CN116184314B

CN116184314B - ADS-B site position selection method for civil aviation multi-point positioning

Info

Publication number: CN116184314B
Application number: CN202310100885.1A
Authority: CN
Inventors: 孙金龙
Original assignee: Nanjing University of Posts and Telecommunications
Current assignee: Nanjing University of Posts and Telecommunications
Priority date: 2023-02-10
Filing date: 2023-02-10
Publication date: 2025-07-29
Anticipated expiration: 2043-02-10
Also published as: CN116184314A

Abstract

The present invention discloses an ADS-B station location selection method for multi-lateration positioning of civil aviation. Based on the ADS-B trajectory data of historical flights along the route, the signal arrival time of the aircraft to each station is obtained by inversion calculation, and then the aircraft solution position coordinates are obtained using the MLAT solution algorithm; the ADS-B position coordinates and the aircraft solution position coordinates are compared to calculate the solution error; with the optimal position of the station as the target, the station position is obtained through multiple iterative calculations, and the position wandering direction angle is updated according to a preset random deviation; the optimal position of the current station is obtained, and this position is used as the optimal position of the ADS-B station to be deployed. The present invention can effectively optimize the station position along a specific path. Only a few iterative processes are required, and the accuracy of the multi-lateration positioning algorithm can be improved ten to a hundred times.

Description

ADS-B site position selection method for civil aviation multi-point positioning

Technical Field

The invention relates to the technical field of aircraft positioning and tracking, in particular to an ADS-B site position selection method for civil aviation multipoint positioning.

Background

Air traffic has experienced explosive growth in the last decade, and in order to avoid aviation accidents and even collisions in such high density airspace, a consensus has been developed worldwide that uses ATM (AIR TRAFFIC MANAGEMENT ) technology and rules to divide aircraft by altitude and distance, namely, integrated use of ATS (AIR TRAFFIC SERVICE, air traffic services), ASM (AIR SPACE MANAGEMENT ), and ATFM (AIR TRAFFIC Flow Management) to achieve safe operation of the aircraft. The key technology for realizing airspace division and collision avoidance is that all aircrafts in the air can be continuously positioned and tracked. Conventional aircraft positioning and tracking techniques rely on radar systems, mainly including air traffic control primary and secondary radar, but are limited by radar range, which greatly reduces their effect in transoceanic and remote areas of flight.

For the purpose of worldwide tracking and monitoring of aircraft, in particular flights, the ITU (International Telecommunications Union ) has agreed upon an integrated space-air-ground positioning and tracking system using ADS-B (Automatic Dependent Surveillance-Broadcast automatic dependent surveillance) information. On the other hand, the aim of a future high-capacity space-air-ground integrated communication network is achieved, and urgent demands are also put forward for high-precision real-time positioning and tracking technology of the aircraft.

In view of the features and challenges of a single ADS-B system, in combination with the evolution of aircraft tracking and monitoring technology, solutions can be sought from the multi-device, co-operating mode direction, and multi-point location (MLAT) systems are just for such consideration. The MLAT can utilize the same aircraft ADS-B broadcast signals received by a plurality of ground stations to estimate the track information of the target aircraft without demodulating the ADS-B messages through the capture of signal level features. Because the MLAT mode does not depend on the demodulated message information and a plurality of (more than 3) ground station receivers are mutually redundant, the positioning precision and the anti-interference capability of the system can be improved theoretically.

In a multi-point positioning system, the position distribution condition of a ground receiver station has an important influence on the positioning accuracy of an MLAT algorithm, and the ground station has an optimal position and needs to be optimally deployed. From the complexity of system design and deployment, for example, when the MLAT algorithm requires at least five sites to cooperatively complete the position calculation and decision, if the planar position space is considered as a grid with intervals of every 500m on a 1000km×1000km plane, the size of the solution space is on the order of 10 ³³, which cannot be obtained through brute force exhaustion.

