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.
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.
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.
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.
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.
In this paper, we introduce a decentralized fusion and tracking based on a distributed multi-source multitarget
filtering and robust communication with the following features: (i) data reduction; (ii) a disruption tolerant dissemination
procedure that takes advantage of storage and mobility; and (iii) efficient data set reconciliation algorithms.
We developed and implemented complex high-fidelity marine application demonstration of this approach that encompasses
all relevant environmental parameters. In the simulated example, multi-source information is fused by
exploiting sensors from disparate Unmanned Underwater Vehicles (UUV) and Unmanned Surface Vehicle (USV)
multi-sensor platforms. Communications among the platforms are continuously establishing and breaking depending
on the time-changing geometry. We compare and evaluate the developed algorithms by assessing their performance
against different scenarios.
In this paper we present collision event modeling, detection, and tracking using a space-based Low Earth
Orbit (LEO) EO/IR constellation of platforms. The implemented testbed is based on our previous work on
dispersed and disparate sensor management for tracking Space Objects (SOs). The known SOs' LEO trajectory
parameters are tracked by using a first order state perturbation model, and the estimates are updated using Monte Carlo
sampling techniques. Using multi-hypothesis testing we estimate if the tracked RSO is on a collision trajectory with
a satellite. Trajectories that can lead to a collision are then constantly observed and tracked using observations from
EO/IR sensors located on LEO platforms. The developed algorithms are tested and evaluated on a simulated testbed.
Open problems and future work are discussed.
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.
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.
Dynamic sensor management of heterogeneous and distributed sensors presents a daunting theoretical and practical
challenge. We present a Situational Awareness Sensor Management (SA-SM) algorithm for the tracking of ground
targets moving on a road map. It is based on the previously developed information-theoretic Posterior Expected
Number of Targets of Interest (PENTI) objective function, and utilizes combined measurements form an airborne
GMTI radar, and a space-based EO/IR sensor. The resulting filtering methods and techniques are tested and evaluated.
Different scan rates for the GMTI radar and the EO/IR sensor are evaluated and compared.
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.
Optimal sensor management of dispersed and disparate sensors for tracking Low Earth Orbit (LEO) objects
presents a daunting theoretical and practical challenge since it requires the optimal utilization of different types of
sensors and platforms that include Ground Based Radars (GBRs) positioned throughout the globe, and the Space
Based Visible (SBV) sensor on board LEO platforms. We derive and demonstrate new computationally efficient algorithms
for multisensor-multiobject tracking of LEO objects. The algorithms are based on the Posterior Expected
Number of Objects as the sensor management objective function, observation models for the sensors/platforms, and
the Probability Hypothesis Density Particle Filter (PHD-PF) tracker.
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
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
Joint search and sensor management for space situational awareness presents daunting scientific and practical
challenges as it requires a simultaneous search for new, and the catalog update of the current space objects. We
demonstrate a new approach to joint search and sensor management by utilizing the Posterior Expected Number of
Targets (PENT) as the objective function, an observation model for a space-based EO/IR sensor, and a Probability
Hypothesis Density Particle Filter (PHD-PF) tracker. Simulation and results using actual Geosynchronous Satellites
Dynamic sensor management of dispersed and disparate sensors for space situational awareness presents daunting
scientific and practical challenges as it requires optimal and accurate maintenance of all Resident Space Objects
(RSOs) of interest. We demonstrate an approach to the space-based sensor management problem by extending a
previously developed and tested sensor management objective function, the Posterior Expected Number of Targets
(PENT), to disparate and dispersed sensors. This PENT extension together with observation models for various sensor
platforms, and a Probability Hypothesis Density Particle Filter (PHD-PF) tracker provide a powerful tool for tackling
this challenging problem. We demonstrate the approach using simulations for tracking RSOs by a Space Based Visible
(SBV) sensor and ground based radars.
Sensor management for space situational awareness presents a daunting theoretical and practical challenge as
it requires the use of multiple types of sensors on a variety of platforms to ensure that the space environment is
continuously monitored. We demonstrate a new approach utilizing the Posterior Expected Number of Targets (PENT)
as the sensor management objective function, an observation model for a space-based EO/IR sensor platform, and a
Probability Hypothesis Density Particle Filter (PHD-PF) tracker. Simulation and results using actual Geostationary
Satellites are presented. We also demonstrate enhanced performance by applying the ProgressiveWeighting Correction
(PWC) method for regularization in the implementation of the PHD-PF tracker.
A theoretical formulation for mission based sensor management and
information fusion using advanced tools of probability theory and stochastic
processes is presented.
We apply Bayes' Belief Network methods to fuse features and determine
a tactical significant function which is used by the sensor management objective
function. The estimated multi-sensor multi-target posterior that results
reflects tactical significant, and is used to determine the course of action
for the given mission. We demonstrate the performance of the algorithm using the simple mission of
reaching a pre-specified location while avoiding threatening targets, and
discuss the results.
