Adaptive array beam forming using a combined RLS-LMS algorithm
A new adaptive algorithm, called RLMS, which combines the use of recursive least square (RLS) and least mean square (LMS), is proposed for array beam forming. The convergence of the RLMS algorithm is analyzed, in terms of mean square error, in the presence of additive white Gaussian noise. Computer...
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| Format: | Conference Paper |
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Institute of Electrical and Electronics Engineers (IEEE)
2008
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| Online Access: | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4773748 http://hdl.handle.net/20.500.11937/31618 |
| _version_ | 1848753431699783680 |
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| author | Srar, Jalal Chung, Kah-Seng |
| author2 | APCC 2008 Technical Program Committee |
| author_facet | APCC 2008 Technical Program Committee Srar, Jalal Chung, Kah-Seng |
| author_sort | Srar, Jalal |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | A new adaptive algorithm, called RLMS, which combines the use of recursive least square (RLS) and least mean square (LMS), is proposed for array beam forming. The convergence of the RLMS algorithm is analyzed, in terms of mean square error, in the presence of additive white Gaussian noise. Computer simulation results show that the convergence performance of RLMS is superior to either RLS or LMS operating on its own. Furthermore, the convergence of RLMS is quite insensitive to changes in either signal-to-noise ratio, or the initial value of the input correlation matrix for the RLS section, or the step size adopted for the LMS section. |
| first_indexed | 2025-11-14T08:24:25Z |
| format | Conference Paper |
| id | curtin-20.500.11937-31618 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:24:25Z |
| publishDate | 2008 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-316182017-01-30T13:26:24Z Adaptive array beam forming using a combined RLS-LMS algorithm Srar, Jalal Chung, Kah-Seng APCC 2008 Technical Program Committee A new adaptive algorithm, called RLMS, which combines the use of recursive least square (RLS) and least mean square (LMS), is proposed for array beam forming. The convergence of the RLMS algorithm is analyzed, in terms of mean square error, in the presence of additive white Gaussian noise. Computer simulation results show that the convergence performance of RLMS is superior to either RLS or LMS operating on its own. Furthermore, the convergence of RLMS is quite insensitive to changes in either signal-to-noise ratio, or the initial value of the input correlation matrix for the RLS section, or the step size adopted for the LMS section. 2008 Conference Paper http://hdl.handle.net/20.500.11937/31618 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4773748 Institute of Electrical and Electronics Engineers (IEEE) fulltext |
| spellingShingle | Srar, Jalal Chung, Kah-Seng Adaptive array beam forming using a combined RLS-LMS algorithm |
| title | Adaptive array beam forming using a combined RLS-LMS algorithm |
| title_full | Adaptive array beam forming using a combined RLS-LMS algorithm |
| title_fullStr | Adaptive array beam forming using a combined RLS-LMS algorithm |
| title_full_unstemmed | Adaptive array beam forming using a combined RLS-LMS algorithm |
| title_short | Adaptive array beam forming using a combined RLS-LMS algorithm |
| title_sort | adaptive array beam forming using a combined rls-lms algorithm |
| url | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4773748 http://hdl.handle.net/20.500.11937/31618 |