Low-Complexity Spatial-Temporal Filtering Method via Compressive Sensing for Interference Mitigation in a GNSS Receiver

Abstract

A compressive sensing based array processing method is proposed to lower the complexity, and computation load of array system and to maintain the robust antijam performance in global navigation satellite system (GNSS) receiver. Firstly, the spatial and temporal compressed matrices are multiplied with array signal, which results in a small size array system. Secondly, the 2-dimensional (2D) minimum variance distortionless response (MVDR) beamformer is employed in proposed system to mitigate the narrowband and wideband interference simultaneously. The iterative process is performed to find optimal spatial and temporal gain vector by MVDR approach, which enhances the steering gain of direction of arrival (DOA) of interest. Meanwhile, the null gain is set at DOA of interference. Finally, the simulated navigation signal is generated offline by the graphic user interface tool and employed in the proposed algorithm. The theoretical analysis results using the proposed algorithm are verified based on simulated results.

The Unbiased Characteristic of Doppler Frequency in GNSS Antenna Array Processing

“…Although STAP is an effective way to mitigate the interference in GNSS signal [14], biases induced by antennas and algorithms are not negligible for precision applications, especially when a large antenna array with complicated filtering is considered [3]. This section presents a model of array processing for describing errors induced by STAP and proving their dependency on interference circumstances.…”

“…By applying some criterions [14], such as power minimization [15,16], multiple constrained minimum variance [17], and minimum mean square error [18], the power of interference signal can be effectively restrained after the adaptive weighted summation. However, characteristics of antennas and channels, as well as STAP algorithms, cause biases to the output signal.…”

Random distortion testing with linear measurements

“…We here consider a compressive sensing based array processing scenario where Y contains time series of multiple sensors. In this case, A and B can represent time and space compressed measurement matrices, respectively [22]. Such matrices are usually random and contain sub-Gaussian i.i.d.…”

Direction of Arrival Estimation Based on the Multistage Nested Wiener Filter

“…In the last two decades, smart antenna has been widely used in many applications such as radar, sonar, and wireless communication systems [1]. It is also utilized in tracking [2, 3], localization [4, 5], intelligent transportation [6], ultra-wideband wireless sensor networks [7], array calibration [8], scatter cluster model [9], and antijamming [10]. For example, the multiple input multiple output (MIMO) radar utilizes multiple sensor array antennas to simultaneously transmit and receive diverse waveforms, which estimates the signal parameters to locate and track the target [2].…”

“…The performance of the wireless communication system is evaluated based on scatter cluster models by estimating the corresponding parameters [9]. Array sensor and subarray adaptive beamforming techniques obtain the best antijamming performance widely used in GNSS receivers [10], active radar, and sonar [11]. In these sensor networks implication systems and scenarios, direction of arrival (DOA) is an important parameter that is needed to be estimated to determine the direction of the located and tracked target or the position of the sensor nodes.…”