Radio Frequency Interference Mitigation Algorithms Research Articles

Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties

Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the partial features of RFI, but the RFI reconstruction models in these algorithms are rough and can be improved further. This paper proposes two algorithms for accurately modeling the structural properties of RFI and target echo signal (TES). Firstly, an RFI mitigation algorithm joining the low-rank characteristic and dual-sparsity property (LRDS) is proposed. In this algorithm, RFI is treated as a low-rank and sparse matrix, and the sparse matrix assumption is made for TES in the time–frequency (TF) domain. Compared with the traditional low-rank and sparse models, it can achieve better RFI mitigation performance with less signal loss and accelerated algorithm convergence. Secondly, the other RFI mitigation algorithm, named as TFC-LRS, is proposed to further reduce the signal loss. The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. Finally, the effectiveness, superiority, and robustness of the proposed algorithms are verified by RFI mitigation experiments on the simulated and measured SAR datasets.

Efficient Narrowband RFI Mitigation Algorithms for SAR Systems With Reweighted Tensor Structures

Radio-frequency systems, such as TV and cellular networks, severely interfere with synthetic aperture radar (SAR) systems. Narrowband radio-frequency interference (RFI) has a special low-rank property in the received signal matrix, because it performs like a sinusoid with nearly invariant frequency as the slow time proceeds. Exploiting this special property, in this paper, we divide the received signal matrix into several small matrices, in each of which the RFI is also low rank. Without losing the connection between these small matrices, we stack them into a three-mode tensor to separate the low-rank RFI tensor and recover the informative signal tensor. Previous studies employed the nuclear norm to regularize the low-rank RFI, which is not a good choice. Hence, we propose two reweighted algorithms, the reweighted tensor nuclear norm (RTNN) and the reweighted tensor Frobenius norm (RTFN) algorithms, to approximate the rank function in a tensor and accurately extract the low-rank RFI tensor from the received signal tensor. As a result, the introduction of the tensor structure dramatically decreases the computational cost. Furthermore, the reweighted scheme helps suppressing the RFI and recovering the useful signal with excellent performance. Finally, real SAR data with measured RFI is employed to demonstrate the effectiveness of the proposed methods for RFI mitigation.

Impact of Signal Quantization on the Performance of RFI Mitigation Algorithms

Radio Frequency Interference (RFI) is currently a major problem in Communications and Earth Observation, but it is even more dramatic in Microwave Radiometry because of the low power levels of the received signals. Its impact has been attested in several Earth Observation missions. On-board mitigation systems are becoming a requirement to detect and remove affected measurements, increasing thus radiometric accuracy and spatial coverage. However, RFI mitigation methods have not been tested yet in the context of some particular radiometer topologies, which rely on the use of coarsely quantized streams of data. In this study, the impact of quantization and sampling in the performance of several known RFI mitigation algorithms is studied under different conditions. It will be demonstrated that in the presence of clipping, quantization changes fundamentally the time-frequency properties of the contaminated signal, strongly impairing the performance of most mitigation methods. Important design considerations are derived from this analysis that must be taken into account when defining the architecture of future instruments. In particular, the use of Automatic Gain Control (AGC) systems is proposed, and its limitations are discussed.

Radio Frequency Interference Suppression for HF Surface Wave Radar Using CEMD and Temporal Windowing Methods

A common source of interference in high-frequency (HF) surface wave (HFSW) radars is radio frequency interference (RFI). Its existence inhibits the detection performance of HFSW radars since its amplitude can mask the sea echoes. On the basis of the analysis of RFI characteristics, a new RFI mitigation algorithm based on inverse temporal windowing and complex empirical mode decomposition (CEMD) is proposed in this letter. In this method, echoes containing RFI are decomposed into a number of intrinsic mode functions (IMFs) via CEMD and then the inverse temporal windowing technique is applied to each IMFs in the time domain. Test results show that the proposed method outperforms the conventional method in simulated and practical conditions and can effectively mitigate RFI without losing sea echoes.

Mitigation of Non-Narrowband Radio Frequency Interference Incorporating Array Imperfections

In a recent paper, we presented a non-narrowband spatial radio frequency interference (RFI) mitigation algorithm for radio astronomy arrays. The algorithm constructs a 2nd-order filter by combining a 1st-order subspace subtraction method with a non-narrowband signal model. The model is based on the assumption that the frequency response is approximately flat and that the array is calibrated. In this paper, we consider the effects of array imperfections such as unknown complex gains and mutual coupling, incorporate these into the non-narrowband signal model and extend the RFI mitigation algorithm to include a calibration step. With a calibration step and no mutual coupling, the proposed algorithm was able to process twice the bandwidth per channel when compared to conventional narrowband techniques. This performance declines to 1.6 times greater bandwidth when the effect of mutual coupling is included. The evaluation of the algorithm was done using the layout of a Low Frequency Array (LOFAR) High Band Antenna (HBA) station and a digital audio broadcast recorded with a software defined radio.

Radio frequency interference mitigation using deep convolutional neural networks

Abstract We propose a novel approach for mitigating radio frequency interference (RFI) signals in radio data using the latest advances in deep learning. We employ a special type of Convolutional Neural Network, the U-Net , that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. We train and assess the performance of this network using the HIDE & SEEK radio data simulation and processing packages, as well as early Science Verification data acquired with the 7m single-dish telescope at the Bleien Observatory. We find that our U-Net implementation is showing competitive accuracy to classical RFI mitigation algorithms such as SEEK ’s SumThreshold implementation. We publish our U-Net software package on GitHub under GPLv3 license.

Radio frequency interference mitigation in OFDM based passive bistatic radar

High frequency passive bistatic radar (HFPBR) is a novel and promising technique in development. DRM broadcast exploiting orthogonal frequency division multiplexing (OFDM) is accepted as the only standard for HF band by ITU-R. DRM broadcast transmitters supply good choice for the illuminator of HFPBR. HFPBR based on DRM broadcast is a beneficial supplement to the active HF radar. HFPBR working in crowded short wave band, as the active radar, faces with radio frequency interference (RFI) problem. The traditional amplitude modulation (AM) broadcasts are ones of the main RFI resources. Unlike the active radar, RFI will affect the reference signal reconstruction quality in HFPBR with OFDM waveform, which will directly affect the performance of direct-path wave and multipath clutter rejection. Direct-path wave and multipath clutter rejection are a stumbling block for target detection of HFPBR. As the direct-path wave and multipath clutter do, RFI also will mask potential target echoes. RFI mitigation has to share the system's degree of freedom with clutter rejection. In order not to occupy the system's degree of freedom, a temporal RFI mitigation algorithm is proposed to cope with the AM interferences, after RFI model and impacts analysis on HFPBR with new radar waveform. The proposed algorithm is a closed-form approximate maximum likelihood estimator, which is easy to perform. The impacts analysis of RFI and the RFI mitigation algorithm performance are evaluated using the simulation and experimental data of DRM-based HFPBR.