Disclosure of Invention

The invention aims to provide an ADS-B site position selection method for civil aviation multipoint positioning, which can effectively carry out optimizing selection on site positions along a specific path, reduce iterative processes and improve the precision of a multipoint positioning algorithm.

In order to achieve the purpose, the invention provides the following technical scheme that the ADS-B site position selection method for civil aviation multipoint positioning comprises the following steps:

S1, acquiring signal arrival time of an aircraft to each station by inversion calculation by utilizing aircraft track ADS-B position coordinates and initializing N ADS-B station positions to be deployed based on historical flight ADS-B track data of a route;

s2, calculating and obtaining the arrival time difference of the aircraft reaching the rest N-1 sites by using the arrival time of the signals from the aircraft to each site, and obtaining the aircraft resolving position coordinate by using the arrival time difference of the N-1 sites as input by using an MLAT resolving algorithm based on TDOA;

S3, taking the optimal position of the station as a target, presetting a station moving step length and a position drifting direction angle change range based on the station position and the resolving error of the last iteration of the MLAT resolving algorithm of the TDOA in the step S2, respectively moving N stations to obtain N moving schemes, resolving the position of an aircraft of the N moving schemes and the corresponding N resolving errors, and taking the minimum value in the N resolving errors as the resolving error of the iteration;

S4, comparing the calculation error of the current iteration with the calculation error of the last iteration, taking the site position corresponding to the smaller calculation error as the site position of the current iteration, updating the position shifting direction angle according to the preset random deviation, and then returning to the execution step S3 until the preset maximum iteration times are reached, so as to obtain the optimal position of the current site, and taking the position as the optimal position of the ADS-B site to be deployed.

Further, in the aforementioned step S3, the range of the positional displacement direction angle is not more than pi/N-1.

The step S3 is specifically that based on the last iteration station position (S_pos_last_x, S_pos_last_y) and the solution error, the station movement step and the position excursion direction angle change range adt are preset, and the station is respectively tried to move, wherein the following formula is adopted:

S_pos_try_x=S_pos_last_x+step*cos(Angle_now),

s_pos_try_y =s_pos_last y + step sin (Angle now), where angle_now is the position excursion direction Angle.

Further, in the step S4, the preset random deviation range is greater than or equal to-pi/N-1 and less than or equal to pi/N-1.

Further, in the aforementioned step S4, updating the position drift direction angle includes the following steps:

S4.1, comparing the calculation error of the iteration with the calculation error of the previous iteration, if the calculation error of the iteration is smaller, executing the step S4.2, otherwise, executing the step S4.3;

s4.2, adding a random deviation on the basis of the previous position shifting direction angle, and taking the added result as an updated position shifting direction angle;

And S4.3, adding a random deviation and an angle pi to carry out large-angle inversion on the basis of the previous position shifting direction angle, and taking the added result of the three as an updated position shifting direction angle.

Further, in the foregoing step S4.2, a random deviation is added to the previous position-shifting direction angle, and the added result is used as an updated position-shifting direction angle, where the following formula is:

Angle_now=Angle_last+adt·(2rand-1),

where rand is a random number between 0, 1.

Further, in the step S4.3, a random deviation is added to the previous position-shifting direction angle, and the angle pi is added to perform large-angle inversion, and the result of adding the three is used as the updated position-shifting direction angle, where the following formula is:

Angle_now=Angle_last+adt·(2rand-1)+π,

where rand is a random number between 0, 1.

Compared with the prior art, the invention has the following beneficial effects:

1. before the ADS-B site is newly built, the historical ADS-B data are collected and analyzed, so that the building position of the newly built site can be evaluated and optimized, the optimal site selection position is determined, and the accuracy and reliability of multipoint positioning of the aircraft in the navigation scene are improved.

2. Compared with the existing site screening method, the iterative optimization method adopted by the method is high in convergence speed, and the computational complexity of violent solving is avoided.

3. The method is suitable for different multi-point positioning algorithms, can be flexibly embedded into the existing multi-point positioning algorithm module, and provides a better reference site scheme for the multi-point positioning algorithm.

