David D. Sworder

An LQ-solution to a control problem associated with a solar thermal central receiver

The linearized process dynamics of the steam boiler in a solar-powered central receiver change abruptly when clouds interfere with the suns rays. The steam temperature regulator used to maintain proper exit steam conditions must control a system with variable structure and discontinuous state trajectories. This paper investigates the quadratic-optimal control of such a system, and gives the design equations for the optimal regulator.

Image-enhanced tracking

Measurement of the angular orientation of the target or the target designation makes possible the design of high-accuracy tracking and prediction systems. Such measurements are best accomplished with an imaging sensor. A model is presented for the image-based observation link, along with the equations used to convert a sequence of image processor outputs into an estimate of target status. An example examines the interrelationship which exists between the rate of feature changes of a target, the frame rate of the imager, and the sensitivity of the sensor-image processor. It is shown that even at high frame rates, anomalous events may occur. >

Exact hybrid filters in discrete time

Using the reference probability method and the change of measure in discrete time, the state estimator problem is considered for linear systems observed in Gaussian noise when the coefficients are functions of a noisily observed, finite-state Markov chain. The methods are new, and finite-dimensional filters are obtained. However, the number of statistics increases in time. A numerical comparison of this filter with the interactive multiple model algorithm introduced by Blom and Bar-Shalom (1988) is given.

Maneuver estimation using measurements of orientation

An image-based algorithm which provides an estimate of the current radial acceleration of a target is studied. Since the data in a single image frame provide information only on the orientation of the target, a sequence of frames must be processed to detect maneuvers. The estimation problem is parameterized in terms of natural groupings of measurement errors, and the influence of these errors on estimator fidelity is studied. >

Renewal models for maneuvering targets

Most model-based tracking algorithms represent the temporal dynamics of a maneuver acceleration with a Markov process. The sample paths so generated may not be plausible. A renewal process model is shown to be more realistic, but a tracker based upon it is seen to be more complicated to implement. The response of the renewal-model tracker is compared with a simpler estimator based upon a Markov model both for the case in which the tracker utilizes an imager, and the case where it does not. It is shown in the latter case that performance improvement is not commensurate with algorithmic complexity. For an image-enhanced tracker, a sophisticated model of target dynamics promises significant performance improvement. >

Image fusion for tracking manoeuvring targets

It is known that improvements in target tracking can be achieved by using multiple sensors. Most commonly, the individual measurement sequences are merged using a variant of linear algorithms. The approach proposed here differs from the conventional one in that nonlinear methods of data fusion are proposed to account for the peculiarities of the different measurement categories. This technique, called complementary fusion, is illustrated with the problem of tracking an agile target. It is shown that complementary fusion not only leads to higher fidelity tracking, but it also permits the more efficient utilization of the primary sensor.

Utility of imaging sensors in tracking systems

Abstract To justify the introduction of new sensor hardware, the potential performance improvement must be sufficient to warrant the additional expense and potential design risks that a change in system architecture entails. This paper compares the response of an image-enhanced tracker with that obtained from a conventional sensor suite. Because the underlying motion model is nonlinear and the tracking environment non-Gaussian, performance is scenario dependent. For a problem of tracking an agile target in the plane, it is shown that a system containing an imager is superior to an orthodox tracker without one.

Optimal Control, Repair, and Inventory Strategies for a Linear Stochastic System

A class of linear systems is studied which is subject to random failures. In such systems the designer must specify strategies for both control and repair. The dynamic interaction of these two facets of the problem is explored, and it is shown that quick repair permits a reduction in gains in the feedback loop. An algorithm is presented for finding the optimal controller and the best policy of repair.

Sensor fusion in estimation algorithms

Abstract In a multimodal, system, the growth in the number of possible modal paths makes state estimation difficult. Practical algorithms bound complexity by merging estimates that are conditioned on different modal path fragments. Commonly, the weight given to these local estimates is inversely related to the normalized magnitude of the residuals generated by each local filter. This paper presents a novel dual-sensor estimator that uses a merging formula that is based upon a different function of the residuals. Its performance is contrasted with an estimator using a single sensor and with another dual-sensor algorithm that requires fewer on-line calculations.

Multiple model methods in path following II

Path following is difficult when the observation rate is low. Multiple model estimators incorporating multisensor fusion have proven useful in this application. This paper shows the advantage of a recently developed multiple model algorithm. Performance comparisons with some current algorithms are presented.