Modern tracking and fusion settings involve multiple platforms in different locations, tracking different target tracks,
focusing on different regions of interest, while using different update rates and sensor resolutions with the goal of
providing increased situation awareness in the region by fusing together the diversity of information from each platform.
In this paper, a decentralized, distributed fusion architecture is presented along with results and trade studies comparing
performance to that of a centralized fusion architecture. The decentralized distributed architecture is designed to work
with legacy tracking systems and uses an efficient message passing scheme to share information and coordinate tracks
across a diverse group of platforms. This system does not rely on a central node and allows for track information to be
maintained at the local level while utilizing track information from other platforms to increase situation awareness. We
compare the performance between our distributed approach and a centralized system using simulated airborne sensors
operating in overlapping regions of interest with target densities and routes chosen to demonstrate tradeoffs between the
different architectures. Preliminary results show that the decentralized distributed system provides similar performance
to the centralized fusion system in terms of situation awareness relative to traditional tracking metrics, but at the cost of
using an increased communication bandwidth to provide frequent updates to neighboring platforms. Results demonstrate
the tradeoff between flexibility and optimality - configuration of the distributed decentralized system to provide
increased flexibility and robustness comes at the cost of reduced situation awareness as compared to the centralized
The problem of automated scheduling a constellation of satellites to achieve maximum information content is a
challenging problem. This optimization problem is further complicated when one attempts to meet collection
requirements on various sites all while operating within a power budget. The goal of this research is to find the schedules
for a set of satellite sensors that observe a set of fixed ground locations while incorporating visibility, solar angles, time
of day, site priority, desired goal time between collects for each site, and satellite power. Our solution approach utilizes a
top-down approach that accounts for information over the entire scheduling window. The higher layers use relaxed
satellite information in ever decreasing time windows, while the lowest resolution scheduling layer uses a Lagrangian
relaxation based approach which incorporates the power constraint in the objective function. The final step of our
approach is to search locally for better solutions using a k-switch local search method to improve on the optimization
objective function. This paper will focus on the technical discussion of the hierarchical approach and generation of the
final solution via Lagrangian relaxation. The information content or benefit of the top-down, Lagrangian relaxation
technique will also be compared to other scheduling techniques to provide a measure of performance. We will provide
performance results for our approach using simulated data and sensors.
Current surveillance systems operate in a highly dynamic environment in which large numbers of sensors on board
multiple platforms must cooperate in order to achieve overall mission success. In an attempt to maximize sensor
performance, today's sensors employ rudimentary or, in some cases, inflexible sensor tasking schemes. These
approaches are highly tuned to a specific scenario and geometry and are inflexible to changes in the mission,
environmental conditions, heterogeneous sensors, and different system architectures. As the complexity of the problem
space increases and new sensors become available, it is critical to have a sensor management scheme that is capable of
incorporating new environmental knowledge, new sensors and different systems approaches with minimal computational
impact on the overall system. Each system should develop an autonomous sensor tasking capability which factors in
global concerns within the complete distributed network of platforms and sensors. Moreover, tasking efficiency can be
improved by a highly developed understanding of sensor performance at each point in time. This can be achieved by
incorporating the impact of problem geometry - sensor location, track object type and view angle - and weather
phenomena, such as clouds, aerosols, turbulence and sun glint.
This paper describes our approach for simultaneously optimizing sensor resource management, surveillance objectives,
and atmospheric transmission of signals while minimizing sensor and environmental noise. Our approach uses a genetic
algorithm to evolve a population of sensor tasking assignments through constantly-updating track locations, weather
conditions, and lighting conditions. Preliminary studies demonstrate encouraging improvements in sensor management
performance. We will present results from our preliminary studies and discuss a path forward for our technology.
The current military trend toward many diverse platforms and sensors available for use within a surveillance
environment requires the ability to efficiently and effectively task these sensors. This results in a requirement for
functionality within the surveillance problem for sensor resource management. This functionality requires the automatic
generation of appropriate tasks, the mapping of these tasks to a set of feasible sensors, the calculation of the benefit
achieved for executing the task, and the eventual optimal scheduling of these tasks.
As part of a recent research effort, we have developed a closed loop sensor resource management environment. As part
of this simulation testbed environment we have addressed two key problems. The first is the development of genetic
algorithm approach for solving the sensor scheduling problem. Our approach solves a sensor scheduling problem
involving multiple sensors as well as several constraints related to scheduling time windows, resource limitations, and
linked/repeating tasks. The second area of development is the automatic generation of the tasks to be scheduled. This
automated task generation includes the generation of tasks for different missions which in our problem include both
surveillance as well as high priority task requests. In each case, our task generation capability creates a sensor
independent score that is used in the scheduling algorithm.
