Target tracking method and truncated integral Kalman filtering method and device

Abstract

The invention discloses a target tracking method, a target tracking system, a truncated integral Kalman filtering method and a truncated integral Kalman filtering device. The truncated integral Kalman filtering method comprises the following steps of obtaining an original prior probability density function in a target state according to a Gauss-Hermite integral; obtaining a first posterior probability density function in the target state according to the original prior probability density function; correcting the original prior probability density function according to a target observation vector at a current target observation moment to obtain a corrected prior probability density function; obtaining a second posterior probability density function in the target state according to the corrected prior probability density function; obtaining a joint posterior probability density function in the target state according to the first posterior probability density function and the second posterior probability density function. By applying the target tracking method, the target tracking system, the truncated integral Kalman filtering method and the truncated integral Kalman filtering device, the prior distribution variance in the target state can be effectively reduced; the state update is implemented in a self-adapting way according to the precision of observation information; the filtering precision is effectively improved; the practicability is higher.

Description

technical field [0001] The invention relates to the field of nonlinear filtering, in particular to a target tracking method and system, and a truncation integral Kalman filtering method and device. Background technique [0002] Passive sensors (such as infrared, sonar, etc.) themselves do not emit electromagnetic waves. They detect the location of the target by receiving infrared and electromagnetic waves radiated by engines, communications, radars, etc., which take the target as a carrier, or external electromagnetic waves reflected by the target. information. Usually, multiple passive sensors are used to form a passive sensor array to observe the same target to realize the tracking of the target. [0003] Aiming at the problem of target tracking in passive sensor arrays, the existing technology mainly adopts the following methods: The first is to adjust the state model of the target adaptively to achieve accurate tracking of the target, such as interactive multi-model ( ...

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Problems solved by technology

[0004] The inventors of the present application have discovered in the long-term research and development that the first method in the prior art cannot detect the maneuvering target in time when the target maneuvers suddenly, resulting in a decrease in the tracking performance of the target during maneuvering, so that the target may be lost; based on The second method of particle filtering will increase the particle dimension and calculation amount with the increase of the target state dimension, which is generally difficult to be practically applied.

In addition, when the above two methods are used to track the target, when the target maneuvers, due to the inaccuracy of the motion model and the existence of observation errors, the prediction error of the target increases rapidly, resulting in an increase in the variance of the prior distribution of the target state. Object tracking performance degrades

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