Ali T. Alouani

Use of a kinematic constraint in tracking constant speed, maneuvering targets

The use of a kinematic constraint as a pseudomeasurement in the tracking of constant speed, maneuvering targets is considered in this note. The kinematic constraint provides additional information about the target motion that can be processed to improve tracking performance. A new formulation of the constraint equation is presented, and the rationale for the new formulation is discussed. The filter using the kinematic constraint as a pseudomeasurement is shown to be unbi- ased and sufficient conditions for stochastic stability of the filter are given. Simulated tracking results are given to demonstrate the potential that the new formulation provides for improving tracking performance.

Theory of distributed estimation using multiple asynchronous sensors

This work solves a practical sensor to sensor track fusion problem when the sensors used are asynchronous, communication delays exist between sensor platforms and track fusion center, and tracks may arrive out-of-sequence. For the proposed linear fusion rule, the solution is shown to be optimal in the minimum mean square sense. The new fusion rule does not require the synchronization of tracks for the purpose of track fusion. The batch processing of incoming tracks provides an elegant solution for the out-of-sequence and latent tracks without special processing of such data. As an example, it is shown that the Bar-Shalom-Campo fusion rule is a special case of the new asynchronous track fusion algorithm.

On the optimality of two-stage state estimation in the presence of random bias

Sufficient conditions for the optimality of a two-stage state estimator in the presence of random bias are derived. Under an algebraic constraint on the correlation between the state and bias process noises, the optimal estimate of the system state can be obtained as a linear combination of the output of the first stage (a bias-free filter) and the second stage (a bias filter). Because the algebraic constraint is restrictive in practice, the results indirectly indicate that for most practical systems the proposed solution to the two-stage estimation problem will be suboptimal. >

A two-stage Kalman estimator for state estimation in the presence of random bias and for tracking maneuvering targets

The authors provide the optimal solution of a two-stage estimation problem in the presence of random bias. Under an algebraic constraint, the optimal estimate of the system state can be obtained as a linear combination of the output of the first stage (a bias-free filter) and the second stage (a bias filter). The results presented provide a basis for assessing the suboptimality of a two-stage estimator when used for a specific system. By treating the bias vector as a target acceleration, the two-state Kalman estimator can be used for tracking maneuvering targets.<<ETX>>

Sliding mode control synthesis using fuzzy logic

In variable structure control algorithms, sliding mode plays a crucial role in making the closed loop system insensitive to modeling uncertainties and external disturbances, and also in transforming the original system dynamics into prescribed reduced order dynamics restricted to a switching manifold on which desired system dynamic behavior is achieved. The control law used to realize the desired sliding mode dynamics is discontinuous on the switching manifold. However, due to imperfections in switching, such as time delays, the system trajectory chatters instead of sliding along the switching manifold. This chattering is undesirable because it may excite unmodeled high-frequency dynamics in the physical system. To overcome this drawback, in this paper, a new sliding mode control algorithm using concepts from fuzzy theory and fuzzy logic is developed. The eccentricity of the new sliding mode control algorithm resides in generalized form of switching manifold which allows for a smooth transition between the reaching mode and the sliding mode. To demonstrate its performance, the proposed control algorithm is applied to a one-degree of freedom robot arm.

A Low-Cost Stand-Alone Multichannel Data Acquisition, Monitoring, and Archival System With On-Chip Signal Preprocessing

The objective of this paper is to design a new generation of affordable sophisticated data acquisition and processing (DAQP) systems. Because of the proposed system hardware reconfigurability, it can be used to meet the need of many real-world applications while keeping the cost of a device low. The hardware implementation of the different processing functions of the device allows for high-speed processing without the need of expensive general-purpose processors, as is the case of computer-based or microcontroller-based data acquisitions (DAQs). The target technology of implementing the proposed design is the system on chip via field-programmable gate array (SoC-FPGA). A four-channel DAQP was designed, developed, and tested in the Embedded Systems Design Laboratory, Tennessee Technological University, Cookeville. Various modules of the conceptual design are implemented and verified. Performance evaluation and cost comparisons are provided. The comparison showed that the results of the proposed instrument are comparable to existing technologies at a fraction of the cost.

Distributed estimation: constraints on the choice of the local models

The following estimation problem is considered: a coordinator must reconstruct the (global) probability density of a nonlinear random process, conditioned on the noise-corrupted observation history. The coordinator can only access the (local) probability density produced by local processing of the observation history using a (local) model different from the process model. It is shown that if the local model satisfies an algebraic constraint, the coordinator can reconstruct the same conditional density of the state process as the one obtained if the observations were processed using the coordinator (process) model. It is assumed that the random process is a nonlinear stochastic differential equation driven by a Brownian motion, and the observation process is corrupted by additive Brownian motion, which is identically modeled by the coordinator and the local processor. >

On optimal asynchronous track fusion

In practice, multisensor systems use dissimilar sensors having different data rates. Such sensors may also have inherent delays as well as communication delays. In the existing approaches to track fusion, the common assumption used is that the sensors are synchronous and have no communication delays. This paper presents a fusion architecture that combines tracks provided by different sensors that have different sampling rate and communication delays. By providing two way communications between the sensor platforms and the track fusion center, it is shown that the proposed asynchronous track fusion architecture provides a solution that is optimal in the mean square sense. Simulations are presented to compare the performance of the proposed track fusion with its sequential processing counterpart.

On optimal synchronous and asynchronous track fusion

In practice, multisensor systems use dissimilar sensors that may have different data rates. Such sensors may also have inherent delays due to multitasking as well as communication delays between the sensor platform and a remote central processing site. Track fusion algorithms are presented that are valid for asynchronous sensors (the sensors have different data rates and different delays) as well as synchronous sensors (all of the sensors take measurements at the same time and the same rate with no delays). The asynchronous track fusion problem is formulated and solved first. Then the synchronous track fusion problem is obtained as a special case of the asynchronous one. Finally, using simulated target tracks, the performance of the asynchronous track fusion (ASTF) algorithm is analyzed and compared to an existing track fusion algorithm. Different sensor data rates and communication delays are used in the simulations. It is found that the ASTF algorithm outperforms its counterpart and is able to handle relatively large communication delays. The results presented set the foundation for deriving optimal track fusion algorithms when taking into account realistic constraints such as sensors with different data rates and different communication and/or processing delays. The results can also be used as a benchmark to evaluate existing suboptimal track fusion algorithms.

Estimation of sensor bias in multisensor systems

An adaptive Kalman-filter-based technique to estimate and remove the sensor biases in a multiradar system is presented. The measurement model is based on the Taylor series approximation of the exact model and it incorporates range bias, bearing bias, and elevation bias. The technique was implemented using target tracks. The simulation results are presented. It was found that this technique gives accurate estimates of the sensor biases when given a reasonable model of the bias dynamics.<<ETX>>