution. Unlike traditional TOA processes, the multiple distances required for each TOA positioning are obtained because the drone is constantly moving and each measurement only captures the distance to a single anchor node. Therefore, the distances obtained are not the distances from the same drone to different anchor nodes, but rather the distances from different drone locations to the corresponding anchor nodes. Therefore, the drone's position at each of these distance measurements must be approximately considered the same. This method is used to perform multiple TOA positioning operations. The TOA positioning results are then used to perform trajectory fitting, predicting the drone's trajectory and ultimately determining the drone's position at the next moment. Initially, due to the limited amount of position information involved in trajectory fitting, the predicted position deviates significantly from the actual position. As the TOA process continues, the amount of position information involved in trajectory fitting gradually increases, and the deviation between the predicted position and the actual position decreases. However, this error persists. By setting an error threshold, when the error falls below the threshold, the second phase begins.

[0032] Among them, in actual situations, there is always an error in the location of the base station, that is, the time from the target node to the base station measured by TOA has an error, and there may be no solution to the equation group. In this case, the TOA-based Chan algorithm can be used; this algorithm first assumes that the solutions of the equation group are independent of each other, linearizes them, and uses weighted least squares to obtain their estimated values. Then, the first least squares method is used to estimate the relationship between them, and finally the target position is solved.

[0033] Specifically, such as Figure 4 As shown, the drone performs a TOA ranging measurement every Δt. Starting from the drone's takeoff, the five distance information obtained every 5Δt is considered a group, and the five distances actually obtained by the drone at five locations are considered to be obtained at one location. The Chan algorithm used in TOA positioning is applied to each group of distances to perform position calculation, and a TOA positioning result Q is obtained every 5Δt. The motion trajectory of the drone is fitted using multiple groups of TOA positioning information, and the position of the drone at the next Δt can be inferred based on the trajectory. Trajectory fitting methods that can be used in the present invention include but are not limited to polynomial fitting, Bezier curves, Kalman filtering, and least squares methods.

[0034] At the next Δt, the difference between the distance d between the drone's position and the anchor node at that moment, as estimated by trajectory estimation, and the distance d1 measured by actual communication is calculated. This difference serves as the transition threshold between the first and second stages. If the difference is greater than the set threshold, the first stage TOA process is repeated; if it is less than the threshold three times in a row, the second stage is entered.

[0035] In the second phase, the drones continue to communicate with different anchor nodes to obtain distance information. Although the TOA method is still used for positioning, the method for obtaining the distance information required for a TOA calculation is different from the first phase. Using fitted trajectories, the distance information obtained from different drone positions used for the same TOA positioning can be converted to the distance information of the same position. TOA positioning is then performed, and the fitted trajectory is continuously iterated, thereby reducing errors.

[0036] Specifically, such as Figure 5 As shown in Figure 2, based on the established UAV trajectory, the trajectory is used to infer the five positions of the UAV at the next five △t moments, and the distances d_initial1 to d_initial5 between the five UAV positions predicted by the trajectory and the corresponding five anchor nodes are calculated; and the distances d_last1 to d_last5 between the UAV position predicted by the trajectory and each anchor node at the next 5△t moment are calculated; d_last minus d_initial is used to obtain the distance correction deta_d corresponding to the next five △t moments, as shown in formula (2).

[0037] deta_d=d_last-d_initial (2)

[0038] Add deta_d as a correction value to the distances d_real1 to d_real5 from the corresponding anchor nodes actually measured by TOA at the corresponding positions at the next five △t moments, and correct the TOA distance value, as shown in formula (3).

[0039] d_real=deta_d+d_initial (3)

[0040] The five corrected distance values ​​can be considered the distances from the drone's location to the five anchor nodes after 5Δt. This step is equivalent to position conversion. Unlike the first phase, where the distances measured at the five locations are directly used for TOA positioning, this position conversion can be considered as using the distance measured at a single location for TOA positioning, resulting in a new Q value. The corrected distances are then used for position calculation every 5Δt, and the new Q value is continuously used to update the fitted trajectory. The positioning process is repeated to achieve continuous localization of the drone's position.

[0041] The following is an explanation with reference to specific examples.

[0042] Five anchor nodes are deployed on the ground, and the positions of the anchor nodes are BS1 = [0, 0, 0], BS2 = [-5000, 0, 0], BS3 = [0, -5000, 0], BS4 = [-5000, -5000, 0], and BS5 = [-2500, -2500, 250]. Assuming that the ranging is performed every 0.1 seconds, that is, △t = 0.1s, the initial velocity of the UAV in the x, y, and z directions is 200m / s, and the acceleration obeys a normal distribution with a variance of 50, the UAV is simulated to make irregular movements in three-dimensional space. Comparing the results of positioning solution using the traditional TOA chan algorithm and the positioning solution using the method of the present invention, as shown in the figure. Figure 6 After error analysis, in the current scenario, when the UAV is about 30 kilometers away from the origin, the error of traditional TOA positioning is about 5%, while the positioning error of the present invention is about 3%, which shows the reliability of the present invention.

[0043] In summary, the above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring the trajectory of a fixed-wing UAV in a satellite-denied environment, characterized in that: include: Step 1: During flight, the UAV communicates with each ground anchor node in turn and uses the Time of Observation (TOA) method to measure the distance between each flight time and the corresponding anchor node. The UAV positioning solution is performed using N adjacent flight times as a group to obtain the UAV positioning results corresponding to each group. Step 2: Based on the positioning results of the corresponding drones in each group, the motion trajectory is fitted and the flight position at the next moment is predicted; Step 3: Calculate the distance between the predicted flight position at the next moment and the corresponding anchor node, and compare it with the distance between the flight position and the anchor node actually measured using the TOA method at the next moment; if the difference between the two distances is less than the set threshold, proceed to step 4; otherwise, return to step 1; Step 4: Based on the fitted motion trajectory, estimate the flight position at the next N moments; calculate the distances d_initial1 to d_initialN between the estimated flight position at the next N moments and the corresponding anchor nodes; at the same time, calculate the distances d_last1 to d_lastN between the estimated flight position at the next Nth moment and each anchor node; subtract d_initial from d_last to obtain the distance correction deta_d between the estimated flight position at each moment in the next N moments and the corresponding anchor node; Step 5: At the next N flight times, use TOA to measure the distance between the flight time and the corresponding anchor node, and use the distance correction value obtained in step 4 to compensate the TOA measurement distance; perform drone positioning based on the compensated TOA measurement distance, update the fitted trajectory, and return to step 4 until the navigation ends.

2. The method according to claim 1, wherein In step 1, the Chan algorithm is used to perform drone positioning calculation for a group of N TOA measurement distances to obtain the drone positioning result corresponding to the group.

3. The method according to claim 1, wherein In step 2, motion trajectory fitting is performed using polynomial fitting, Bezier curve, Kalman filtering or least squares method.

4. The method according to claim 1, 2 or 3, wherein: In step 3, when three consecutive differences are all smaller than the set threshold, step 4 is executed; otherwise, step 1 is returned.

5. The method according to claim 1, wherein The N is the number of ground communication anchor nodes; the number of ground communication anchor nodes is greater than or equal to 4.

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