Minimum MSE-based detection of cyclostationary signals in array processing

Xin, Jingmin; Tsuji, Hiroyuki; Yoshimoto, Shigetoshi; Sano, Akira

doi:10.1109/spawc.1997.630228

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…3] and [16]. We have also proposed a detection method by minimizing the mean-squared-error (MSE) of the estimated LP parameters in [21] and [46], and it is well suited to be used in conjunction with the approach for the coherent signals described in this paper. If the cycle frequency is unknown, it can be estimated from the finite data by using the methods proposed in [18] and [29].…”

Section: A Data Modelmentioning

confidence: 99%

“…Most man-made communication signals exhibit cyclostationarity for a given cycle frequency because of the underlying periodicity arising from carrier frequencies or baud rates [13], [14]. Many direction estimation methods exploiting this inherently temporal property have been developed recently (see, e.g., [14]- [21] and references therein) in which the stationary noise and the interfering signals that do not share a cycle frequency common to the desired signals are suppressed. For estimating the directions of coherent cyclostationary signals, a cyclic ML method [22] and an SS-based cyclic MUSIC method [23], [50] were proposed.…”

Section: Introductionmentioning

confidence: 99%

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Linear prediction approach to direction estimation of cyclostationary signals in multipath environment

Xin¹,

Sane²

2001

IEEE Trans. Signal Process.

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In this paper, we investigate the estimation of the directions-of-arrival (DOA) of closely spaced narrowband cyclostationary signals in the presence of multipath propagation. By exploiting the spatial and temporal properties of most communication signals, we propose a new cyclic forward-backward linear prediction (FBLP) approach for coherent signals impinging on a uniform linear array (ULA). In the proposed algorithm, the evaluation of the cyclic array covariance matrix is avoided, and the difficulty of choosing the optimal time lag parameter is alleviated. As a result, the proposed approach has two advantages: 1) The computational load is relatively reduced, and 2) the robustness of estimation is significantly improved. The performance of the proposed approach is confirmed through numerical examples, and it is shown that this approach is superior in resolving the closely spaced coherent signals with a small length of array data and at relatively low signal-to-noise ratio (SNR).

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Section: A Data Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Linear prediction approach to direction estimation of cyclostationary signals in multipath environment

Xin¹,

Sane²

2001

IEEE Trans. Signal Process.

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“…Cyclic direction finding methods have been proposed, that incorporate the cyclostationary characteristic to improve the performance. A cyclic-MUSIC method was proposed in [ 1,6] and a least-squares method based on a Lp model of the sensor outputs was presented in [5].…”

Section: Introductionmentioning

confidence: 99%

Direction estimation using conjugate cyclic cross-correlation: more signals than sensors

Manimohan

Fitzgerald

1999

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)

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We consider the problem of estimating the directions of arrival of multiple communication signals arriving at an uniform linear array. By considering the conjugate cyclic crosscorrelations of the sensor outputs and using a Bayesian framework, we propose a direction finding algorithm that allows us to estimate the directions of arrival for a more signals than sensors scenario. The algorithm does not need any training sequence and only requires apriori knowledge of the cyclic frequencies and the number of sources. It is also possible to estimate the directions of arrival in a multi-path fading environment under certain conditions.

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