Improving antenna gain estimations in non-ideal test sites with auto-tunable filters

Abstract

Estimating the gain of an antenna prototype is an important step during both design and validation phases. Nevertheless, many small technology companies and universities are not provided with adequate infrastructure to run this type of test. In this paper, we propose a computational tool to deal with undesirable contributions coming from non-ideal test conditions over electromagnetic measurements. It is based on auto-tunable filters and therefore it allows a lab technician to run the test without a major knowledge on signal processing. After a series of evaluations based on lab measurements, the proposed tool gives results as good as those provided by two other approaches but with a reduction of the total number of experimental runs. Furthermore, it is more efficient than the traditional time gating approach for low signal-to-noise ratio environments. Finally, a validation with different antennas and test sites confirmes its robustness.

Multi-taper-based automatic correction of non-anechoic antenna measurements

“…The proposed correction framework has been subjected to a comparative analysis (in both test sites) against the state-ofthe-art post-processing algorithms that involve time-domain analysis of the The of benchmark methods includes the determination of intervals (pertinent to the relevant part of the RA-AUT transmission) based on: (i) manual estimation of the RA-AUT distance (with rectangular window function), (ii) cognition-based analysis of the impulse response (with Hann function), and (iii) determination of intervals using a composite window [12][13][14]32]. To ensure an equitable comparison, the configuration parameters, i.e.…”

“…Example locations include office cubicles, hallways, courtyards, or parks [9][10][11]. Unfortunately, the responses directly obtained in such non-anechoic conditions are useless for rendering meaningful conclusions on the far-field performance of the antenna under test (AUT) [9][10][11][12].…”

“…Popular methods are based on the concepts of: (i) signal decomposition and (ii) characterization of the propagation environment . The first group involves extracting the useful part of the responsepertinent to line-of-sight transmission between the reference antenna (RA) and AUT-using time-, or frequencydomain methods [9][10][11][12][13][14][15][16][17][18][19][20]. The former exploits window functions centered around the signal pulse pertinent to direct transmission [9][10][11][12][13][14].…”

“…The first group involves extracting the useful part of the responsepertinent to line-of-sight transmission between the reference antenna (RA) and AUT-using time-, or frequencydomain methods [9][10][11][12][13][14][15][16][17][18][19][20]. The former exploits window functions centered around the signal pulse pertinent to direct transmission [9][10][11][12][13][14]. The frequency-based routines focus on approximating the relevant fraction of the response using truncated compositions of basis functions.…”

“…The second class of methods is oriented towards extracting the effects of the propagation environment on the RA-AUT transmission. This is predominantly realized based on a series of independent measurements performed in the laboratory [12,20,24]. Practical approaches to the problem involve analysis of the AUT in multiple locations within the test-site [24], extraction of the noise floor in the environment [12], or estimation of the equivalent currents on a hull that encloses the radiator [28].…”