In order to improve the surveillance effect of the aviation target surveillance radar, this paper improves the traditional filtering algorithm and builds the channel optimization system of the ADS-B aviation target surveillance radar based on the improved filtering algorithm. Moreover, this paper uses algorithm improvement to ensure the positive definite or semipositive definiteness of the state covariance and uses the root mean square volume Kalman filter to avoid the filter divergence or tracking interruption caused by the nonpositive definiteness of the matrix; the filtering principle of the interactive multimodel is to use multiple filters for parallel processing and achieve the adaptive adjustment algorithm residual error by adjusting the one-step prediction covariance in the adjustment algorithm. In addition, this paper combines the actual needs to construct a system functional structure to optimize the channel of the ADS-B aviation target surveillance radar and uses software engineering methods to model and analyze the requirements. Finally, this paper designs experiments to verify system performance. The research results show that the performance of the system constructed in this paper meets actual needs.

No abstract

No abstract

Considering the problem that measurement outliers exist in ADS-B monitoring system and seriously affect the stability and accuracy of the Kalman filter, an improved "current" statistical model Kalman filtering algorithm has been proposed in this paper. The algorithm can dynamically adjust the acceleration variance and the maneuvering frequency, automatically identify and eliminate outliers, through a combination of CA model, so as to realize the track forward and reverse extrapolation and smoothing data loss. Simulation results show that the algorithm can not only effectively eliminate outliers and reduce the adverse impact on the filtering accuracy, but also has high tracking accuracy in the weak or the high maneuvering situation.