Radio Frequency Interference Estimation Research Articles

Inverse MoM Approach to Near-Field Prediction and RFI Estimation in Electronic Devices With Multiple Radiating Elements

An inverse method of moments (MoM) approach is developed to estimate the equivalent electric/magnetic current sources to predict radio frequency interference (RFI) in electronic devices. The proposed method is designed to reconstruct the noise sources in electronic devices containing multiple radiating elements by using a finite metal object carrying electric surface currents. The surface current elements are further applied to calculate the electromagnetic fields on arbitrary observation planes near the device under test (DUT). The method is validated through numerical simulation examples, including electric-type CPU noise, magnetic-type non-planar HDMI connector noise, printed circuit board (PCB) with Wi-Fi and PIFA antennas, and heat sink embedded in the DUT. The results confirm less than 0.4 dB difference in the predicted pattern at the antenna operating frequency when a triangular segmentation is applied in the vicinity of the noise source area, and the equivalent surface current is calculated at the edges of the Rao-Wilton-Glisson (RWG) meshes. The proposed method is a promising ’source reconstruction’ candidate for predicting the near-field radiation characteristics of electronic devices with multiple peaks and nulls in the near-field radiation pattern. The method helps estimate the level of RFI by summing up the contributions of all the dipoles of the equivalent noise source derived by solving forward and inverse problems. The RFI results based on the inverse MoM approach are validated by comparing them with those obtained from CST simulation, and good agreement is achieved between the two.

Joint Down-Range and Cross-Range RFI Suppression in Ultra-Wideband SAR

Radio frequency interference (RFI) is a critical problem for ultra-wideband synthetic aperture radars (UWB SAR) because the VHF/UHF band used by them is shared by other systems as well. A number of solutions have been proposed over the years. Recently, sparsity and low-rank estimation-based solutions were shown to perform better than traditional methods such as adaptive notch filtering. These algorithms model the SAR signal to be sparse and the RFI to be either sparse or low-ranked in nature and solve an optimization problem to estimate the SAR signal and RFI simultaneously. Algorithms in this class share the common characteristic that the SAR signal sparsity is captured by modeling each data vector as a linear combination of shifted SAR pulses. This data model addresses the structure of SAR signals in the down-range direction, but the inter-aperture cross-range structure, i.e., the fact that SAR signals add coherently across the cross-range, has been completely ignored. In this work, we incorporate this “global” 2-D structure of the SAR data into the RFI mitigation problem. Two algorithms are proposed: 1) (2-D) sparse SAR and sparse RFI estimation and 2) (2-D) sparse SAR and low-rank RFI estimation. The experimental results demonstrate that the 2-D model does a much better job of capturing the sparsity of SAR and the 2-D algorithms consistently perform better than the “local” 1-D algorithms. The level of improvement rises significantly in challenging cases-when the noise level and/or the number of RFI bands increases. Experiments are conducted extensively on simulated data sets as well as real SAR and RFI data sets collected by the U.S. Army Research Laboratory (ARL) to validate the proposed framework.

Estimation and Mitigation of Time-Variant RFI Based on Iterative Dual Sparse Recovery in Ultra-Wide Band Through-Wall Radar

This paper focuses on the time-variant radio frequency interference (RFI) issue that ultra-wide band (UWB) through-wall radar (TWR) is faced with, and presents an iterative dual sparse recovery (IDSR) framework to combat it. The framework consists of two stages: 1) RFI estimation and detection and 2) IDSR of scattered echoes from objects and RFI signals. In the first stage, an overlapped short time Fourier transform is employed to construct and update the discrete frequency Doppler spectrum (DFDS). Then, a minimum statistic operation is conducted on the DFDS to estimate RFI signals, followed by detection via a 1-D cell-averaging constant false alarm rate detector to determinate whether RFI signals exist or not. In the second stage, a dual sparse model of the collected signals is set up, based on the fast-time frequency sparsity of RFI signals because of their narrow bands and the Doppler frequency sparsity of scattered echoes from objects because of their limited moving velocities. The alternating direction method of multipliers (ADMM) is introduced to iteratively and alternately recover RFI signals and scattered echoes from objects. Specifically, the iterative hard thresholding (IHT) method is used to complete the two sparse recovery operations. The involved two sparse dictionaries are simple and only made up of inverse discrete Fourier transform basis and independent of the received signals. The IDSR framework is able to relieve the influences of RFI signals and scattered echoes from objects on each other, and thus can reconstruct the two type signals to the maximum limit. Field experiments using a UWB TWR were carried out to verify the proposed method.

