Direction-finding performance of the multistage CM array

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

doi:10.1109/acssc.1994.471581

Cited by 11 publications

(6 citation statements)

References 5 publications

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“…It was observed that the algorithm can lock onto one of the interfering signals rather than the desired signal. In later papers, this misbehavior was used to set up a multi-stage CMA, in which the intention is to capture all incident CM signals [10,26,27]. The output of a first CMA stage results in the detection of the first CM signal.…”

Section: A Blind Signal Separationmentioning

confidence: 99%

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An analytical constant modulus algorithm

Veen

Paulraj

1996

IEEE Trans. Signal Process.

449

363

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Abstract-Iterative constant modulus algorithms such as Godard and CMA have been used to blindly separate a superposition of co-channel constant modulus (CM) signals impinging on an antenna array. These algorithms have certain deficiencies in the context of convergence to local minima and the retrieval of all individual CM signals that are present in the channel. In this paper, we show that the underlying constant modulus factorization problem is, in fact, a generalized eigenvalue problem, and may be solved via a simultaneous diagonalization of a set of matrices. With this new, analytical approach, it is possible to detect the number of CM signals present in the channel, and to retrieve all of them exactly, rejecting other, non-CM signals. Only a modest amount of samples are required. The algorithm is robust in the presence of noise, and is tested on measured data, collected from an experimental set-up.

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Section: A Blind Signal Separationmentioning

confidence: 99%

“…[37], so that it has at least the complexity of (28). Table I gives a listing of (27) and (28) for a range of values of m and d, in kflop per snapshot (i.e., n 1). It is seen that for up to 6 sources the complexity of the ACMA is comparable to the GSA.…”

Section: Computational Complexitymentioning

confidence: 99%

An analytical constant modulus algorithm

Veen

Paulraj

1996

IEEE Trans. Signal Process.

449

363

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“…It was shown in [21] that if the AOAs of the received signals are well separated, then for (for example, is 25 dB down when the AOAs are separated by only 5 ). For this situation, the canceler vectors are accurate estimates of the source direction vectors and thus are appropriate directional constraints for the parallel LCCM array implementation.…”

Section: Direction Vector Bias Sinr and Sirmentioning

confidence: 98%

“…The canceler weights are no longer an accurate estimate of the direction vector for , so that the weights of the th stage LCCM array converge instead to (20) Substituting (20) and (1), resolving in terms of its direction vectors, and substituting where , the output can be rewritten as (21) Note that and have forms similar to those of and , respectively, except that they depend on the modified (biased) direction vectors of the cascade structure.…”

Section: Direction Vector Bias Sinr and Sirmentioning

confidence: 98%

“…Since the parallel LCCM stages operate on the same input (unlike the cascade system), the hybrid implmentation does not have the problem where a user captured in a particular stage might be partially canceled in a previous stage. However, it was shown in [21] that when the AOAs of the received signals are close, the canceler vectors will be biased estimates of the direction vectors. We analyze this behavior analytically for the hybrid implementation and illustrate the performance of the receiver via computer simulations.…”

Section: Introductionmentioning

confidence: 97%

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A Multistage Hybrid Constant Modulus Array With Constrained Adaptation for Correlated Sources

Venkataraman

Shynk

2007

IEEE Trans. Signal Process.

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The multistage constant modulus (CM) array was previously proposed for capturing multiple received signals in a cochannel signal environment. It consists of a cascade of individual CM array stages combined with adaptive signal cancelers that remove the various signals captured across the stages. However, when the received signals are mutually correlated, the signals captured by the CM array stages are not completely canceled, and previous parallel extensions of the system do not guarantee that different signals will be captured across the stages. In this paper, we present a hybrid implementation of the multistage CM array for separating correlated signals where the canceler weights in the cascade structure provide estimates of the direction vectors of the captured signals. These estimates are then used in a parallel implementation of the linearly constrained CM (LCCM) array leading to the hybrid structure. Since the direction vectors are obtained directly from the canceler weights, the hybrid implementation does not require prior knowledge of the array response matrix and is independent of the type of antennas used in the receiver. The effect of a bias in the direction vector estimates for closely-spaced signals is analyzed, and the steady-state performance of the hybrid structure is compared to that of a conventional constrained implementation for correlated sources. Computer simulations for example cochannel scenarios are provided to illustrate various properties of the system. Mean-square-error (MSE) learning curves indicate that the proposed hybrid LCCM algorithm converges faster and has lower MSE than previous implementations.Index Terms-Adaptive array signal processing, blind signal separation, constrained adaptive beamforming, interference suppression.

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