Misadjustment and tracking analysis of the constant modulus array

Keerthi, A.V.; Mathur, Abhishek; Shynk, J.J.

doi:10.1109/78.651171

Cited by 8 publications

(6 citation statements)

References 21 publications

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“…For the purpose of blind source separation, an additional complication is that only a single source is found at a time. To recover the other signals successively or in parallel, the previous solutions have to be removed from the data, or independence constraints must be introduced, with additional complications for the convergence [11], [14]- [16], [21], [23].…”

Section: Introductionmentioning

confidence: 99%

Asymptotic properties of the algebraic constant modulus algorithm

Veen

2001

IEEE Trans. Signal Process.

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Abstract-The algebraic constant modulus algorithm (ACMA) is a noniterative blind source separation algorithm. It computes jointly beamforming vectors for all constant modulus sources as the solution of a joint diagonalization problem. In this paper, we analyze its asymptotic properties and show that (unlike CMA) it converges to the Wiener beamformer when the number of samples or the signal-to-noise ratio (SNR) goes to infinity. We also sketch its connection to the related JADE algorithm and derive a version of ACMA that converges to a zero-forcing beamformer. This gives improved performance in applications that use the estimated mixing matrix, such as in direction finding.Index Terms-Array signal processing, blind beamforming, constant modulus algorithm, simultaneous diagonalization.

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Section: Introductionmentioning

confidence: 99%

Asymptotic properties of the algebraic constant modulus algorithm

Veen

2001

IEEE Trans. Signal Process.

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“…Let and denote the polynomials associated with the even-and odd-indexed subchannels Then, it can be shown that if these polynomials do not have common zeros, and if , then there exists an equalizer that leads to an overall channel-equalizer impulse response of the form col (4) for some constant phase shift , and where the unit entry is in some position , . Equalizers that result in overall impulse responses of the above form are called zero-forcing equalizers and will be denoted by .…”

Section: A Perfect Equalizationmentioning

confidence: 99%

“…Although these assumptions may not hold in general, they are realistic for sufficiently small step sizes and, as we shall see from several simulations, lead to good fits between our theoretical results and the simulation results. 4 Following each assumption, we will provide a brief motivation and justification for its use.…”

Section: Steady-state Analysis Of Cma2-2mentioning

confidence: 99%

“…The difficulty arises from the fact that classical approaches to steady-state performance evaluation often require, as an intermediate step, that a recursion be determined for the covariance matrix of the weight error vector. This step can become a burden for CM algorithms due to their inherent nonlinear updates (see, e.g., the analysis of the constant modulus array algorithm for adaptive beamforming in [4] and the analysis of the performance of CMA for interference cancellation in [5,Sec. 3.3]).…”

Section: Introductionmentioning

confidence: 99%

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A feedback approach to the steady-state performance of fractionally spaced blind adaptive equalizers

John

Sayed

2000

IEEE Trans. Signal Process.

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Abstract-This paper proposes a new approach to the analysis of the steady-state performance of constant modulus algorithms (CMA), which are among the most popular adaptive schemes for blind equalization. A major feature of the proposed feedback approach is that it bypasses the need for working directly with the weight error covariance matrix. In so doing, approximate expressions for the steady-state mean-square error of several CM algorithms are derived, including CMA2-2, CMA1-2, normalized CMA, and a new normalized CMA variant with less bias. A comparison among the various algorithms is also performed, along with several simulation results. The conclusions confirm the superior performance of CMA2-2.Index Terms-Adaptive filter, blind equalization, constant modulus signal, feedback analysis, mean-square error.

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“…Most existing CMAs [2] find only a single source. In the context of data-adaptive algorithms, the CM Array [3,4] attempts to find all sources recursively, by using a CMA to find a source and an LMS to subtract it from the data. However, convergence may be slow and subsequent signals tend to be less accurate due to misadjustment; it is also possible that the same signal is found twice.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Version of the Algebraic Constant Modulus Algorithm

Veen

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

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The Algebraic Constant Modulus Algorithm (ACMA) is a very effective block-algorithm for blind source separation. A particular feature is that it finds beamformers for all sources simultaneously. However, in cases with continuously transmitting sources and varying environments, data-adaptive algorithms may be more appropriate. In this paper, we derive an adaptive version of the ACMA, with a reasonable computational complexity. Simulations indicate that it is reliably tracking beamformers to all sources even in a highly time-varying scenario.

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Misadjustment and tracking analysis of the constant modulus array

Cited by 8 publications

References 21 publications

Asymptotic properties of the algebraic constant modulus algorithm

Asymptotic properties of the algebraic constant modulus algorithm

A feedback approach to the steady-state performance of fractionally spaced blind adaptive equalizers

An Adaptive Version of the Algebraic Constant Modulus Algorithm

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