An Approximate Algorithm for Robust Adaptive Beamforming
- PDF / 865,008 Bytes
- 9 Pages / 600 x 792 pts Page_size
- 17 Downloads / 286 Views
An Approximate Algorithm for Robust Adaptive Beamforming Tomoaki Yoshida NTT Access Network Service Systems Laboratories, Chiba 261-0023, Japan Email: [email protected]
Youji Iiguni Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan Email: [email protected] Received 11 February 2004; Revised 7 July 2004; Recommended for Publication by Mos Kaveh This paper presents an adaptive weight computation algorithm for a robust array antenna based on the sample matrix inversion technique. The adaptive array minimizes the mean output power under the constraint that the mean square deviation between the desired and actual responses satisfies a certain magnitude bound. The Lagrange multiplier method is used to solve the constrained minimization problem. An efficient and accurate approximation is then used to derive the fast and recursive computation algorithm. Several simulation results are presented to support the effectiveness of the proposed adaptive computation algorithm. Keywords and phrases: robust array antenna, Lagrange multiplier method, Taylor series approximation, direction of arrival.
1.
INTRODUCTION
The directionally constrained minimization of power (DCMP) adaptive array adjusts the array weights to minimize the mean output power while keeping the antenna response to the direction of arrival (DOA) of the desired signal [1, 2]. When the true DOA is known a priori, the DCMP array achieves a good performance. More precisely, the array provides spatial filtering that maximizes the radar’s sensitivity in the desired direction while suppressing interference signals coming from other directions and measurement noises. However, if there is a mismatch between the prescribed and actual DOAs, the desired signal is viewed as an interference and then suppressed [3]. Even a small mismatch may cause a significant performance degradation. For the solution, a number of robust array antennas that impose the directional derivative constraints [4, 5, 6, 7, 8, 9], the inequality directional constraints [10, 11, 12, 13], and the mean-square deviation constraints [14, 15, 16] have been developed. These methods succeed in achieving flat main beam magnitude responses and decreasing the array sensitivity to look-direction errors. However, the adaptive weight computation algorithm to solve the constrained minimization problem at each time step is not provided, which is required to follow changing interference environment. Although some adaptive algorithms were presented in [6, 7, 10], they were derived based on the steepest descent technique and
therefore exhibit slower convergence than the sample matrix inversion (SMI) technique [17, 18]. We here consider the robust array antenna with the inequality directional constraints [10, 11, 12, 13]. The robust array antenna is designed so that the mean output power is minimized under the constraint that the mean square deviation between the desired and actual responses satisfies a certain magnitude bound. The constrained m
Data Loading...
