SUPERRESOLUTION TECHNIQUES FOR TIME-DOMAIN MEASUREMENTS WITH A NETWORK ANALYZER

Superresolution techniques for time delay estimation are proposed, which are applied to frequency-domain data measured with a network analyzer. Among many superresolution techniques, we employ a MUSIC (MUltiple SIgnal Classification) algorithm preprocessed by spatial smoothing. The spatial smoothing preprocessing is performed to destroy signal coherence, and we examine the decorrelation performance in detail. Furthermore, the expression which gives an individual response is shown, namely, using the expression, we may eliminate unwanted signals that appear as ripples in the frequency domain. Experimental results show that the frequency bandwidth required by the MUSIC algorithm to resolve distinct time-domain responses and eliminate unwanted signals is much narrower than that required by the fast Fourier transform (FFT). Thus, it may be said that the MUSIC algorithm is applicable to the time-domain measurements with the network analyzer and has much higher resolution capability than the conventional FFT techniques. The MUSIC algorithm is one of the most promising methods to enhance the accuracy of measurement for narrow-band devices such as antennas.