- Volume 51 Issue 5
DOI QR Code
Mean Square Projection Error Gradient-based Variable Forgetting Factor FAPI Algorithm
평균 제곱 투영 오차의 기울기에 기반한 가변 망각 인자 FAPI 알고리즘
- Seo, YoungKwang (Department of Electrical and Computer Engineering, Pusan National University) ;
- Shin, Jong-Woo (Department of Electrical and Computer Engineering, Pusan National University) ;
- Seo, Won-Gi (NEXTWILL Co., Ltd.) ;
- Kim, Hyoung-Nam (Department of Electrical and Computer Engineering, Pusan National University)
- Received : 2013.12.30
- Accepted : 2014.04.30
- Published : 2014.05.25
This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).
Supported by : 한국산업기술진흥원
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