4. The method can help to reduce the early analysis decision cost and reduce the manpower and material resources for improving the intelligentization level of air traffic management construction.

Drawings

Fig. 1 is a flow chart of the method of the present invention.

Fig. 2 is a graph of the comparison of the tracking track errors of the aircraft before and after the site optimization, in which, (a) is a tracking track graph of the aircraft before the site optimization, and (b) is a tracking track graph of the aircraft after the site optimization.

Detailed Description

For a better understanding of the technical content of the present invention, specific examples are set forth below, along with the accompanying drawings.

Aspects of the invention are described herein with reference to the drawings, in which there are shown many illustrative embodiments. The embodiments of the present invention are not limited to the embodiments described in the drawings. It is to be understood that this invention is capable of being carried out by any of the various concepts and embodiments described above and as such described in detail below, since the disclosed concepts and embodiments are not limited to any implementation. Additionally, some aspects of the disclosure may be used alone or in any suitable combination with other aspects of the disclosure.

As shown in fig. 1, the flow chart of the invention is an ADS-B site location selection method for civil aviation multipoint positioning, comprising the steps of:

S1, selecting a certain navigation path of a civil aviation as an optimization object, and acquiring a large amount of historical flight ADS-B track data in the navigation path; based on historical flight ADS-B track data of the route, initializing 5 ADS-B site positions to be deployed, and carrying out inversion calculation by utilizing the ADS-B position coordinates of the aircraft track and the 5 ADS-B site positions to be deployed to obtain signal arrival time of the aircraft to each site;

S2, calculating and obtaining arrival time differences of the aircraft reaching the rest 4 stations by using signal arrival time of the aircraft to each station, obtaining aircraft resolving position coordinates by using an MLAT resolving algorithm based on TDOA, taking the arrival time differences of the 4 stations as input, comparing ADS-B position coordinates with the aircraft resolving position coordinates, calculating and obtaining resolving errors, S3, taking the optimal position of the station as a target, presetting a station position (S_pos_last_x, S_pos_last_y) and resolving errors of the last iteration of the MLAT resolving algorithm based on the TDOA, and presetting a station moving step length and a position drifting direction angle change range adt, wherein the range is not more than pi/4. Then, the 5 stations are respectively tried to move, and the trying positions are as follows:

S_pos_try_x=S_pos_last_x+step*cos(Angle_now),

S_pos_try_y =s_pos_last y + step sin (Angle now), angle_now is the position excursion direction Angle.

Obtaining 5 movement schemes, then resolving the positions of the aircrafts of the 5 schemes and the corresponding 5 resolving errors, and taking the minimum value in the 5 resolving errors as the resolving error of the iteration;

S4, comparing the calculation error of the current iteration with the calculation error of the last iteration, taking the site position corresponding to the smaller calculation error as the site position of the current iteration, and simultaneously according to the preset random deviation, the random deviation range is more than or equal to-pi/4 and less than or equal to pi/4. Then updating the position shifting direction angle in the following specific updating mode:

s4.2, adding a random deviation based on the previous excursion direction angle, and taking the added result as an updated position excursion direction angle, namely

Angle_now=Angle_last+adt·(2rand-1)

Wherein rand is a random number between [0,1 ];

s4.3, adding a random deviation and an angle pi to carry out large-angle inversion on the basis of the previous position migration direction angle, and taking the added result of the three as an updated position migration direction angle, namely

Angle_now=Angle_last+adt·(2rand-1)+π

Where rand is a random number between 0, 1.

And (3) based on the calculation error, the new position and the new position migration direction angle of the current iteration period, returning to the execution step (S3) until the preset maximum iteration times are reached, obtaining the optimal position of the current site, and taking the position as the optimal position of the ADS-B site to be deployed. As shown in fig. 2, the comparison result of the tracking track errors of the aircraft before and after the site optimization is shown in fig. 2 (a) is a tracking track map of the aircraft before the site optimization and (b) is a tracking track map of the aircraft after the site optimization. By the method, the optimal site selection position is determined, and the accuracy and reliability of multipoint positioning of the aircraft in the navigation scene are improved.

