Target advanced predicting and tracing method

Abstract

The invention discloses a target advanced predicting and tracing method. A motion state of a target can be predicted in advance under the condition of low observation noise influence. The target advanced predicting and tracing method comprises the steps of firstly building a motion model for a moving target; then carrying out advanced prediction, that is, priori estimation, for the motion state ofthe target in the next moment by adopting a square root volume kalman filter method; building a Markov transition probability model by counting an error sequence between the priori estimation value and an observation value before the current moment; predicting a priori prediction error at the next moment by using the Markov transition probability model and correcting an advanced prediction valueat the next moment by using the error. The advanced predicting and tracing method can predict the motion state of the target in advance and can provide a target state prediction value in advance for atracing control system; and the control system can act in advance based on the advanced prediction value to overcome tracing lag and can keep continuous tracing for the target based on the advanced prediction value when the target is shielded.

Description

technical field [0001] The invention relates to the field of image, radar and sonar signal processing. Background technique [0002] Target tracking is to use signal processing methods to estimate the target state information in image, radar, sonar and other applications, and is widely used in radar and video tracking, wireless laser communication, astronomical observation, aerial photography and other fields. [0003] In the target tracking system, the transmission and processing of the target observation signal will cause the observation lag of the target information. At the same time, when the control system dynamically tracks the moving target through the actuator, there will be a certain control lag. In many cases, the impact of noise on the target tracking effect is far less than the impact of observation and control lag on the entire tracking system. If the motion state of the target can be predicted in advance, the target state prediction value can be provided in ad...

AI Technical Summary

Problems solved by technology

[0004] The existing moving target tracking methods generally obtain the optimal estimated value of the state by applying the filtering method after obtaining the observed value of the moving state of the target. The purpose of filtering is mainly to overcome the influence of interference noise, but it cannot overcome the tracking problem. lag

When filtering is used for target state prediction and tracking, the prediction accuracy depends on the distribution of interference noise, and the one-step prediction accuracy is limited, which cannot meet the accuracy requirements of prediction and tracking

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