Research Article
Effect of Noise Correlation Coefficient on Joint Recursive Least Squares Parameters and State Estimation of Linear Stochastic State-space System
Khalid Abd El Mageed Hag El Amin*
Issue:
Volume 7, Issue 1, June 2025
Pages:
1-13
Received:
22 May 2025
Accepted:
5 June 2025
Published:
30 August 2025
Abstract: This article addresses the joint estimation of parameters and states in linear stochastic systems with correlated process and measurement noises. We propose the Kalman Filtering with Correlated Noises based Recursive Generalized Extended Least Squares (KF-CN-RGELS) algorithm, which innovatively integrates a reformulated Kalman filter to handle noise cross-correlation via a gain matrix T, alongside recursive least squares for synchronous parameter-state updates. The algorithm’s key advantage lies in its ability to leverage noise correlation for improved accuracy: experimental results demonstrate that a higher correlation coefficient (ρw, v=0.8)reduces parameter estimation errors to 0.85% (vs. 1.81% for ρw, v=0) and enhances state estimation. The method’s robustness is validated under varying noise conditions, offering practical utility in systems like radar guidance and industrial control.
Abstract: This article addresses the joint estimation of parameters and states in linear stochastic systems with correlated process and measurement noises. We propose the Kalman Filtering with Correlated Noises based Recursive Generalized Extended Least Squares (KF-CN-RGELS) algorithm, which innovatively integrates a reformulated Kalman filter to handle nois...
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Research Article
On Sequentially Perfect and Sequentially Uniform One Factorizations of Product of Cycles
Jamuna Loganathan*,
Hemalatha Palanisamy
Issue:
Volume 7, Issue 1, June 2025
Pages:
14-20
Received:
12 August 2025
Accepted:
28 August 2025
Published:
25 September 2025
Abstract: Let G be an n-regular graph. A sequentially perfect one-factorization is a decomposition of G into one-factors, such that union of any two consecutive one-factors gives a hamiltonian cycle, and also if the union of two consecutive one-factors forms a two regular graph, then this property is termed as sequentially uniform. For any two graphs G and H their product [Cartesian, Tensor, Wreath] has the vertex set {(g, h); g belongs to V(G) and h belongs to V(H)}. The Cartesian Product G Cartesian Product H has the edge set such that two vertices (g1, h1) and (g2, h2) are adjacent if they differ in exactly one coordinate, and the corresponding vertices in that graph are adjacent; in the tensor product G Tensor Product H two vertices (g1, h1) and (g2, h2) are adjacent only if both g1 is adjacent to g2 in G and h1 is adjacent to h2 in H; in the wreath product G Wreath Product H the edge set is E(G Wreath Product H) = {(g1, h1) (g2, h2) : g1g2 belongs to E(G) or g1=g2 and h1h2 belongs to E(H)}. In this paper it is proved that for all even m > 2 and odd n ≥ 3, there exists sequentially perfect and sequentially uniform one-factorizations in Cm Cartesian Product Cn, Cm Tensor Product Cn and Cm Wreath Product Cn.
Abstract: Let G be an n-regular graph. A sequentially perfect one-factorization is a decomposition of G into one-factors, such that union of any two consecutive one-factors gives a hamiltonian cycle, and also if the union of two consecutive one-factors forms a two regular graph, then this property is termed as sequentially uniform. For any two graphs G and H...
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