A benchmark problem for tracking maneuvering targets is presented. The benchmark problem involves beam pointing control of a phased array (i.e., agile beam) radar against highly maneuvering targets in the presence of false alarms (FAs) and electronic counter measurements (ECM). The testbed simulation described includes the effects of target amplitude fluctuations, beamshape, missed detections, FAs, finite resolution, target maneuvers, and track loss. Multiple waveforms are included in the benchmark so that the radar energy can be coordinated with the tracking algorithm. The ECM includes a standoff jammer (SOJ) broadcasting wideband noise and targets attempting range gate pull off (RGPO). The limits on the position and maneuverability of the targets are given along with descriptions of six target trajectories. The "best" tracking algorithm is the one that minimizes a weighted average of the radar energy and radar time, while satisfying a constraint of 4% on the maximum number of lost tracks, The radar model, the ECM techniques, the target scenarios, and performance criteria for the benchmark are presented.

Benchmark for radar allocation and tracking in ECM

Two model reference adaptive system (MRAS) estimators are developed for identifying the parameters of permanent magnet synchronous motors (PMSM) based on the Lyapunov stability theorem and the Popov stability criterion, respectively. The proposed estimators only need online measurement of currents, voltages, and rotor speed to effectively estimate stator resistance, inductance, and rotor flux-linkage simultaneously. The performance of the estimators is compared and verified through simulations and experiments, which show that the two estimators are simple, have good robustness against parameter variation, and are accurate in parameter tracking. However, the estimator based on the Popov stability criterion, which can overcome parameter variation in a practical system, is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator, in contrast to only one integral unit in the estimator based on the Lyapunov stability theorem. In addition, the estimator based on the Popov stability criterion does not need the expertise that is required in designing a Lyapunov function.

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Comparison of two novel MRAS based strategies for identifying parameters in permanent magnet synchronous motors

We address the issue of reduced order H/sub /spl infin// filters for linear time varying systems. We introduce a new class of reduced order H/sub /spl infin// filters distinguished from existing methods in two aspects. First, the design method results in filters with the structure of Luenberger observers. Next, the design procedure involves the solution of a nonlinear difference equation having special properties. Filter operation is demonstrated in simulated tracking of a manoeuvring target by a radar homing tactical missile.

Reduced order H/sub /spl infin// filters for discrete linear systems

The advent of technology has brought to the field of engineering many tools that were once considered impractical. For example, the increased processing speed of microprocessors now allows measurements from image sensors to be used for target tracking or target identification in real time—a task once thought unachievable. Of late the advances made in artificial intelligence (AI), specifically the artificial neural network, have sprung many different applications among which the implementation of AI controllers being the most popular. However, these advances have been slow in their implementation in the field of target tracking for several reasons. First, there seems to be a lack of sound tracking architectures that can exploit the use of artificial intelligent agents. Second, there is some difficulty in fusing the different forms of information that can be measured from the various available sensors such as the image sensor, millimeter wave radar, Doppler radar, etc. Third, the increased computational complexity due to the employment of the various sensors could limit the practical usefulness of such a tracking system. 
This dissertation presents a novel framework in which various dissimilar sensors can be used simultaneously to track a highly agile and non-cooperative target. The proposed framework not only allows the usage of multiple sensors to yield a robust and accurate tracker but also maintain a reasonable computational requirement. Unlike the methods proposed in the literature for the design of multi-sensor tracking systems, this dissertation presents an AI-based system that can accept, process, and fuse measurements from any number of sensors of dissimilar forms. The principal contributions of this dissertation are the following: (i) a novel architecture of a three-layer feedforward neural-network-based tracking system with the ability to fuse measurements from dissimilar sensors; (ii) a powerful optimization algorithm for training the neural network; (iii) a novel mathematical target motion model to simplify the training and implementation of the proposed tracking system.

Target tracking in a multisensor environment using neural networks

The authors perform a thorough investigation of the extended H-infinity and Kalman filters based on results from stiffness estimation experiments. The paper uses the time-varying tolerance method to tune the H-infinity filter. Exploring similarities in the associated Riccati equations, the authors also propose a new approach for tuning the Kalman filter. The experimental results show that the H-infinity outperforms the Kalman filter along a wide range of tuning parameter values.Copyright © 2005 by ASME

A Comparison of H-Infinity With Kalman Filtering in Parameter Identification

Develops, tests and evaluates H/sub /spl infin// algorithms for tracking maneuvering targets as an alternative to traditional approaches based on Kalman filter extensions and modifications. A crucial property of H/sub /spl infin// filters is an automatic increase of the filter bandwidth. The authors further explore this property versus appropriate artificial increases of the bandwidth of a Kalman filter and the H/sub /spl infin// filter itself. H/sub /spl infin// and Kalman filters are compared for RMSE performance as well as performance robustness to different maneuvering trajectories. Simulation results are presented showing the performance and robustness of the two filters for different maneuvering trajectories, data rates and bandwidth adjustments.

http://ieeexplore.ieee.org/iel4/5816/16245/00752382.pdf

M. Athans

Papers

Tracking maneuvering targets using H/sub /spl infin// filters

Tracking maneuvering targets using H/sub /spl infin// filters