Adaptive state estimation for tracking of civilian aircraft. Issue 6 (1st September 2018)

• Record Type: Journal Article
• Authors: Patra, Nilanjan; Sadhu, Smita; Ghoshal, Tapan Kumar
• Is Part Of: IET science, measurement & technology. Volume 12:Issue 6(2018)
• Page Start: 777
• Page End: 784
• Publication Date: 2018-09-01
• Subjects: Monte Carlo methods -- nonlinear estimation -- aircraft -- adaptive estimation -- state estimation; nonlinear adaptive state estimation -- manoeuvring subsonic aerospace vehicle tracking -- civilian aircraft tracking -- adaptive single mode nonlinear estimator -- smooth variable structure filter -- SVSF -- interacting multiple model estimator -- IMM estimator -- Monte Carlo simulation; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2017.0529
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : Tracking of manoeuvring subsonic aerospace vehicles has traditionally been handled by state estimators. Ordinary state estimators perform poorly as the concerned process model can only be defined imprecisely. This contribution evaluates and compares the performance of adaptive single mode non‐linear estimators against several versions of other estimators. The primary comparison is with the recently introduced smooth variable structure filter (SVSF) which is claimed to inherit the robustness of variable structure approach. Both the above types of estimators are then benchmarked with a well‐known version of interacting multiple model (IMM) estimator which treats the manoeuvring aircraft as a hybrid system consisting of multiple modes. Monte Carlo simulation has been used and several descriptors have been used for comparison. The comparison demonstrates that a version of non‐linear adaptive estimators incorporating sigma points and a single constant turn model performs substantially better than the SVSF and its tracking performance approaches that obtainable by the IMM estimator. Novelty of this contribution lies in providing a detailed comparison of the above three families of estimators, which provides adequate insight for selecting tracking estimators by trading off estimation accuracy, algorithm complexity, tuning requirement, and computational load.