A multi-sensor target tracking filtering method and system

Abstract

The invention discloses a multi-sensor target tracking filtering method and system. The method includes: acquiring the measured value of the pitch angle, the measured value of the azimuth angle, the measured value of the distance and the autocovariance of the sensor detected by the sensor relative to the tracking target; calculating the measured values ​​of the pitch angle detected by multiple sensors difference, azimuth measurement value difference and distance measurement value difference; the pitch angle measurement value, azimuth measurement value and distance measurement value are reconstructed to obtain the reconstructed pitch angle measurement value, Reconstructed azimuth measurement and reconstituted distance measurement; extend from reconstituted pitch measurement, reconstituted azimuth measurement and reconstituted distance measurement Kalman filtering to obtain the tracking and positioning results of the target. By adopting the method and system of the present invention, the influence of time-varying correlation noise existing between multiple sensors on the target tracking performance can be effectively eliminated, and the target tracking accuracy can be ensured.

Description

Technical field[0001]The invention relates to the technical field of target tracking and positioning, in particular to a multi-sensor target tracking filtering method and system.Background technique[0002]Target tracking technology has a pivotal position and an important role in industry, military, and daily life, and has received extensive attention and applications. For example, in the industrial field, the automatic identification, sorting and grasping system of workpieces is an important application of target tracking technology. In addition, target tracking in fields such as mobile robots, high-precision assembly, coastal surveillance, air control, and other video surveillance systems The system also has important applications.[0003]With the increase in the number of detection targets, increased mobility, and faster speeds, the data measured by a single sensor can no longer meet the growing demand. People began to explore integrating the measurement data of multiple sensors, ext...

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Problems solved by technology

However, in the actual use process, the above conditions are difficult to be satisfied due to the following reasons: multiple sensors are located on the same moving carrier, and the impact of the carrier vibration and impact is the same, and the working environment and other factors are very difficult for multiple sensors. The impact is the same, the above-mentioned same interference factors will lead to the inevitable correlation of multi-sensor measurement noise; in addition, because the sensor environment, device performance and other influencing factors are not constant, resulting in multi-sensor measurement noise The correlation also changes accordingly, which makes it impossible to calibrate and compensate the correlation in advance. This time-varying correlation will directly affect the accuracy of the Kalman filter, and may even cause the filter to diverge, thereby directly reducing the target tracking. the accuracy of

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