Adaptive Tracking System Research Articles

Sector Based Scanning and Adaptive Active Tracking of Multiple Objects

This paper presents an adaptive active tracking system with sector based scanning for a single PTZ camera. Dividing sectors on an image reduces the search space to shorten selection time so that the system can cover many targets. Upon the selection of a target, the system estimates the target trajectory to predict the zooming location with a finite amount of time for camera movement. Advanced estimation techniques using probabilistic reason suffer from the unknown object dynamics and the inaccurate estimation compromises the zooming level to prevent tracking failure. The proposed system uses the simple piecewise estimation with a few frames to cope with fast moving objects and/or slow camera movements. The target is tracked in multiple steps and the zooming time for each step is determined by maximizing the zooming level within the expected variation of object velocity and detection. The number of zooming steps is adaptively determined according to target speed. In addition, the iterative estimation of a zooming location with camera movement time compensates for the target prediction error due to the difference between speeds of a target and a camera. The effectiveness of the proposed method is validated by simulations and real time experiments.

Nonlinear adaptive tracking‐control synthesis for functionally uncertain systems

AbstractThe paper is concerned with the problem of adaptive tracking system control synthesis. It is assumed that a nonlinear, feedback linearizable object dynamics (model structure) is (partially) unknown and some of its nonlinear characteristics can be approximated by a sort of functional approximators. It has been proven that proportional state feedback plus parameter adaptation are able to assure its asymptotic stability. This form of controller permits online compensation of unknown model nonlinearities and exogenous disturbances, which results in satisfactory tracking performance. An interesting feature of the system is that the whole process control is performed without requisite asymptotic convergence of approximator parameters to the postulated ‘true’ values. It has been noticed that the parameters play rather a role of slack variables on which potential errors (that otherwise would affect the state variables) cumulate. The system's performance has been tested via Matlab/Simulink simulations via an example of ship path‐following problem. Copyright © 2009 John Wiley & Sons, Ltd.

Tumor tracking and motion compensation with an adaptive tumor tracking system (ATTS): System description and prototype testing

A novel system for real-time tumor tracking and motion compensation with a robotic HexaPOD treatment couch is described. The approach is based on continuous tracking of the tumor motion in portal images without implanted fiducial markers, using the therapeutic megavoltage beam, and tracking of abdominal breathing motion with optical markers. Based on the two independently acquired data sets the table movements for motion compensation are calculated. The principle of operation of the entire prototype system is detailed first. In the second part the performance of the HexaPOD couch was investigated with a robotic four-dimensional-phantom capable of simulating real patient tumor trajectories in three-dimensional space. The performance and limitations of the HexaPOD table and the control system were characterized in terms of its dynamic behavior. The maximum speed and acceleration of the HexaPOD were 8 mm/s and 34.5 mm/s2 in the lateral direction, and 9.5 mm/s and 29.5 mm/s2 in longitudinal and anterior-posterior direction, respectively. Base line drifts of the mean tumor position of realistic lung tumor trajectories could be fully compensated. For continuous tumor tracking and motion compensation a reduction of tumor motion up to 68% of the original amplitude was achieved. In conclusion, this study demonstrated that it is technically feasible to compensate breathing induced tumor motion in the lung with the adaptive tumor tracking system.

SU-FF-J-07: Analysis of Response Time Delays in An Adaptive Feedback System: Considerations for a Real-Time Intra-Fraction Motion Tracking Couch

Purpose: To assess the impact of response time delays in an adaptive feedback couch-based respiration-induced motion tracking system. Method and Materials: We employed a convolution/superposition based Monte Carlo dose calculation to determine the impact of response delays between the tumor or surrogate respiration markers and the couch on planned dose distributions. The isocenter was randomly sampled from the distribution of the residual tumor/marker/couch trajectory. With this calculation we obtained the ensemble average over all possibilities of the dose distribution (assuming each fraction starts with a random respiration phase). Two test motion trajectories were used: 1) sinusoidal and 2) a published diaphragm motion description. For motion periods of 3 and 4 s and a motion amplitude of 1 cm, we simulated response delays ranging from 100–500 ms. Calculations were validated with measurement for one case. Results: For a motion period of 3 s, the penumbra width increased by 1–11.5 mm when superior-inferior motion with an amplitude of 1 cm was considered for response delays ranging from 100–500 ms with slightly worse results observed for the published motion trajectory. The decrease in the width of the 90% isodose line was 1.2–16.7 mm. The degradation in the penumbra and 90% isodose line width in the direction of motion were less severe when the motion period was 4 s. Phase delay film measurements agreed with our calculations to < 2 mm agreement of the corresponding isodose lines. Conclusion: For both sinusoidal and a published diaphragm motion trajectory, a response time of < 300 ms within the feedback loop is desirable to deliver highly conformal plans reliably. Response times of 500 ms or higher are equivalent to applying no real-time motion compensation and in some cases can degrade the plan much worse than no corrective action.

Collaborative and Reconfigurable Object Tracking

Many Applications perceive visual information through networks of embedded sensors. Intensive image processing computations have to be performed in order to process the perceived information. Such computations usually demand hardware implementations in order to exhibit real time performance. Furthermore, many of such applications are hard to be characterized a priori, since they take different paths according to events happening in the scene at runtime. Hence, reconfigurable hardware devices are the only viable platform for implementing such applications, providing both real time performance and dynamic adaptability for the system.In this paper, we present a collaborative and dynamically adaptive object tracking system that has been built in our lab. We exploit reconfigurable hardware devices embedded in a number of networked cameras in order to achieve our goal. We justify the need for dynamic adaptation of the system through scenarios and applications. Experimental results on a set of scenes advocate the fact that our system works effectively for different scenario of events through reconfiguration. Comparing results with non-adaptive implementations verify the fact that our approach improves system's robustness to scene variations and outperforms the traditional implementations.

