Novel Multi-Object Filtering Approach for Space Situational Awareness

Abstract

Surveillance activities with ground-based assets in the context of space situational awareness are particularly challenging. The observation process is indeed hindered by short observation arcs, limited observability, missed detections, measurement noise, and contamination by clutter. This paper exploits a recent estimation framework for stochastic populations for space situational awareness surveillance scenarios. This framework shares the flexibility of the finite set statistics framework in the modeling of a dynamic population of objects and the representation of all the sources of uncertainty in a single coherent probabilistic framework and the intuitive approach of traditional trackbased techniques to describe individual objects and maintain track continuity. We present a recent multi-object filtering solution derived from this framework, the filter for distinguishable and independent stochastic populations, and propose a bespoke implementation of the multitarget tracking algorithm for a space situational awareness surveillance activity. The distinguishable and independent stochastic populations filter is tested on a surveillance scenario involving two ground-based Doppler radars in a challenging environment with significant measurement noise, limited observability, missed detections, false alarms, and no a priori knowledge about the number and the initial states of the objects in the scene. The tracking algorithm shows good performance in initiating tracks from object-generated observations and in maintaining track custody throughout the scenario, even when the objects are outside of the sensors' fields of view, despite the challenging conditions of the surveillance scenario.Nomenclature α y = probability of existence of previously detected target with observation path y cH = probability of existence of hypothesis H H = hypothesis, i.e., subset of compatible tracks in Y (H, n) = multitarget configuration, describing a possible composition of population X H tjt−1 ; H t = set of all hypotheses, propagated by the filter for distinguishable and independent stochastic populations (DISP) lz; x = likelihood of association between target with state x and observation with state z P tjt−1 , P t = law of whole population of targets, propagated by the distinguishable and independent stochastic populations filter P a = law of population of appearing targets p d x = probability of detection of target with state x p fa z = probability that observation with state z is false alarm p y = probability distribution of previously detected target with obs. path y p ϕ = probability distribution of each yet-to-be-detected target wH; n = joint probability of existence of targets in configuration (H, n) X = population of targets X = target state space X = target state space augmented with empty state ψ x = target state (e.g., position and velocity coordinates) Y tjt−1 , Y t = set of all observation paths, propagated by distinguishable and independent stochastic populations filter y = observation path or track characterized by said o...

Multitarget Filtering With Linearized Complexity

“…It appears that the predicted configurationP t|t−1 takes the same form as the posterior configurationP t−1 at time t − 1 expressed in eq. (5). Although the single-target laws of disappeared targets are not very informative, they can be useful in practice since the scalar…”

“…By considering the usual assumptions of MTT, a first algorithm following from this paradigm has been introduced in [13,Chapt. 3] and detailed in [7], [5] and is referred to as the filter for distinguishable and independent stochastic populations (DISP filter). This filter can be derived without relying on approximations but unsurprisingly displays high computational complexity, similarly to the MHT.…”

Incomplete Information Pursuit-Evasion Game Control for a Space Non-Cooperative Target

“…Therefore, protecting the safety of space assets in face of these threats is critical for cleaning space. Many studies have been done to find a way out, e.g., space situational awareness, on-orbit servicing and so on [5][6][7][8][9][10][11][12]. Developing on-orbit servicing vehicles with corresponding GNC (guidance, navigation and control) systems used to handle the space debris, inactive satellites and military vehicles threating to the space asset is the most effective method for safety.…”

Trajectory Optimization for Multitarget Tracking Using Joint Probabilistic Data Association Filter

“…A IRBORNE target tracking is a key enabling technology in many civilian and military applications, including situation awareness [1][2][3], vehicle monitoring [4,5], public surveillance [6], and traffic management [7]. However, reliable target tracking becomes challenging when dealing with highly complex multitarget tracking (MTT) problem.…”