Aleksandar Zatezalo

Background agnostic CPHD tracking of dim targets in heavy clutter

Detection and tracking of dim targets in heavy clutter environments is a daunting theoretical and practical problem. Application of the recently developed Background Agnostic Cardinalized Probability Hypothesis Density (BA-CPHD) filter provides a very promising approach that adequately addresses all the complexities and the nonlinear nature of this problem. In this paper, we present analysis, derivation, development, and application of a BA-CPHD implementation for tracking dim ballistic targets in environments with a range of unknown clutter rates, unknown clutter distribution, and unknown target probability of detection. The effectiveness and accuracy of the implemented algorithms are assessed and evaluated. Results that evaluate and also demonstrate the specific merits of the proposed approach are presented.

Joint Search and Sensor Management of Space Based EO/IR Sensors for LEO Event Estimation

We derive new algorithms for Low Earth Orbit (LEO) event estimation based on joint search and sensor management of space based EO/IR sensors. Our approach is based on particle representation of hypothesized probability densities and the Posterior Expected Number of Objects of Interest sensor management objective function. We address scientific and practical challenges of this LEO estimation problem in the context of space situational awareness. These challenges include estimating changes in satellites trajectories, estimating current trajectories (localization), and estimating future collisions with other LEO space objects. Simulations and the results obtained using actual LEO satellites are presented.

Optimal constellation design of low earth orbit (LEO) EO/IR sensor platforms for space situational awareness

Constellations of EO/IR space based sensors can be extremely valuable for space situational awareness. In this paper, we present trade-off analysis and comparisons of different Low Earth Orbit (LEO) EO/IR sensor platform constellations for space situational awareness tasks. These tasks include early observation of changing events, and localization and tracking of changing LEO orbits. We derive methods and metrics for evaluation, testing, and comparisons of different sensor constellations based on realistic models and computationally efficient methods for simulating realistic scenarios.

EO/IR Satellite Constellations for the Early Detection and Tracking of Collision Events

The detection and tracking of collision events involving existing Low Earth Orbit (LEO) Resident Space Objects (RSOs) is becoming increasingly important with the higher LEO space objects traffic volume which is anticipated to increase even further in the near future. Changes in velocity that can lead to a collision are hard to detect early on time, and before the collision happens. Several collision events can happen at the same time and continuous monitoring of the LEO orbit is necessary in order to determine and implement collision avoidance strategies. We present a simulation of a constellation system consisting of multiple platforms carrying EO/IR sensors for the detection of such collisions. The presented simulation encompasses the full complexity of LEO trajectories changes which can collide with currently operating satellites. Efficient multitarget filter with information-theoretic multisensor management is implemented and evaluated on different constellations.

Multimodel filtering of partially observable space object trajectories

In this paper we present methods for multimodel filtering of space object states based on the theory of finite state time nonhomogeneous cadlag Markov processes and the filtering of partially observable space object trajectories. The state and observation equations of space objects are nonlinear and therefore it is hard to estimate the conditional probability density of the space object trajectory states given EO/IR, radar or other nonlinear observations. Moreover, space object trajectories can suddenly change due to abrupt changes in the parameters affecting a perturbing force or due to unaccounted forces. Such trajectory changes can lead to the loss of existing tracks and may cause collisions with vital operating space objects such as weather or communication satellites. The presented estimation methods will aid in preventing the occurrence of such collisions and provide warnings for collision avoidance.

Space collision threat mitigation

Mitigation of possible collision threats to current and future operations in space environments is an important an challenging task considering high nonlinearity of orbital dynamics and discrete measurement updates. Such discrete observations are relatively scarce with respect to space dynamics including possible unintentional or intentional rocket propulsion based maneuvers even in scenarios when measurement collections are focused to a one single target of interest. In our paper, this problem is addressed in terms of multihypothesis and multimodel estimation in conjunction with multi-agent multigoal game theoretic guaranteed evasion strategies. Collision threat estimation is formulated using conditional probabilities of time dependent hypotheses and spacecraft controls which are computed using Liapunov-like approach. Based on this formulation, time dependent functional forms of multi-objective utility functions are derived given threat collision risk levels. For demonstrating developed concepts, numerical methods are developed using nonlinear filtering methodology for updating hypothesis sets and corresponding conditional probabilities. Space platform associated sensor resources are managed using previously developed and demonstrated information-theoretic objective functions and optimization methods. Consequently, estimation and numerical methods are evaluated and demonstrated on a realistic Low Earth Orbit collision encounter.

