Maneuvering target tracking method based on fading memory sequential detector

Abstract

The invention discloses a maneuvering target tracking method based on a fading memory sequential detector. Aiming at advantages and disadvantages of a decision-based single-model tracking method and a multi-model tracking method, the invention provides the maneuvering target tracking method based on the fading memory sequential detector. The method comprises the following steps of: performing Kalman filter-unscented Kalman filter (KF-UKF) combined filtration, wherein Kalman filtration based on a uniform model is adopted for a non-maneuvering target; when a maneuvering target is detected, expanding the dimension of a coefficient of variation (CV) model, and performing state estimation by adopting an unscented Kalman filter (UKF); detecting maneuvering by using the fading memory sequential detector; and after the maneuvering is detected, correcting a dimension expanded state vector and a covariance matrix, so that the matched state is matched with the actual moving mode. By the method, the state of the target can be corrected in real time, and matched filters can be selected according to different moving modes, so that the tracking precision is improved.

Description

technical field [0001] The invention belongs to the field of maneuvering detection and maneuvering target tracking, and relates to a maneuvering target tracking method based on an attenuation memory sequential detector. Background technique [0002] In recent years, mobile target tracking has been widely used in military and civilian fields such as air traffic control (ATC), ground reconnaissance and surveillance, and homing guidance. The difficulty of maneuvering target tracking mainly comes from the uncertainty of target motion and state observation. The uncertainty of observation refers to the uncertainty of the target identity, which belongs to the category of multi-target tracking; the uncertainty of target motion mainly refers to the uncertainty of the target motion model, mainly including: unknown maneuver input and process noise statistical characteristics, these All factors will cause the mismatch between the tracking model and the target motion mode, resulting in ...

AI Technical Summary

Problems solved by technology

Usually, the detection probability increases with the increase of the window length, but the increase of the window length will lead to the increase of the maneuver detection delay, which has a huge impact on the target tracking performance; while the reduction of the window length can reduce the maneuver detection delay , but the false alarm probability will increase accordingly, so how to choose the window length is the difficulty of this type of detector

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