International Conference on Acoustics, Speech, and Signal Processing
DOI: 10.1109/icassp.1990.116173
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Direction finding algorithms based on high-order statistics

Abstract: Direction finding techniques are usually based on the second order statistics of the received data. In this paper we derive two types of direction finding algorithms which use the fourth order cumulants of the array data. One is a MUSIC-like technique based on eigendecomposition of a suitably defined cumulant matrix. The other is an optimal (asymptotically minimum variance) estimator based on minimization of a certain cost function.

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Cited by 65 publications
(108 citation statements)
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“…In the IQML method the coefficients are found via an iterative procedure that involves the solution of a quadratic minimization problem at each iteration step. Similarly, in case of the FO statistics -based minimum variance algorithm [11] the DOAs are found as solutions of a nonlinear optimization problem with accuracy slightly better than obtained by MUSIC-like FO methods [11]. Here, we have shown that thanks to the MRCA concept, the FO RPR algorithm retains the same algebraic simplicity as the SO RPR algorithm and is computationally much simpler than FO minimum variance method proposed in [11].…”
Section: T S T mentioning
confidence: 75%
See 1 more Smart Citation
“…In the IQML method the coefficients are found via an iterative procedure that involves the solution of a quadratic minimization problem at each iteration step. Similarly, in case of the FO statistics -based minimum variance algorithm [11] the DOAs are found as solutions of a nonlinear optimization problem with accuracy slightly better than obtained by MUSIC-like FO methods [11]. Here, we have shown that thanks to the MRCA concept, the FO RPR algorithm retains the same algebraic simplicity as the SO RPR algorithm and is computationally much simpler than FO minimum variance method proposed in [11].…”
Section: T S T mentioning
confidence: 75%
“…At this point we would like to comment on the relation between the SO RPR algorithm and the Iterative Quadratic Maximum-Likelihood (IQML) algorithm of [14], as well as on the relation between the FO RPR algorithm and the FO statistics-based minimum variance algorithm described in [11]. Both SO RPR and IQML algorithms estimate DOAs from the L roots of the L-th order polynomial.…”
Section: T S T mentioning
confidence: 99%
“…A new method based on multiscale decomposition and high-order statistics is presented for the simultaneous solution of multiuser interference and time-varying multipath propagation in the uplink of a cellular direct-sequence spread-spectrum code-division multiple-access (DC/CDMA) system [4]. The high-order statistics were also used in direction finding and the parameters estimation of exponentially damped sinusoids signals [5], [6]. It is demonstrated the new methods are effective when the additive Gaussian noise is present.…”
Section: Introductionmentioning
confidence: 99%
“…Higher-order statistics have begun to find wide applicability in many fields, such as communications, sonar, radar, and so on [1][2][3][4][5][6][7]. Non-Gaussian signals have valuable statistics information in their highorder moments.…”
Section: Introductionmentioning
confidence: 99%
“…PM has a lower computational complexity at the expense of negligible performance loss. In [10][11][12][13][14][15][16], the DOA estimation methods have been developed based on higherorder statistics instead of second-order statistics using generalized eigen structure analysis. Porat and Friedlander [12] proposed the DOA estimation method based on fourth-order cumulant to eliminate the effect of Gaussian noise from the non-Gaussian signals.…”
Section: Introductionmentioning
confidence: 99%