There has been increased impetus to leverage Small Unmanned Aerial Systems (SUAS) for collaborative sensing applications in which many platforms work together to provide critical situation awareness in dynamic environments. Such applications require critical sensor observations to be made at the right place and time to facilitate the detection, tracking, and classification of ground-based objects. This further requires rapid response to real-world events and the balancing of multiple, competing mission objectives. In this context, human operators become overwhelmed with management of many platforms. Further, current automated planning paradigms tend to be centralized and don’t scale up well to many collaborating platforms. We introduce a decentralized approach based upon information-theory and distributed fusion which enable us to scale up to large numbers of collaborating Small Unmanned Aerial Systems (SUAS) platforms. This is exercised against a military application involving the autonomous detection, tracking, and classification of critical mobile targets. We further show that, based upon monte-carlo simulation results, our decentralized approach out-performs more static management strategies employed by human operators and achieves similar results to a centralized approach while being scalable and robust to degradation of communication. Finally, we describe the limitations of our approach and future directions for our research.
A dynamic path-planning algorithm is proposed for UAV tracking. Based on tangent lines between two dynamic UAV
turning and objective circles, analytical optimal path is derived with UAV operational constraints given a target
position and the current UAV dynamic state. In this paper, we first illustrate that path planning for UAV tracking a
ground target can be formulated as an optimal control problem consisting of a system dynamic, a set of boundary
conditions, control constraints and a cost criterion. Then we derive close form solution to initiate dynamic tangent lines
between UAV turning limit circle and an objective circle, which is a desired orbit pattern over a target. Basic tracking
strategies are illustrated to find the optimal path for UAV tracking. Particle filter method is applied as a target is
moving on a defined road network. Obstacle avoidance strategies are also addressed. With the help of computer
simulations, we showed that the algorithm provides an efficient and effective tracking performance in various
scenarios, including a target moving according to waypoints (time-based and/or speed-based) or a random kinematics
Tracking an entity for a long duration allows the gathering of intelligence on a target. While the system comprises a collection of different elements (e.g., tracking, sensor tasking, etc.), the ability to track objects continuously over long periods rests on feature measurements that are collected "on-the-fly" and used to uniquely characterize the target of interest. These features are then used to track the target over extended periods of time and through situations in which the targets can be confused with other moving objects. The collecting of features helps support tracking the target when it becomes kinematically ambiguous with other objects. If the system is unable to avoid ambiguities between the target of interest and other moving objects, features collected post-ambiguity can be used to resolve the ambiguities. A collection of algorithms that model and attempt to resolve any association ambiguity between a target of interest and the tracks in the fusion and tracking database is required to accomplish this task. This module is referred to as the Tracked Object Manager (TOM) and forms the backbone of a system for the continuous tracking of high-value targets. The TOM utilizes the collected features to help correct track switches and, if appropriate, stitch tracks together to maintain continuous track on high-value targets. The algorithms are being incorporated into and evaluated using Toyon's Intelligence, Surveillance and Reconnaissance (ISR) simulation environment named SLAMEM.
Ground vehicles can be effectively tracked using a moving target indicator (MTI) radar. However, vehicles whose velocity along the line-of-sight to the radar falls below the minimum detectable velocity (MDV) are not detected. One way targets avoid detection, therefore, is to execute a series of move-stop-move motion cycles. While a target can be acquired after beginning to move again, it may not be recognized as a target previously in track. Particularly for the case of high-value targets, it is imperative that a vehicle be continuously tracked. We present an algorithm for determining the probability that a target has stopped and an estimate of its stopped state (which could be passed to a tasker to schedule a spot synthetic aperature radar (SAR) measurement. We treat a non-detection event as evidence that can be used to update the target state probability density function (PDF). Updating the target state PDF using a non-detection event pushes the probability mass into regions of the state space in which the vehicle is either stopped or traveling at a speed such that the range-rate fails the MDV. The target state PDF updated with the non-detection events is then used to derive an estimate of the stopped target’s location. Updating the target state PDF using a non-detection event is, in general, non-trivial and approximations are required to evaluate the updated PDF. When implemented with a particle filter, however, the updating formula is simple to evaluate and still captures the subtleties of the problem.
We have developed and implemented an approach to performing feature-aided tracking (FAT) of ground vehicles using ground moving target indicator (GMTI) radar measurements. The feature information comes in the form of high-range resolution (HRR) profiles when the GMTI radar is operating in the HRR mode. We use a Bayesian approach where we compute a feature association likelihood that is combined with a kinematic association likelihood. The kinematic association likelihood is found using an IMM filter that has onroad, offroad, and stopped motion models. The feature association likelihood is computed by comparing new measurements to a database of measurements that are collected and stored on each object in track. The database consists of features that have been collected prior to the initiation of the track as well as new measurements that were used to update the track. We have implemented and tested our algorithm using the SLAMEM simulation.
