Three-satellite passive fusion positioning system maneuvering target tracking method

Abstract

The invention belongs to the technical field of passive tracking of maneuvering targets by three-satellite formation, and particularly relates to a maneuvering target tracking method of a three-satellite passive fusion positioning system. According to the invention, an improvement is provided for a three-satellite fusion positioning system, iteration is introduced, and an L-M method is used to improve extended Kalman filtering. The truncation error of a strong nonlinear system linearized by extended Kalman filter can be reduced with fewer iterations; the improved IEKF is used in particle filtering to generate an importance density function, and latest observation information is fused, so that the generated importance density function is closer to the actual posterior probability, and the tracking filtering precision is improved. According to the invention, high-precision tracking can be carried out on the moving radiation source target without elevation prior information.

Description

technical field [0001] The invention belongs to the technical field of passive tracking of a maneuvering target by a three-satellite formation, and in particular relates to a tracking method of a maneuvering target in a Samsung passive fusion positioning system. Background technique [0002] Spaceborne positioning and tracking is playing an increasingly important role in various fields because of its advantages such as long detection distance, wide monitoring range, not affected by regions, and good concealment performance. With the development of science and technology, the target of satellite positioning has changed from the traditional static target on the ground to the target positioning of the mobile radiation source covering the land, sea and airspace. The traditional time difference and frequency difference positioning method often introduces the error caused by the unknown target speed when positioning the high-speed mobile target. On the other hand, high-speed maneu...

AI Technical Summary

Problems solved by technology

In 2017, Zhu Jianfeng proposed to realize the three-dimensional positioning of airspace targets by combining the direction-of-arrival (DOA) direction-finding information on the basis of the three-star time difference (TDOA) information. However, when the accuracy of the DOA is insufficient, the positioning effect of the algorithm will be severely reduced.

In the same year, he proposed to Zhang Jun to use the Extended Kalman Filter (EKF) algorithm to track moving targets. However, due to the high nonlinearity of the tracking system of the Samsung fusion system, a certain truncation error will be introduced when using EKF to linearize the system, which will affect the filtering. Accuracy, compared with EKF, particle filter (PF) does not require the degree of nonlinearity of the system, but when using the standard particle filter algorithm to achieve target tracking, because the algorithm uses the transition probability of the system state as the importance density function, it does not The latest observation information is effectively used, resulting in a certain deviation between the particle sampling sample and the true posterior probability, which in turn leads to a decrease in filtering accuracy

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