Adaptive covariance matrix taper for GNSS antenna array receivers through subspace leakage analysis

doi:10.23919/JSEE.2025.000148

Abstract

Abstract:

Antenna arrays have become a popular choice for protecting global navigation satellite system (GNSS) receivers in strong radio frequency interference environments. Environmental nonstationarities, which are caused by fast variations in propagation channels and rapid motion of interfering sources or antenna arrays, cause performance degradation of antenna array receivers. The covariance matrix taper (CMT) has been used to widen the adaptive pattern nulls toward the interference sources in GNSS antenna array receivers to address highly dynamic environments. In this paper, it is verified that the loss of the signal to interference and noise ratio (SINR) caused by a CMT cannot be ignored, and a CMT with a fixed null width cannot work well when the dynamic range of the GNSS array receiver is large. To design the modest null width, a CMT with an adaptive null width for space-time adaptive processing (STAP) is introduced. In the adaptive CMT, the proposed subspace leakage in STAP is used to estimate the required null width. In the simulations, the SINRs of adaptive CMT are always close to the SINRs of the optimal fixed null width in different scenarios, and the average SINRs of the adaptive CMT are even more than 10 dB higher than that of CMT with fixed null width in some scenarios. The adaptive CMT can be widely applied to GNSS antenna array receivers with a large dynamic range.

Key words: global navigation satellite system (GNSS), space-time adaptive processing (STAP), high dynamic, covariance matrix taper (CMT)

Jie Wang, Wei Xiao, Fangxuan Zou, Wenxiang Liu, Gang Ou. Adaptive covariance matrix taper for GNSS antenna array receivers through subspace leakage analysis[J]. Journal of Systems Engineering and Electronics, 2026, 37(5): 1438-1448.

Figures/Tables 15

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Scenario	Rotational speed/(°/s)	Average output SINR/dB
Scenario	Rotational speed/(°/s)	Adaptive	$ {\varDelta _\theta } = 0.01^\circ $	$ {\varDelta _\theta } = 0.1^\circ $	$ {\varDelta _\theta } = 1^\circ $
Scenario 1	100	−28.2	−28.2	−30.0	−31.0
	1000	−29.3	−28.7	−30.0	−31.0
	8000	−31.6	−34.5	−30.9	−31.1
Scenario 2	100	−31.6	−31.8	−37.9	−42.4
	1000	−33.5	−32.2	−37.9	−42.4
	8000	−40.2	−40.7	−40.0	−42.4
Scenario 3	100	−28.5	−28.3	−29.8	−30.9
	1000	−30.1	−32.9	−30.3	−31.0
	8000	−31.6	−33.5	−32.7	−30.7
Scenario 4	100	−32.0	−31.8	−37.4	−41.5
	1000	−38.5	−37.7	−38.4	−41.4
	8000	−42.7	−44.0	−44.9	−42.4

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