Stochastic dynamic programming based resource allocation for multi target tracking for electronically steered antenna radar /

Download

index.pdf

Date

2015

Author

Uzun, Çağlar

Metadata

Show full item record

Item Usage Stats

214
views

131
downloads

In this work, the concept of sensor management is introduced and stochastic dynamic programming based resource allocation approach is proposed to track multiple targets. The core of this approach is to use Lagrange relaxation for decreasing the state space dimension. By this approximation, the overall problem is separated into components instead of using joint Markov model to optimize large scale stochastic control problem. The aim of this study is to adaptively allocate radar resources in an optimal way in order to maintain track qualities for multi-target case. The radar is electronically steered antenna radar. Resource allocation is done only for tracking excluding the search beams. Adaptive target tracking is performed by Kalman filter. Problem is modeled as a set of controlled Markov chains each dedicated to one track. Time scale is divided into two levels that are called as micro management and macro management. During the thesis, we deal with macro management part that aims to construct a policy which is optimal for a given objective function under the resource constraints. Stochastic dynamic programming with constraints in the sense of [32] is the method used. In this thesis, five different scenarios are constructed and corresponding algorithms are confirmed by simulation results. The performances of the algorithms are also compared. Their performances are analyzed on the average number of update decision and average number of target drops in time horizon.

Subject Keywords

Radar, Sensor networks., Markov processes., Dynamic programming., Mathematical optimization.

URI

http://etd.lib.metu.edu.tr/upload/12618383/index.pdf
https://hdl.handle.net/11511/24371

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Stochastic Dynamic Programming Based Resource Allocation for Multi Target Tracking for Electronically Steered Antenna Radar Uzun, Caglar; Demirekler, Mübeccel (2015-05-19) In this work, the concept of sensor management is introduced and stochastic dynamic programming based resource allocation approach is proposed to track multi target. The core of this approach is to use Lagrange relaxation for decreasing the state space dimension. By this approximation, the overall problem is separated into components instead of using joint Markov model to optimize large scale stochastic control problem. The aim of this study is to adaptively allocate radar resources in an optimal way in ord...
Improved state estimation for jump Markov linear systems Orguner, Umut; Demirekler, Mübeccel; Department of Electrical and Electronics Engineering (2005) This thesis presents a comprehensive example framework on how current multiple model state estimation algorithms for jump Markov linear systems can be improved. The possible improvements are categorized as: -Design of multiple model state estimation algorithms using new criteria. -Improvements obtained using existing multiple model state estimation algorithms. In the first category, risk-sensitive estimation is proposed for jump Markov linear systems. Two types of cost functions namely, the instantaneous an...
Dynamic Programming with Ant Colony Optimization Metaheuristic for Optimization of Distributed Database Queries Dokeroglu, Tansel; Coşar, Ahmet (2011-09-28) In this paper, we introduce and evaluate a new query optimization algorithm based on Dynamic Programming (DP) and Ant Colony Optimization (ACO) metaheuristic for distributed database queries. DP algorithm is widely used for relational query optimization, however its memory, and time requirements are very large for the query optimization problem in a distributed database environment which is an NP-hard combinatorial problem. Our aim is to combine the power of DP with heuristic approaches so that we can have ...
Dissimilarity maximization method for real-time routing of parts in random flexible manufacturing systems Saygin, C; Kilic, SE (Springer Science and Business Media LLC, 2004-04-01) This paper presents a dissimilarity maximization method (DMM) for real-time routing selection and compares it via simulation with typical priority rules commonly used in scheduling and control of flexible manufacturing systems (FMSs). DMM aims to reduce the congestion in the system by selecting a routing for each part among its alternative routings such that the overall dissimilarity among the selected routings is maximized. In order to evaluate the performance of DMM, a random FMS, where the product mix is...
Stochastic modeling of biochemical systems with filtering and smoothing Haksever, Merve; Uğur, Ömür; Department of Scientific Computing (2019) Deterministic modeling approach is the traditional way of analyzing the dynamical behavior of a reaction network. However, this approach ignores the discrete and stochastic nature of biochemical processes. In this study, modeling approaches, stochastic simulation algorithms and their relationships to each other are investigated. Then, stochastic and deterministic modeling approaches are applied to biological systems, Lotka-Volterra prey-predator model, Michaelis-Menten enzyme kinetics and JACK-STAT signalin...

Citation Formats

Ç. Uzun, “Stochastic dynamic programming based resource allocation for multi target tracking for electronically steered antenna radar /,” M.S. - Master of Science, Middle East Technical University, 2015.