Federated Kalman filtering-based method for fusing multilateration data and radar data

Abstract

The invention relates to a Federated Kalman filtering-based method for fusing multilateration data and radar data, comprising seven processes: pretreatment, time and space registration, error correction, single-sensor line tracking or airborne trace interconnection and filtration, data association, data fusion as well as error estimation. On the basis of multiclass monitoring information, the technical scheme of the invention establishes data association mapping relations among all classes of targets, constructs a process method fusing the multilateration system data and the radar data, realizes deep fusion of all classes of information and comprehensive utilization on effective information, ensures high updating rate of a multilateration system and the whole precision of system monitoringwhen period is less than or equal to 1sec, reduces impact of radar measuring error on system monitoring precision, and significantly improves the tracking precision of the system.

Description

technical field [0001] The invention relates to a multi-sensor data fusion processing method, in particular to a multi-point positioning data and radar data fusion method based on joint Kalman filtering. Background technique [0002] When an aircraft is moving on the ground or flying in the air, dynamic surveillance must be carried out and the whole process of control must be implemented to ensure flight safety. Therefore, a variety of surveillance sensors are installed at airports and air routes to obtain information on the activities and situation of the charged targets. [0003] Multi-sensor data fusion processing technology is the key technology of Airport Surface Movement Guidance Control System (A-SMGCS). It can combine various sensor information connected to the system for fusion processing to obtain unique, accurate, real-time, and effective information, and display it on the terminal to help controllers implement surface surveillance and control tasks. [0004] Th...

AI Technical Summary

Problems solved by technology

[0014] The existing technical solutions have the following disadvantages: (1) the data fusion between the air traffic control radar and the multilateration system cannot make the system track reach a higher accuracy

In this way, even if the multipoint positioning system has high measurement accuracy, it cannot guarantee that the system output results have high accuracy.

(2) It is impossible to solve the problem that the performance of the entire system decreases due to the decrease in the detection accuracy of a certain sensor

As the sensor ages, its detection accuracy will decrease

In addition, changes in the weather will also affect the detection accuracy of the sensor to a certain extent

However, in the current actual use, the detection accuracy of the sensor is not considered when processing the detection data sent by the sensor, and the data sent by all sensors are treated equally

In this way, if there is a serious drop in the detection accuracy of a certain sensor, according to the existing technical solution, it will directly lead to a drop in system accuracy

(3) The problem that the multilateration system has no stable period cannot be solved

However, the multi-point positioning system does not have a stable update cycle, and generally sends several measurement values ​​​​of the same target within 1 second.

Can't deal with such problem according to existing technical scheme

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