Proc. SPIE. 5430, Acquisition, Tracking, and Pointing XVIII
KEYWORDS: Weapons, Detection and tracking algorithms, Sensors, Monte Carlo methods, Particle filters, Missiles, Electronic filtering, Performance modeling, Filtering (signal processing), Global Positioning System
This paper considers the problem of tracking and intercepting a potentially moving ground-based RF source with an autonomous guided munition that has a passive bearings-only sensor located on its nose. It is assumed that the munition has lost GPS signal lock and that it relies only on its noisy inertial measurement unit (IMU) for guidance and navigation. Bearings-only target motion analysis (TMA) algorithms are used to obtain a position and velocity estimate for the RF source using the position, velocity and attitude estimates of the munition as well as the azimuth and elevation measurements obtained from the bearings-only RF sensor. Six degree-of-freedom (6-DOF) and three degree-of-freedom (3-DOF) munition models are used to evaluate the tracking and intercept/seeker performance of a hybrid coordinate (HC) extended Kalman filter (EKF), a particle filter (PF), a multiple hypothesis (MH) HC-EKF, and a pseudo-linear least squares (PLLS) filter
Sequential Monte Carlo methods have attracted the attention of the tracking community as a solution to Bayesian estimation particularly for nonlinear problems. Several attributes of particle filters support their use in jointly tracking and identifying ground targets in a road-constrained network. First, since the target dynamics are simulated, propagating a target within a constrained state space is handled quite naturally since the particle filter is not restricted to propagating Gaussian PDFs. Furthermore, a particle filter can approximate a PDF which is a mixture of continuous random variables (the target kinematic state) and discrete random variables (the target ID) which is necessary for the joint tracking and identification problem. Given HRRGMTI measurements of a target, we propose to jointly estimate a target's kinematic state and identification by propagating the joint PDF of the target kinematic state (position and velocity) and target ID. In this way, we capitalize on the inherent coupling between the target's feature measurement (the HRR profile) and the target's kinematic state. In addition to the coupling between a target's feature measurement and the target's kinematic state, there exists a coupling between a target's dynamics and the target's ID which can also be exploited through particle filtering methods. We develop the particle filtering algorithm for tracking and identifying ground targets in a road-constrained environment and present simulation results for a two-class problem.
We propose a particle filtering algorithm for tracking multiple ground targets in a road-constrained environment through the use of GMTI radar measurements. Particle filters approximate the probability density function (PDF) of a target's state by a set of discrete points in the state space. The particle filter implements the step of propagating the target dynamics by simulating them. Thus, the dynamic model is not limited to that of a linear model with Gaussian noise, and the state space is not limited to linear vector spaces. Indeed, the road network is a subset (not even a vector space) of R2. Constraining the target to lie on the road leads to adhoc approaches for the standard Kalman filter. However, since the particle filter simulates the dynamics, it is able to simply sample points in the road network. Furthermore, while the target dynamics are modeled with a parasitic acceleration, a non-Gaussian discrete random variable noise process is used to simulate the target going through an intersection and choosing the next segment in the road
network on which to travel. The algorithm is implemented in the SLAMEM simulation (an extensive simulation which models roads, terrain, sensors and vehicles using GVS). Tracking results from the simulation are presented.
An algorithm is derived for signature-aided tracking which uses features (e.g. high-range resolution radar (HRRR) profiles), or functions of features, in addition to kinematic measurements to associate measurements to known tracks, clutter or new tracks. The approach taken here is to derive the probability of the measurement-to-track association hypotheses which incorporates the likelihood of features as well as the traditional approach of using the kinematic measurement likelihood. It is assumed that the probability density function (PDF) of the features (or some function of the features) is available from a library. The approach to probabilistically characterizing the PDF of the profiles relies on the availability of a class-specific library for each target type. The class-specific library of PDFs characterizes the profiles conditioned on the target class from which the profile originated and the aspect at which the profile was obtained. The algorithm is evaluated using the SLAMEM simulation.
A refined version of a nonlinear estimation algorithm for tracking extended targets using imaging array data is presented. The algorithm is applied to a situation in which there is no closed-form functional representation for the image of the target. Based on the reduced sufficient statistic method of R Kulhavy, the algorithm recursively propagates, in a Bayes-closed sense, a set of sufficient statistics which approximate the true posterior density of the target parameter vector. The approximation is based on minimizing the Kullback-Leibler distance between the true posterior density and the approximating density. In previous work this density was a Gaussian mixture, while here scale functions are used to approximate the posterior density from which an approximate minimum variance estimate can be calculated. As the tracking progresses the posterior density is estimated on an increasingly finer scale. In order to reduce the number of scale functions, however, a pruning process is necessary. In this way, the number of scale functions approximating the density increases in areas for which the true density is significant while scale functions which approximate the density over regions where it is insignificant are ignored. Results are presented for simulations carried out in which the algorithm is applied to tracking an aircraft based on a sequence of synthetic images.
Proc. SPIE. 2235, Signal and Data Processing of Small Targets 1994
KEYWORDS: Detection and tracking algorithms, Sensors, Computer simulations, Sensor networks, Monte Carlo methods, Electronic filtering, Algorithm development, Data communications, Filtering (signal processing), Data fusion
A fusion algorithm is presented for a multisensor tracking system, in which the local trackers are N-scan data association filters. Previously, a fusion algorithm was given for the case where the local trackers are JPDA filters. Here, a fusion algorithm is presented for the more general case of local N-scan data association filters, of which the JPDA is a special case (N equals 0). The fusion equations consist of a simultaneous updating of the global hypothesis probabilities, and conditional global target state estimates. Two communication schemes between the local trackers and global processor are considered. A unidirectional communication scheme is examined in which the local trackers send the updated hypothesis probabilities and conditional target state estimates to the global processor; the local nodes then continue to track without knowledge of the global estimates. A bidirectional communication scheme is examined in which the local trackers send the updated hypothesis probabilities and conditional target state estimates to the global processor.