In last year's conference we demonstrated new results using a foundational, joint control-theoretic approach to situation assessment (SA) and SA sensor management that is based on a "dynamic situational significance map", the maximization of the expected number of targets of tactical interest, and approximate multitarget filters (specifically, first-order multitarget moment filters and multi-hypothesis correlator (MHC) engines). This year we report on
the following new developments and extensions: (1) a tactical significance function based on the fusion of different ambiguous attributes from several different sources; (2) a Bayes' belief network formulation for multi-target tracking and information fusion; and (3) a recursive closed form expression for the posterior expected number of targets of interests (PENTIs) for ANY number of sources. Results of testing this sensor management algorithm with
significance maps defined in terms of targets/attributes interrelationships using simplified battlefield situations demonstrate that these new advancements allow for a better SA, and a more efficient SA sensor management.
Sensor management in support of Level 1 data fusion (multisensor integration), or Level 2 data fusion (situation assessment) requires a computationally tractable multitarget filter. The theoretically optimal approach to this multi-target filtering is a suitable generalization of the recursive Bayes nonlinear filter. However, this optimal filter is intractable and computationally challenging that it must usually be approximated. We report on the approximation of a multi-target non-linear filtering for Sensor Management that is based on the particle filter implementation of Stein-Winter probability hypothesis densities (PHDs). Our main focus is on the operational utility of the implementation, and its computational efficiency and robustness for sensor management applications. We present a multitarget Particle Filter (PF) implementation of the PHD that include clustering, regularization, and computational efficiency. We present some open problems, and suggest future developments. Sensor management demonstrations using a simulated multi-target scenario are presented.
Sensor management in support of situation assessment (SA) presents a daunting theoretical and practical challenge. We demonstrate new results using a foundational, joint control-theoretic approach to SA and SA sensor management that is based on three concepts: (1) a "dynamic situational significance map" that mathematically specifies the meaning of tactical significance for a given theater of interest at a given moment; (2) an intuitively meaningful and potentially computationally tractable objective function for SA, namely maximization of the expected number of targets of tactical interest; and (3) integration of these two concepts with approximate multitarget filters (specifically, first-order multitarget moment filters and multi-hypothesis correlator (MHC) engines). Under this approach, sensors will be directed to preferentially collect observations from targets of actual or potential tactical significance, according to an adaptively modified definition of tactical significance.
Result of testing this sensor management algorithm with significance maps defined in terms of target's location, speed, and heading will be presented. Testing is performed against simulated data, and different sensor management algorithms including the proposed are compared.
The ambiguousness of human information sources and of a PRIORI human context would seem to automatically preclude the feasibility of a Bayesian approach to information fusion. We show that this is not necessarily the case, and that one can model the ambiguities associated with defining a "state" or "states of interest" of an entity. We show likewise that we can model information such as natural-language statements, and hedge against the uncertainties associated with the modeling process. Likewise a likelihood can be created that hedges against the inherent uncertainties in information generation and collection including the uncertainties created by the passage of time between information collections. As with the processing of conventional sensor information, we use the Bayes filter to produce posterior distributions from which we could extract estimates not only of the states, but also estimates of the reliability of those state-estimates. Results of testing this novel Bayes-filter information-fusion approach against simulated data are presented.
The particle filter is an effective technique for target tracking in the presence of nonlinear system model, nonlinear measurement model or non-Gaussian noise in the system and/or measurement processes. In this paper, we compare three particle filtering algorithms on a spawning ballistic target tracking scenario. One of the algorithms, the tagged particle filter (TPF), was recently developed by us. It uses separate sets of particles for separate tracks. However, data association to different tracks is interdependent. The other two algorithms implemented in this paper are the probability hypothesis density (PHD) algorithm and the joint multitarget probability density (JMPD). The PHD filter propagates the first order statistical moment of multitarget density using particles. While, the JMPD stacks the states of a number of targets to form a single particle that is representative of the whole system. Simulation results are presented to compare the performances of these algorithms.
Monopulse radar tracking of target elevation for objects flying close to a reflecting surface is difficult due to interference between the direct echo and surface-reflected target echoes. Ideally, target height could be estimated directly from the probability density for monopulse measurements given target range and height. This direct approach is usually unfeasible because the density generally has many false peaks so there are multiple solutions for target height. This paper describes a nonlinear filter that exploits this behavior to estimate target height. The filter recursively computes the probability density for height and vertical velocity conditioned on the monopulse measurement sequence. The time evolution of this density between measurements is determined by a Fokker-Planck partial differential equation. This is solved in real-time using a finite difference scheme. The monopulse measurement probability density is computed from a physical model and used to update the conditional target state density using Bayes' rule. In simulation testing for a generic C-band shipboard radar the filter is able to reliably acquire and track transonic targets through mild maneuvers with about 12 m root-mean-square height accuracy.