This paper will describe the sensor management problem in general as well as give a description of our genetic algorithm
scheduling approach. We will also describe our approach for generating tasks for multiple missions and the generation of
the corresponding task benefit. We will conclude with a discussion of the results obtained during our effort and
directions for future research.
As the military continues to move forward with an increased number of sensor and fusion systems, it becomes necessary
for these systems to be able to communicate efficiently and effectively. In these environments there are multiple sensor
and fusion systems that in the past have operated independently of one another. As an increasing number of systems
become available, eventually an overlap in the coverage area occurs between these fusion systems. This results in a need
for coordination between these semi-autonomous fusion systems. Short of a complete redesign of all the fusion systems,
a solution is required to address the handoff of data between these systems.
The primary goal of this paper is to describe a data fusion handoff capability that is able to augment these existing
systems. This is accomplished by the use of a Handoff Manager that is added to each fusion system. The Handoff
Manager is responsible for developing a global representation the track information displayed onboard its own fusion
system that is common with the other members of the federation of fusion systems. This is accomplished by using a
global track numbering scheme that requires communication and adjudication between the multiple Handoff Manager
components that are present on the different fusion systems within the federation.
This paper will define the data fusion handoff problem and describe our approach for handling the data fusion handoff
problem within the context of overlapping and non-overlapping sensor environments. We will conclude with a
discussion of results for a sample problem and of the path forward.
Today's battlefield environment contains a large number of sensors, and sensor types, onboard multiple platforms. The
set of sensor types includes SAR, EO/IR, GMTI, AMTI, HSI, MSI, and video, and for each sensor type there may be
multiple sensing modalities to select from. In an attempt to maximize sensor performance, today's sensors employ either
static tasking approaches or require an operator to manually change sensor tasking operations. In a highly dynamic
environment this leads to a situation whereby the sensors become less effective as the sensing environments deviates
from the assumed conditions.
Through a Phase I SBIR effort we developed a system architecture and a common tasking approach for solving the
sensor tasking problem for a multiple sensor mix. As part of our sensor tasking effort we developed a genetic algorithm
based task scheduling approach and demonstrated the ability to automatically task and schedule sensors in an end-to-end
closed loop simulation. Our approach allows for multiple sensors as well as system and sensor constraints. This provides
a solid foundation for our future efforts including incorporation of other sensor types.
This paper will describe our approach for scheduling using genetic algorithms to solve the sensor tasking problem in the
presence of resource constraints and required task linkage. We will conclude with a discussion of results for a sample
problem and of the path forward.
Surveillance tracking is rapidly becoming an important application for GMTI radars. Surveillance tracking differs from precision tracking primarily in the scope of the problem being considered. Where precision tracking focuses primarily on the highly accurate location of a few numbers of targets, surveillance tracking is more interested in understanding the general location of large numbers of targets.
Several challenges arise as one attempts to directly apply techniques from precision tracking applications to the surveillance realm. In the surveillance problem using GMTI radars, the revisit rate is typically lower due to a larger area that must be considered. As a result, in all but the most benign environments, it is difficult to generate estimates of individual targets. This challenge is further compounded by poor sensor performance which results in large uncertainty in target positions and ambiguity from closely spaced targets. Given these issues, new techniques are required for addressing the surveillance tracking problem.
Our approach is to treat the surveillance problem in a slightly different fashion. Rather than attempting to track each of the individual targets in the surveillance region, we will focus on the bigger picture and track the groups of targets. This generalization will result in measurement not being individual radar returns off of a single target but rather a clustered grouping of detections representing a single group detection with both a location and group size. In this way, we are able to provide a true group tracking solution rather than attempting cluster the tracks of individual targets. In order to perform this task it is necessary to cluster detections of targets into group measurements, estimate the size of the group, and to provide an estimate of the location of the group. This paper will describe alternative approaches for clustering of detections and an examination of their performance in the overall group tracking approach. Additionally we will describe a technique for estimating the size of the group using knowledge of sensor performance characteristics and the number of detections that are clustered together. Finally we will describe a method for generating a reasonable estimate of the location of the group. We will conclude the paper with an example that examines the overall system performance on a representative problem.
A surveillance system needs to accurately locate and identify not only single targets, but also groups of targets engaged in a common activity. Most existing tracking systems are capable of tracking individual targets quite accurately; however, they fail to use information related to group behavior in order to improve these estimates. Furthermore, in wide area surveillance situations a military operator is required to sort through hundreds to thousands of individual targets in order to develop an understanding of the situation. Having the ability to collapse the behavior of individual targets into a common, coordinated motion can greatly enhance the productively and situational awareness of the operator. Our long-term approach to solving this problem is to develop an understanding of how to define a group and then to understand the inter-relationships between the various characteristics that describe a group. Then using this information, we will be able to partition the set of target into groups that can be aggregated over the entire military force hierarchy. This goal of this paper is to describe an approach that is based upon genetic algorithms for solving the military force hierarchy problem. This paper will describe the underlying genetic algorithm, scoring function, and some initial results.