Constrained orbital intercept-evasion

An effective characterization of intercept-evasion confrontations in various space environments and a derivation of corresponding solutions considering a variety of real-world constraints are daunting theoretical and practical challenges. Current and future space-based platforms have to simultaneously operate as components of satellite formations and/or systems and at the same time, have a capability to evade potential collisions with other maneuver constrained space objects. In this article, we formulate and numerically approximate solutions of a Low Earth Orbit (LEO) intercept-maneuver problem in terms of game-theoretic capture-evasion guaranteed strategies. The space intercept-evasion approach is based on Liapunov methodology that has been successfully implemented in a number of air and ground based multi-player multi-goal game/control applications. The corresponding numerical algorithms are derived using computationally efficient and orbital propagator independent methods that are previously developed for Space Situational Awareness (SSA). This game theoretical but at the same time robust and practical approach is demonstrated on a realistic LEO scenario using existing Two Line Element (TLE) sets and Simplified General Perturbation-4 (SGP-4) propagator.

Sensor management of space-based multiplatform EO/IR sensors for tracking geosynchronous satellites

We further develop our previous work on sensor management of disparate and dispersed sensors for tracking geosynchronous satellites presented last year at this conference by extending the approach to a network of Space Based Visible (SBV) type sensors on board LEO platforms. We demonstrate novel multisensor-multiobject algorithms which account for complex space conditions such as the phase angles and Earth occlusions. Phase angles are determined by the relative orientation of the sun, the SBV sensor, and the object, and play an important factor in determining the probability of detection for the objects. To optimally and simultaneously track multiple geosynchronous satellites, our tracking algorithms are based on the Probability Hypothesis Density (PHD) approximation of multiobject densities, its regularized particle filter implementations (regularized PHD-PF), and a sensor management objective function, the Posterior Expected Number of Objects.

Integrate knowledge acquisition with target recognition through closed-loop ATR

Automatic Target Recognition (ATR) algorithm performance is highly dependent on the sensing conditions under which the input data is collected. Open-loop fly-bys often produce poor results due to less than ideal measurement conditions. In addition, ATR algorithms must be extremely complicated to handle the diverse range of inputs with a resulting reduction in overall performance and increase in complexity. Our approach, closed-loop ATR (CL-ATR), focuses on improving the quality of information input to the ATR algorithms by optimizing motion, sensor settings and team (vehicle-vehicle-human) collaboration to dramatically improve classification accuracy. By managing the data collection guided by predicted ATR performance gain, we increase the information content of the data and thus dramatically improve ATR performance with existing ATR algorithms. CL-ATR has two major functions; first, an ATR utility function, which represents the performance sensitivity of ATR produced classification labels as a function of parameters that correlate to vehicle/sensor states. This utility function is developed off-line and is often available from the original ATR study as a confusion matrix, or it can be derived through simulation without direct access to the inner working of the ATR algorithm. The utility function is inserted into our CLATR framework to autonomously control the vehicle/sensor. Second, an on-board planner maps the utility function into vehicle position and sensor collection plans. Because we only require the utility function on-board, we can activate any ATR algorithm onto a unmanned aerial vehicle (UAV) platform no matter how complex. This pairing of ATR performance profiles with vehicle/sensor controls creates a unique and powerful active perception behavior.

Fusion of imaging data and auxiliary signal for target classification

Fusion of imaging data with auxiliary signal such as EW data for multitarget classification poses daunting theoretical and practical challenges. The problem is exacerbated by issues such as asynchronous data flow, uneven feature quality and object occlusion. In our approach, we assign prior probabilities to image and signal feature elements to handle those practical issues in a unified manner. Current state and class probability distributions estimated from previous instances are fused with new outputs from individual classifiers immediate after the outputs become available to establish updated state and class probability distributions in a Bayesian framework. Results are presented that demonstrate joint segmentation and tracking, target classification using imaging data, and fusion of imaging data with noisy and asynchronous auxiliary EW information under realistic simulation scenarios.