Parameter Estimation for p-Order Random Coefficient Autoregressive (RCA) Models Based on Kalman Filter

Mohammed Benmoumen; Jelloul Allal; Imane Salhi

doi:10.60692/ngy4g-aga97

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Parameter Estimation for p-Order Random Coefficient Autoregressive (RCA) Models Based on Kalman Filter

Mohammed Benmoumen Jelloul Allal Imane Salhi

Year: 2019 Journal: Greater South Information System

DOI: 10.60692/ngy4g-aga97

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Abstract

In this paper we elaborate an algorithm to estimate p-order Random Coefficient Autoregressive Model (RCA(p)) parameters. This algorithm combines quasi-maximum likelihood method, the Kalman filter, and the simulated annealing method. In the aim to generalize the results found for RCA(1), we have integrated a subalgorithm which calculate the theoretical autocorrelation. Simulation results demonstrate that the algorithm is viable and promising.

Keywords:

Kalman filter Autoregressive model Estimation theory STAR model Extended Kalman filter Invariant extended Kalman filter Fast Kalman filter Simulated annealing

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Control Systems and Identification

Physical Sciences → Engineering → Control and Systems Engineering

Target Tracking and Data Fusion in Sensor Networks

Physical Sciences → Computer Science → Artificial Intelligence

Direction-of-Arrival Estimation Techniques

Physical Sciences → Computer Science → Signal Processing

Parameter Estimation for p-Order Random Coefficient Autoregressive (RCA) Models Based on Kalman Filter

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