## Abstract

A direct data domain approach to adaptively receiving a signal coming from a given direction in the presence of strong jammers and clutter is presented. In conventional adaptive algorithms, the statistical approach is based on forming an estimate of the covariance matrix of the received antenna voltages (measured voltages at the antenna terminals) without the signal. In this article, first a review of the conventional approaches is presented, where the number of adaptive weights is usually equal to the number of antenna elements. The equations for solutions of the weights are then generated by performing an averaging in time. This makes it possible to handle the unequally spaced antenna case (as in MUSIC). Unfortunately this methodology cannot handle coherent jammers (which may be due to multipath effects) or cases where the noise statistics change quite rapidly. To mitigate the above problems this article presents a direct data domain approach to the adaptive problem, where an equation error is minimized. This is ideally suited to a highly nonstationary environment, particularly in the presence of blinking jammers and for problems where the clutter characteristics change quite rapidly. Hence the processing in the data domain is done on a snapshot-by-snapshot basis. Two new direct data domain approaches are presented. One is based on the computation of a generalized eigenvalue for the signal strength, and the other is based on the solution of a set of block Hankel matrix equations. Since the system matrix has a block Hankel structure, the conjugate gradient method and the Fast Fourier transform can be utilized for efficient solution of the adaptive problem in hardware in real time. Also a methodology which prevents signal cancellation when the direction of arrival is not known precisely is presented. Limited examples are presented to illustrate the efficacy of this technique.

Original language | English (US) |
---|---|

Pages (from-to) | 185-194 |

Number of pages | 10 |

Journal | Digital Signal Processing: A Review Journal |

Volume | 6 |

Issue number | 3 |

DOIs | |

State | Published - Jul 1996 |

## ASJC Scopus subject areas

- Artificial Intelligence
- Signal Processing
- Applied Mathematics
- Electrical and Electronic Engineering
- Computer Vision and Pattern Recognition
- Statistics, Probability and Uncertainty
- Computational Theory and Mathematics