Data‐oriented calibration method to reduce measurement bias in SFAP‐based GNSS receivers

Abstract

Spatial frequency adaptive processing (SFAP) is the dimension-reduced alternative of spatial temporal adaptive processing (STAP), which can achieve good interference mitigation capability with less computational cost. However, just as STAP, the biases of the ranging code phase and carrier phase induced by the adaptive array restrict SFAP from being applied in high-precision global navigation satellite system receivers. To address this issue, a new data-oriented calibration method is proposed. Unlike previous bias reduction methods, which compensate the measurement biases with estimated values in the tracking loop, this method carries out calibration directly on the received data in the frequency domain, thus avoids modification to the rest parts of the receiver other than the SFAP module. The calibration value in each frequency bin is determined from the transfer function of the SFAP array. Simulations are performed on a practical array antenna designed by the high-frequency structure simulator software with high fidelity, and the results verify the effectiveness of this method.

Beidou receiver based on anti-jamming antenna arrays with self-calibration for precise relative positioning

“…Array signal processing has been widely studied for GNSS anti-jamming application recently [25][26][27][28], and there are some insufficient researches which are not verified in real-life experiment about the accuracy of antenna calibration [29][30][31][32][33]. Additionally, there are some studies on the compensation performance of phase distortion [34−38].…”

A Space-Frequency Anti-Jamming Algorithm Based on Sub-Band Energy Detection

“…The core idea of the SFAP algorithm is to convert received GPS signals into the frequency domain by fast Fourier transform (FFT) so as to perform sub-band decomposition, then the adaptive weights are calculated on each sub-band, respectively, thereby the dimensionality reduction processing of the covariance matrix is realized [13]. Some achievements have been made in the research and improvement of the SFAP algorithm [14][15][16][17]. [14] and [15] detail the application of SFAP in GPS and spread spectrum stations.…”

“…[14] and [15] detail the application of SFAP in GPS and spread spectrum stations. On this basis, for the application of SFAP in GNSS, some progress have been made in the researches on the robustness [16] and the estimation of antenna induced biases [17]. These studies include improvements to the performance of SFAP algorithms and error correction in applications.…”