Mechanism and Validation of USB 3.0 Connector Caused Radio Frequency Interference

The mechanism of a universal serial bus 3.0 connector caused radio frequency interference (RFI) to a nearby antenna is studied. The connector radiation is modeled by an equivalent stripline-fed slot antenna, based on the analysis of the common mode current path on the connector structure. With the proposed connector model, the original connector radiation source can be replaced by an equivalent magnetic dipole source, which is directly correlated with the physical quantity on the connector. Applying reciprocity theorem, the equivalent dipole source can be applied to predict the coupled noise power to the antenna. The proposed connector radiation mechanism and model are validated through full-wave simulation and measurement. The application of the magnetic dipole source for RFI estimation is demonstrated in a real laptop system.

MoM-Based Ground Current Reconstruction in RFI Application

A method of moment (MoM)-based current reconstruction method is proposed to estimate the surface current density on the ground plane. Current continuity property is automatically enforced with Rao–Wilton–Glisson basis function. Both the least square method and the optimization method are utilized to solve the inverse problem and obtain the ground current. The proposed optimization method is successfully validated with numeric simulations and also a real-world measurement example. The reconstructed current distribution on the ground plane is further used in a typical radio frequency interference (RFI) example to perform RFI estimation and provide guidelines for RFI design. The proposed MoM-based ground current reconstruction method can be valuable to estimate and debug RFI issues in early design stage.

Estimation and Mitigation of Time-Variant RFI in Low-Frequency Ultra-Wideband Radar

The work presented in this letter focuses on the time-variant radio frequency interference (RFI) issue in the low-frequency ultra-wideband (UWB) radar. Different from many previous studies, we first analyze the characteristics of RFIs and scattered echoes in the slow-time dimension and take advantage of overlapped short-time Fourier transform to adapt to the time-variant RFIs and update the frequency Doppler spectrum. Then, in the frequency Doppler spectrum, we adopt the minimum statistic combined with 1-D cell-averaging constant false alarm rate to estimate and separate the RFI power spectrum from the scattered echoes based on their differences. Finally, to mitigate the estimated RFIs, a suboptimal filter controlled by the defined entropy of range profiles after math filtering is designed. Employing a UWB radar, different experiments were conducted, and results verify the proposed method.

Side-Lobe Reduction for Radio Frequency Interference Suppression via Clipping of Strong Scatterers

In this letter, we present a normalized least mean square filter with clipping of strong scatterers to suppress radio frequency interference (RFI) in low-frequency synthetic aperture radar (SAR) systems. In this filter, the impulse responses of bright scatterers are clipped after range compression to improve the performance of narrow-band RFI estimation for adaptive line enhancer. Then, the estimated RFI is subtracted from the original echo for SAR imaging; thus, the range side-lobes of bright scatterers are reduced successfully. This method is particularly applicable to situations where there are bright scatterers in the image scene. The performance for RFI suppression is investigated by using the simulated data and the raw data as well.

Efficient and Robust RFI Extraction Via Sparse Recovery

This paper presents a simple adaptive framework for robust separation and extraction of multiple sources of radio-frequency interference (RFI) from raw ultra-wideband (UWB) radar signals in challenging bandwidth management environments. RFI sources pose critical challenges for UWB systems since 1) RFI often occupies a wide range of the radar’s operating frequency spectrum; 2) RFI might have significant power; and 3) RFI signals are difficult to predict and model due to the nonstationary nature as well as the complexity of various communication devices. Our proposed framework involves an initial RFI estimation step that operates directly on already contaminated radar signals to identify RFI-dominant frequency sub-bands. This vital prior information is then taken into account to construct an adaptive RFI dictionary with various sinusoidal patterns covering the aforementioned RFI-contaminated frequency spectrum. Finally, we employ a sparsity-driven optimization strategy to separate and then extract RFI from the received radar signals. Our method can be implemented as a denoising preprocessing stage for raw radar signals prior to image formation and other follow-up tasks such as target detection and classification. Recovery results from extensive simulated data sets as well as real-world signals collected by the U.S. Army Research Laboratory (ARL) UWB synthetic aperture radar (SAR) systems illustrate the robustness and effectiveness of our proposed framework.

Estimating Interference to Airborne Patch Antenna With Limited Information

In practice, the detailed geometry information of an off-the-shelf antenna is unavailable. To achieve the estimation of radio frequency interference to an airborne patch antenna with available information, this paper proposes to utilize an equivalent model of the patch antenna. The equivalent model is derived based on the patch antenna's radiation pattern. It shows the same radiating and scattering performances as the original patch antenna. Since, no effective port or phase information is provided by the equivalent model, the reaction theorem was employed to calculate the interference without such information. Simulation and measurement results show that the proposed method can effectively estimate the interference to patch antennas with limited information. Furthermore, it avoids direct modeling of the complex structure of patch antennas and reuses the radiating fields of equivalent models which significantly reduces the computation time and memory cost.