Cascade closed-loop optimization and control of batch reactors

In this paper, a cascade closed-loop optimization and control strategy for batch reactors is proposed. Based on the reduction of a physical conservation model a cascade system is developed, which can effectively combine optimization and control to achieve good on-line optimization and tracking performance under the common condition where incomplete knowledge of the reaction system exists. A two-tier estimation scheme using a nonlinear observer for heat production rate and reaction rates is also developed. In the reaction rate estimation, calorimetric information is used. The on-line closed-loop optimization strategy uses a descending horizon dynamic optimization algorithm based on nonlinear programming and an additive unknown disturbance for feedback. A simple adaptive nonlinear tracking system is designed based on the generic model control concept. The efficiency of this strategy is demonstrated through simulations on a batch reactor under various operation conditions, such as noisy measurements, varying initial states and model mismatch.

On the robust control of nonlinear systems

In this paper we consider robust stabilization of the class of nonlinear plants of the form $$\dot x = f(x) + \sum\limits_{i = 1}^m {g_i } (x)u_i (t),$$ which are equivalent, under smooth state space coordinate transformations and nonlinear state feedback, to controllable systems. This approach is very sensitive for unknown parameters' values. Parameter adaptation may be used as a technique to robustify minimum-phase systems [3]. We give an example of a locally stable adaptive tracking system in which the last assumption is weakened. The minimum-phase plant considered in the paper is a current-controlled squirrel cage induction motor.

Adaptive tracking system for a manoeuvring target using images with correlated noises

An effective scheme is proposed for estimating the state parameters of a manoeuvring target from a noisy image sequence, providing the sequence contains correlated noises and the trajectory of the target is disturbed by an unknown acceleration. For the correlated noises, a first-order difference operator is applied to the original image sequence to generate an artificial measurement sequence with only uncorrelated white noises. For the unknown acceleration, a Kalman filter augmented by a semi-Markov process and the bayesian theory is applied to form an adaptive filter. In the proposed tracking system, the filter first generates an artificial measurement at each sampling time from the observation. It then utilizes an artificial measurement sequence up to the current time instance to predict the a posteriori probability of each possible acceleration state. The weighted average of acceleration, where the weight is the a posteriori probability, is applied to derive the optimal estimates of the state parameter...

An adaptive real-time intelligent seam tracking system

Abstract The concept of a real-time seam tracking system for welding automation and the initial design for building a prototype is presented. The Adaptive Real-time Intelligent Seam Tracker (ARTIST) uses a robot held, laser based vision system for automation of arc welding processes, and is currently under development at the Applied Research Laboratory of the Pennsylvania State University. The design of ARTIST builds upon the concept of a zero-pass technique. The 3D information of seam geometry is collected and processed for real-time guidance and control of the welding torch trailing behind the laser based vision sensor. This zero-pass concept eliminates the need for preprogramming of the weld path and thus, potentially enhances the welding cycle time for small batches. The ARTIST is designed to support multipass arc welding and to handle any tack welds which are encountered during the seam welding operation.

Adaptive underwater target tracking using passive multipath time-delay measurements

This paper examines the problem of adaptively tracking in range and velocity an underwater maneuvering target using passive time delay measurements. The target can make large scale random velocity and depth changes at times which are unknown to the observer. Tracking is accomplished by making use of the basic linearized polar model of target and observer motion previously developed [1]. Now, however, a nonlinear system block has been added to the tracking system [2], [3], which leads to two major benefits. First, the need for extended Kalman filters is eliminated making the passive tracking system more robust than it was previously [3]. The second benefit is a partial decoupling of depth estimation from the polar range estimator, which considerably reduces the computational level of the adaptive tracking system.

内部安定な適応トラッキング系の設計

内部安定な適応トラッキング系の設計

Design of a deadbeat adaptive tracking system

Using an orthogonal decomposition technique, a matrix pseudo – in version lemma is presented to derive recursive algorithms for computing the best approximate solution to the sequential estimation problem. A deadbeat convergence property of the algorithms is shown and is applied to the design of a deadbeat adaptive tracking system. Simulations illustrate perfect tracking of the system output to the periodic reference trajectory in finite steps.

The smoothed step‐track antenna controller

AbstractThe paper describes a new adaptive tracking system which, unlike simple step‐track sytems, does not use the stepping cycles to directly position the antenna. The system can cope with severe noise; about 15 dB worse than step track. The system is cheap and practical. Results are given for both large and small dish antennas.

An adaptive low-angle tracking system

A system for source tracking down to a fraction of a beamwidth of a surface with unknown multipath reflecting properties is described. The system has three major features: 1) the multipath returns are decorrelated by frequency diversity; 2) an efficient broad-band multiple beam antenna design is employed such that the data are characterized by a highly symmetrical (Toeplitz) correlation matrix; 3) Burg's reflection coefficient technique is employed to obtain rapid convergence to the maximum entropy spectrum from which the target location is determined. Realistic simulations including imperfect decorrelation, antenna errors, and transient results show that the system can provide accurate low-angle estimates using a practical antenna and reasonable data-processing time.