Level 2 fusion is defined as situation awareness. Unfortunately, that is the point where the agreement on Level 2 fusion ends. The distinctions between the boundaries between Levels 1, 2, and 3 are not clearly defined. As a result, these disputes tend to cloud the discussion on the required functionality required of a Level 2 tracking system. Our approach to develop a system that solves a perceived Level 2 problem has three basic tenets: define the problem, develop the concept of the fusion architecture, and define the object state. These tenets provide the foundation to outline and explain the conceptual approach to a Level 2 problem. Each step from the problem fundamentals to the state definition used in the formulation of algorithmic approaches are presented. The discussion begins with a summary of the military problem, which can be considered situation assessment, of multiple levels of unit aggregation to determine force composition, current capabilities, and posture. The problem consists of fusion Level 1 information, incorporating doctrine and other knowledge base information to form a coherent scene of what exists in the field that can then be used as a component of intent analysis. The development of the problem model leads to the development of a Fusion architecture approach. The approach mirrors one of the standard approaches of Level 1 fusion: detection, prediction, association, hypothesis generation and management, and update. Unlike the Level 1 problem, these implementation steps will not become a rehash of the Kalman filter or similar approaches. Instead, the architecture permits a composite set of approaches including symbolic methodologies. The problem definition and the architecture lead to the development of the system state which represents the internal composition of the units and their aggregates. From this point, the discussion concludes with a short summary of potential algorithms proposed for implementation.
The ability to provide an accurate view of a region of interest is standard among existing tracking systems. The extension of this capability to include accurate projections of the targets to future times increases the complexity of the problem. However, this ability to predict future locations is an important problem due to the inherent time latency that is present from sensor to shooter. Because of this, a major requirement of the tracking system is that it must be able to use available information to accurately pr edict future target locations. The focus of this paper is to describe an improved state estimation technique that incorporates road information by using a Variable Structure IMM. Furthermore, this approach will identify and account for stopped or stationary moving targets. Finally, some preliminary performance results will be presented.
Proc. SPIE. 3720, Signal Processing, Sensor Fusion, and Target Recognition VIII
KEYWORDS: Detection and tracking algorithms, Sensors, Control systems, Distributed computing, Magnetic sensors, Acquisition tracking and pointing, Motion models, Filtering (signal processing), Fuzzy logic, Data fusion
The Deployable Autonomous Distributed System is an ocean surveillance system that contains a field of sensor nodes. Each sensor node provides target detections to a master node in the field for fusion by a Multiple Hypothesis Tracker Correlator (MHTC). The overall performance of a fusion engine depends upon the set of parameters that are used by the Multiple Hypothesis Tracker. Although a static set of parameters may work well over a wide range of scenarios, they may not lead to optimal performance in all cases. This paper addresses Level 4 fusion to improve performance of the data fusion system at the master node by using a fuzzy logic controller to adaptively tune the parameters. By using a set of linguistic rule based fuzzy logic algorithms, the tuning parameters of the MHTC are modified. A set of metrics are used to determine the added worth of the fuzzy logic controller.
The use of a multiframe moving for the solution of the data association problem in multisensor-multitarget tracking requires the repeated solution of a multidimensional assignment problem. This problem differs from its predecessor only by the addition of the new scan of measurements. In addition, the multidimensional assignment problem is an MP-hard problem which is large scale and sparse yet has 'real-time' solution requirements. The use of relaxation techniques to solve the multidimensional assignment problem has proven to be an effective scheme within the context of a multiframe moving window. This work demonstrates the improved efficiency that is obtained by the use of hot starts in conjunction with a relaxation method in the data association problem. The idea is to use solution information from the previous frame in conjunction with new information from the current problem to hot start the data association problem. Computational results for various tracking scenarios have shown that hot starts can significantly reduce the amount of time needed to solve the data association problem without affecting solution quality.
Large classes of data association problems in multiple hypothesis tracking applications, including sensorfusion, can be formulated as multidimensional assignment problems. Lagrangian relaxation methods have beenshown to solve these problems to the noise level in the problem in real-time, especially for dense scenarios andfor multiple scans of data from multiple sensors. This work presents a new class of algorithms that circumventthe difficulties of similar previous algorithms. The computational complexity of the new algorithms is shownvia some numerical examples to be linear in the number of arcs.