K
Krishna R. Pattipati
Researcher at University of Connecticut
Publications - 500
Citations - 11935
Krishna R. Pattipati is an academic researcher from University of Connecticut. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 55, co-authored 478 publications receiving 10856 citations. Previous affiliations of Krishna R. Pattipati include Toyota Motor Engineering & Manufacturing North America.
Papers
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A generalized S-D assignment algorithm for multisensor-multitarget state estimation
TL;DR: An efficient and recursive generalized S-D assignment algorithm (S/spl ges/3) employing a successive Lagrangian relaxation technique is presented, with application to the localization of an unknown number of emitters using multiple high frequency direction finder sensors.
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Ground target tracking with variable structure IMM estimator
TL;DR: The design of a Variable Structure Interacting Multiple Model (VS-IMM) estimator for tracking groups of ground targets on constrained paths using Moving Target Indicator reports obtained from an airborne sensor is presented, significantly improving performance and reducing computational load.
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A new relaxation algorithm and passive sensor data association
TL;DR: In this paper, the measurement-target association problem is formulated as one of maximizing the joint likelihood function of the measurement partition, which leads to a generalization of the 3D assignment (matching) problem, which is known to be NP hard.
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System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics
TL;DR: A BMS that estimates the critical characteristics of the battery (such as SOC, SOH, and RUL) using a data-driven approach is proposed and the proposed framework provides a systematic way for estimating relevant battery characteristics with a high-degree of accuracy.
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A practical approach to job-shop scheduling problems
TL;DR: The use of Lagrangian relaxation to schedule job shops, which include multiple machine types, generic precedence constraints, and simple routing considerations, is explored and compares favorably with knowledge-based scheduling.