Parametric analysis of ballistic target‐tracking problem by multiple model approach

Abstract

Radar tracking of a ballistic target is a topic presenting very complex issues especially when the characteristics of the objects\ud flying into the Earth’s atmosphere are poorly known. As the target identification is required to obtain good performance, a technique\ud frequently employed in the practical applications is the multiple model filter. This study, thought the increasing interest for this class of\ud estimators, aims to identify the key parameters of the problem by making recognisable their effects on the solution. Thus – along with\ud the ideal operative conditions – the relative geometry target-sensor, the presence of missing plots, the measurement uncertainties and\ud the credibility of filter predictions are investigated. In order to have a strict benchmark of evaluation the multiple model approach\ud performance is compared with the optimal estimate given by posterior Cramér–Rao lower bounds

Knowledge‐based multiple hypothesis tracking and identification of manoeuvring reentry targets

“…On the contrary, the other approaches in the latter category try to tackle the target tracking and identification problem within the framework of the multiple model filtering. This process involves hypothesising multiple target motion models according to the typical acceleration characteristics of the target's class or flight mode [11][12][13][14][15][16][17]. Each model hypothesis is then evaluated to give model probabilities for the classification of target into predefined target groups.…”

Near Space Hypersonic Vehicle Target Tracking Adaptive Non-Zero Mean Model

“…Up to now, the maneuvering target tracking model is divided into two major categories of ballistics models and dynamics models [1] [3]. The ballistic model was first applied to the 21 tracking of ballistic targets and the prediction of landing 22 points [5], this model analyzes the acceleration generated 23 by different forces [6]. It can be further divided into reentry 24 model and boost phase model according to the forces in 25 different stages of ballistic trajectory, and it is suitable for 26 target tracking with relatively simple forces [7] [8].…”

Switching Multiple Model Filter for Boost-Phase Missile Tracking

“…Nevertheless, IMMUFs set-up remains a difficult subject; it relies on a priori information [15] or dedicated analysis [16]. Besides, most literature's IMMUFs consider the probabilities of the state transitioning between modes constant [17]- [21].…”

“…Besides, most literature's IMMUFs consider the probabilities of the state transitioning between modes constant [17]- [21]. This requires two quite conservative hypotheses: i) that the (non-constant) probability of the TBM transitioning between phases are well approximated by constant values; and ii) that these constant probabilities are a priori known [3], [15], [16].…”