While the invention has been described in terms of preferred embodiments, it is not intended to be limiting. Those skilled in the art will appreciate that various modifications and adaptations can be made without departing from the spirit and scope of the present invention. Accordingly, the scope of the invention is defined by the appended claims.

Claims

1. A method for selecting ADS-B station locations for civil aviation multilateration, comprising the following steps:

S1. Based on the historical ADS-B trajectory data of the route, the aircraft's ADS-B position coordinates and the initial positions of N ADS-B stations to be deployed are used to inversely calculate the arrival time of the aircraft's signal to each station.

S2. Using the arrival time of the aircraft's signals from each station, calculate the arrival time difference of the aircraft at the remaining N-1 stations. Using the TDOA-based MLAT solution algorithm, with the arrival time difference of the N-1 stations as input, obtain the aircraft's solved position coordinates. Compare the ADS-B position coordinates with the aircraft's solved position coordinates to calculate the solution error.

S3. Targeting the optimal station position, based on the station position and solution error of the previous iteration of the MLAT solution algorithm for TDOA in step S2, and presetting the station movement step size and the range of position wandering direction angle, N stations are moved separately to obtain N movement schemes. The aircraft positions and the corresponding N solution errors for the N movement schemes are then solved, and the minimum value of the N solution errors is used as the solution error for this iteration.

S4. Compare the solution error of this iteration with the solution error of the previous iteration, and use the site position corresponding to the smaller solution error as the site position of the current iteration. At the same time, update the position wandering direction angle according to the preset random deviation; then return to step S3 and execute until the preset maximum number of iterations is reached. The optimal position of the current site is obtained and used as the optimal position of the ADS-B site to be deployed.

2. The ADS-B station location selection method for civil aviation multilateration according to claim 1, wherein in step S3, the range of the position wandering direction angle is no greater than π/N-1.

3. The ADS-B station location selection method for civil aviation multilateration according to claim 1, wherein step S3 specifically comprises: based on the station location (S_pos_last_x, S_pos_last_y) of the previous iteration and the solution error, the station movement step length step and the position wandering direction angle change range adt are preset, and the stations are tried to move respectively, as follows: S_pos_try_x = S_pos_last_x + step*cos(Angle_now),

S_pos_try_y=S_pos_last_y+step*sin(Angle_now), where Angle_now is the position wandering direction angle.

4. The ADS-B station location selection method for civil aviation multilateration according to claim 1, wherein in step S4, the preset random deviation range is: greater than or equal to -π/N-1 and less than or equal to π/N-1.

5. The ADS-B station location selection method for civil aviation multilateration according to claim 4, wherein in step S4, updating the position wandering direction angle comprises the following steps:

S4.1. Compare the solution error of this iteration with the solution error of the previous iteration. If the solution error of this iteration is smaller, proceed to step S4.2; otherwise, proceed to step S4.3.

S4.2. Add a random deviation to the last position wandering direction angle, and use the result of the addition as the updated position wandering direction angle;

S4.3. Add a random deviation to the last position wandering direction angle, and then add angle π to perform a large angle reversal. The sum of the three is used as the updated position wandering direction angle.

6. The ADS-B station location selection method for civil aviation multilateration according to claim 5, characterized in that in step S4.2, a random deviation is added to the previous position wandering direction angle, and the result of the addition is used as the updated position wandering direction angle, as shown in the following formula:

Angle_now=Angle_last+adt·(2rand-1),

Here, rand is a random number between [0,1].

7. The ADS-B station location selection method for civil aviation multilateration according to claim 6, characterized in that in step S4.3, a random deviation is added to the previous position wandering direction angle, and an angle π is added to perform a large angle reversal, and the sum of the three is used as the updated position wandering direction angle, as shown in the following formula:

Angle_now=Angle_last+adt·(2rand-1)+π,

Here, rand is a random number between [0,1].