Passive tracking multi-target method based on probability hypothesis density (PHD) of time difference measurement box particles

Abstract

The invention discloses a passive tracking multi-target method based on probability hypothesis density (PHD) of time difference measurement box particles, and mainly aims to solve the problem of low operation efficiency of an existing particle filter technology under a passive tracking multi-target situation. The method comprises the following implementation steps: (1) acquiring initial tracking moment box particles; (2) acquiring new box particles; (3) merging the new box particles and the box particles; (4) predict the combined box particles; (5) updating the predicted box particles; (6) resampling the updated box particles; (7) acquiring a multi-target state; (8) judging whether the number of time difference measurements obtained by each passive base station is 0 or not, if so, ending the tracking, and if not, returning to the step (2) to continue tracking. According to the method, the box particles are updated by using a time-difference constraint propagation function, so that multiple targets with passive tracking measurement of time difference can be achieved with fewer box particles, thereby reducing the time complexity of an algorithm and improving the efficiency of multi-target passive tracking.

Description

technical field [0001] The invention belongs to the technical field of guidance, and further relates to a passive tracking multi-target method based on PHD (Probability Hypothesis Density) filtering in the field of target tracking technology. The invention can carry out real-time passive tracking of multiple targets that can radiate electromagnetic waves through the time difference measurement obtained by the passive base station. The invention can be used in the fields of satellite navigation or passive tracking of military targets. Background technique [0002] The common method for locating and tracking the target is to use active equipment such as radar and sonar. However, active equipment needs to transmit signals, which is easy to reveal its own position. Passive (passive) positioning and tracking technology uses the electromagnetic waves radiated by the target itself to locate, which has the advantages of good concealment and strong survivability, and is of great sig...

AI Technical Summary

Problems solved by technology

This method can passively track multiple targets according to angle measurement. However, this method still has the disadvantage that a large number of particles are required to participate in the calculation during the tracking process, and the tracking efficiency is low, which greatly affects its tracking effect.

This method replaces more point particles with fewer box particles, which solves the problem of low particle filter operation efficiency to a certain extent. However, this method still has the disadvantage that because the measurement and state are linear Relationship, that is, the state of the target is the coordinate, and the measurement is also the coordinate. The measurement and the state are the same physical quantity and their dimensions are the same. When using the CP criterion to constrain the box particle to update the weight, it is only necessary to compare the overlapping part of the two

However, in the passive tracking of time difference measurement, the relationship between measurement and state is highly nonlinear, that is, the state coordinates of the target, the measurement is the time difference value, and the state and measurement are different physical quantities with different dimensions, so it is impossible to constrain the particle Update weights, so this method cannot be applied to multiple targets whose passive tracking is measured as time difference

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