Large sensor arrays are very important and widely applied in different fields. Instead of using a single sensor to receive signals, many sensors are used in concert to enhance the received signals with low signal-to-noise ratio (SNR). One of the proven techniques is called signal combining, whose main idea is to find a set of combining weights, so that the maximum output SNR can be achieved.

Traditional eigen-based signal combining methods are stable and effective. SNR, combined output power (COP), and autocorrelation coefficient (AC) are used as the objective functions. These algorithms are referred as SNR EIGEN, COP EIGEN, and AC EIGEN, respectively.

The major problem for all above eigen-based approaches is the heavily computational burden. Their calculation involves forming signal and noise correlation matrices. As a result, the eigen-based methods require huge computational cost in term of hardware circuits and systems as the number of antennas and the length of sample increase.

Earlier this year, WANG Leiou and his team from the Institute of Acoustics (IOA) of the Chinese Academy of Sciences proposed a fast eigen-based method that employed a signum polarization model and Chebyshev polynomials (CP), but the extra multiplications were incurred by using CP.

To further reduce the computational cost, WANG Leiou and his team proposed a fast eigen-based signal combining algorithm by using coordinate rotation digital computer (CORDIC).

CORDIC is an iterative method to calculate trigonometric function, which can use addition and bitshift operations to replace the multiplications in the eigen-based signal combining algorithms.

Researchers first obtained an estimation of polarization cross correlation function by using SP model. Then, they used CORDIC to calculate an estimation of the cross correlation function to eliminate the extra multiplications that incurred by using CP. The computational accuracy and the computational cost were also discussed in detail.

Simulation results indicated that the proposed algorithm could be flexibly applied to COP or AC EIGEN. What’s more, this algorithm can effectively reduce the computational cost while providing a good combining performance.

Reference：

WANG Leiou, WANG Donghui, HAO Chengpeng. A Fast Eigen-based Signal Combining Algorithm by Using CORDIC. 2018 26th European Signal Processing Conference (September 2018).

Contact:

WANG Rongquan

Institute of Acoustics, Chinese Academy of Sciences, 100190, Beijing, China

Email: media@mail.ioa.ac.cn