Adaptive Antijamming Based on Space-Time 2-D Sparse Array for GNSS Receivers

Abstract

Space-time adaptive antijamming problem has received significant attention recently for global navigation satellite system (GNSS). It can jointly utilize spatial filters and temporal filters to suppress interference signals. However, most of the works on space-time antijamming problem presented in the literature require a space-time two-dimension (2-D) array with multiple antennas and delay taps. In this paper, an effective adaptive antijamming method based on a space-time 2-D sparse array is proposed. The maximum array gain is utilized to construct a space-time 2-D sparse array. The spacetime antijamming weight vector is given by minimizing the 2-D sparse array output power. Compared with the previous works, the presented method can have better antijamming performance than a spacetime 2-D uniform array. Simulation results verify the effectiveness and feasibility of the proposed method.

Adaptive interference mitigation space‐time array reconfiguration by joint selection of antenna and delay tap

“…Assume that the expected signal and interference from $$\left({\phi }_{s},{\theta }_{s}\right)=\left(83{}^{\circ},55{}^{\circ}\right)$$ and $$\left({\phi }_{i},{\theta }_{i}\right)=\left(73{}^{\circ},65{}^{\circ}\right)$$ are severally incident on the planar array. In addition to the proposed methods in this study, the other two methods, called Modified Correlation Measurement (MCM) [24] and DC Programming (DCP) [26], respectively, can be borrowed and applied to space‐time array reconfiguration. Next, we evaluate these methods in terms of performance and computational complexity.…”

“…For the space‐time array reconfiguration by selecting M from NL antenna‐delay tap pairs, we can learn from Refs. [24, 26] that the algorithm complexities of the MCM and DCP methods are of order $$O\left({\left(NL\right)}^{3}\left(NL-M\right)\right)$$ and $$O\left({\left(NL\right)}^{3.5}\right)$$. According to the analysis in Section 4, the SDP and DCS methods separately have an algorithm complexity of order $$O\left({\left(NL\right)}^{3.5}\right)$$ and $$O\left({\left(NL\right)}^{2}\left(NL+1\right)\right)$$.…”

“…The complexity reduction is achieved by selecting only those partial antennas and pulses that maximise the signal to clutter plus noise ratio (SCNR) to perform subsequent STAP. Inspired by this, several sparse space‐time array design approaches suitable to the global navigation satellite system (GNSS) were proposed [25, 26]. The proportion differentiation (PD) algorithm in Ref.…”

“…Besides, the authors in Ref. [26] developed another two‐stage optimisation sparse space‐time array, including two steps of antenna optimisation and delay tap optimisation. It can discard some redundant antennas and delay taps that have little effect on performance improvement, thereby declining the computational burden.…”

RRT-MWF-MVDR Algorithm for Space-Time Antijamming

“…Assume that there are an interesting satellite signal s(t) and q narrow-band oppressive jamming signals j k (t) (k = 1, • • • , q) impacting on the ULA. Then, the MP × 1 received signals x(t) at time instant t can be expressed as [20]…”