Che Chong

On the stochastic control of linear systems with different information sets

IEEE Transactions on Automatic Control, 1971

J. Duncan Glover (S'68-M'71) was born in Melrose, Mass., on November 4, 1944. He re-cei... more

Continuous-time interacting multiple model extrapolation

This paper describes a continuous-time, interacting-multiple-model (IMM) extrapolation algorithm.... more This paper describes a continuous-time, interacting-multiple-model (IMM) extrapolation algorithm. A system state is modeled as a continuous-time, affine-Gaussian stochastic dynamical process driven by a white process noise as well as structural changes modeled by a finite-state, continuous-time Markov process. The system generally assumes multiple models with different system state dimensions and an affine-Gaussian state jump whenever a model transition occurs.

AMSTE precision fire control tracking overview

2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484), 2000

Abstract The DARPA Affordable Moving Surface Target Engagement (AMSTE) program is a research effo... more

Ground target tracking-a historical perspective

2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484), 2000

Abstract - Airborne sensor platforms such as Joint STARS provide a capability for ground surveill... more

Annals of Economic and Social Measurement, Volume 4, number 2

MULTISTAGE PRICiNG UNDER UNCERTAIN DEMAND BY CHEE-YEE CHONG AWl) DAVID C. CHENCI The optimal pric... more MULTISTAGE PRICiNG UNDER UNCERTAIN DEMAND BY CHEE-YEE CHONG AWl) DAVID C. CHENCI The optimal pricing policy of a monopolistic firm facing random demand and maximizing its e '.pecteI profit over a period of several stages is considered. The demand function is assumed to he time invarg(sn but unknown. A special case when the cost is certain and the demand is a linear function of the price is investigated. This is formulated as a stochastic control problem. is is found that when both the intercept and slope of the demand function are unknown, the Optimal pricing policy does not correspond to optimal prices for each individual stage. Approximate methods are used to find the optimal policies Simulation results are gtrcn.

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Track association and track fusion with nondeterministic target dynamics

IEEE Transactions on Aerospace and Electronic Systems, 2000

[1] Chen, V. (1994) Radar ambiguity function, time-varying matched filter, and optimal wavelet co... more

Generation of a Fundamental Data Set for Hard/Soft Information Fusion

2008 11th International …, 2008

We report here on our effort to investigate the types of hard/soft information that can be realis... more We report here on our effort to investigate the types of hard/soft information that can be realistically collected in an urban operational environment and to generate a data set that can be used for the development of hard/soft data fusion algorithms. Specifically, we discuss: 1) sources of "hard information" (i.e. information from physics-based sources) and "soft information" (i.e. information from human-based sources), 2) conceptual examples of hard/soft fusion, 3) data collection requirements and strategy, and 4) conceptual requirements for future algorithms with reference points in ongoing work. Specific techniques or frameworks for automated hard/soft fusion are beyond the scope of the effort being reported here. The presentation focuses on describing the context and requirements for hard/soft fusion at Joint Directors of Laboratories (JDL) Levels 1 and 2, and providing information about initial steps to generate a first-of-itskind hard/soft data set that will serve as a foundation and a verification/validation resource for future research.

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High Level Information Fusion developments, issues, and grand challenges: Fusion 2010 panel discussion

by Che Chong and Mitch Kokar

The goal of the High-Level Information Fusion (HLIF) Panel Discussion is to present contemporary ... more The goal of the High-Level Information Fusion (HLIF) Panel Discussion is to present contemporary HLIF advances and developments to determine unsolved grand challenges and issues. The discussion will address the issues between low-level (signal processing and object state estimation and characterization) and high-level information fusion (control, situational understanding, and relationships to the environment). Specific areas of interest include modeling (situations, environments), representations (semantic, knowledge, and complex), systems design (scenario-based, user-based, distributedagent) and evaluation (measures of effectiveness and empirical case studies). The goal is to address the contemporary operational and strategic issues in information fusion system design.

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On the stochastic control of linear systems with different information sets

by Che Chong and Michael Athans

IEEE Transactions on Automatic Control, 1971

J. Duncan Glover (S'68-M'71) was born in Melrose, Mass., on November 4, 1944. He re-cei... more

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