### Abstract

In most adaptive algorithms, it is generally assumed that one knows the direction of arrival (DOA) of the signal of interest (SOI) through the steering vector of the array and the goal is to estimate its complex amplitude in the presence of jammer, clutter and noise. In space-time adaptive processing (STAP) the goal is to seek for a target located along a certain look direction and at a particular Doppler frequency through a given steering vector. Therefore, the accuracy of the computed results in either case is based on the reliability of this a priori assumption of the steering vector. It is possible that due to mechanical vibrations, calibration errors or atmospheric refractions of the incident electromagnetic waves, the assumed DOA may not be very accurate or that the assumed value of the Doppler frequency is not appropriate. In either of these cases, the adaptive algorithm treats the SOI as an interferer and nulls it out. This perennial problem of signal cancellation is an open problem for adaptive algorithms. In this paper It is shown that the proper steering vector occurs at the minimum of the sum of the norm of the adaptive weights and can be used as an indicator to refine the estimate of the DOA of the SOI in adaptive algorithms or both the DOA or/and the Doppler frequency in STAP. Examples are presented to illustrate that the secondary processing outlined in this paper, may provide a refined estimate for the true DOA or/and Doppler frequency for the SOI in the presence of interference, clutter and noise.

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

Pages (from-to) | 1045-1050 |

Number of pages | 6 |

Journal | IEEE Transactions on Antennas and Propagation |

Volume | 54 |

Issue number | 3 |

DOIs | |

State | Published - Mar 1 2006 |

### Keywords

- Adaptive algorithm
- Direct data domain least squares algorithm
- Minimum norm solution
- Optimum weights

### ASJC Scopus subject areas

- Electrical and Electronic Engineering

## Fingerprint Dive into the research topics of 'Minimum norm property for the sum of the adaptive weights for a direct data domain least squares algorithm'. Together they form a unique fingerprint.

## Cite this

*IEEE Transactions on Antennas and Propagation*,

*54*(3), 1045-1050. https://doi.org/10.1109/TAP.2005.863150