Forward Consecutive Mean Excision Research Articles

Radio Frequency Interference Mitigation in Synthetic Aperture Radar Data Based on Instantaneous Spectrum Forward Consecutive Mean Excision

Radio frequency interference (RFI) poses major threats to synthetic aperture radar (SAR) systems. Due to the suppression of useful target signals via high-power RFI, the SAR imaging quality is severely degraded. Nevertheless, existing studies on RFI mitigation mainly focus on narrowband filtering, while wideband RFI mitigation methods are relatively lacking and perform non-robustly. In this paper, an RFI mitigation scheme is proposed based on instantaneous spectrum forward consecutive mean excision (FCME), which is suitable for both narrowband and wideband RFI mitigation. The SAR echo signal is first transformed into a time–frequency (TF) domain through a short-time Fourier transform (STFT). On this basis, the instantaneous spectra polluted via RFI are detected via a kurtosis-based statistical test and then filtered via FCME to achieve RFI mitigation. Finally, connected component analysis is applied as a safety measure so as to avoid the unnecessary loss of useful target signal. The combination of FCME and connected component analysis enables the proposed method to thoroughly filter out RFI while retaining more useful target signals compared with other competing methods. The experimental results on real SAR raw data validate the effectiveness of the proposed method.

High-Efficiency FCME-Based Noise Power Estimation for Long-Term and Wide-Band Spectrum Measurements

Statistics in terms of spectrum occupancy are useful for efficient and smart dynamic spectrum sharing, and the statistics can be obtained by long-term and wide-band spectrum measurements. In this paper, we investigate noise floor (NF) estimation for energy detection (ED)-based long-term and wideband spectrum measurements since the NF estimation heavily affects the ED performance and eventually the accuracy of the statistics in terms of spectrum occupancy. Specifically, we address the following NF estimation problems simultaneously for the first time in the spectrum measurement field: (1) slow timevarying property of the NF, (2) frequency dependency of the NF, (3) the NF estimation in the presence of the signal, and (4) the computational cost of the NF estimation. Firstly, we apply Forward consecutive mean excision (FCME) algorithm-based NF estimation to deal with the above three problems ((1), (2) and (3)) successfully. Second, we propose and apply an NF level change detection on top of the FCME algorithm-based NF estimation to deal with the fourth problem. The proposed NF level change detection exploits the slow time-varying property of the NF. Specifically, only if the significant NF level change is detected, the FCME algorithm-based NF estimation is performed to reduce the redundant NF estimations. In numerical evaluations, we show the efficiency and the validity of the NF level change detection for the NF estimation problems, and compare the NF estimation performance with the method without the NF level change detection.

Adaptive threshold techniques for cognitive radio‐based low power wide area network

AbstractSome low power wide area network (LPWAN) developers such as Sigfox, Weightless, and Nwave, have recently commenced the integration of cognitive radio (CR) techniques in their respective LPWAN technologies, generally termed CR‐LPWAN systems. Their objective is to overcome specific limitations associated with LPWANs such as spectra congestion and interference, which in turn will improve the performance of many Internet of Things (IoT)‐based applications. However, in order to be effective under dynamic sensing conditions, CR‐LPWAN systems are typically required to adopt adaptive threshold techniques (ATTs) in order to improve their sensing performance. Consequently, in this article, we have investigated some of these notable ATTs to determine their suitability for CR‐LPWAN systems. To accomplish this goal, first, we describe a network architecture and physical layer model suitable for the effective integration of CR in LPWAN. Then, some specific ATTs were investigated following this model based on an experimental setup constructed using the B200 Universal Software Radio Peripheral kit. Several tests were conducted, and our findings suggest that no single ATT was able to perform best under all sensing conditions. Thus, CR‐LPWAN developers may be required to select a suitable ATT only based on the specific condition(s) for which the IoT application is designed. Nevertheless, some ATTs such as the forward consecutive mean excision algorithm, the histogram partitioning algorithm and the nonparametric amplitude quantization method achieved noteworthy performances under a broad range of tested conditions. Our findings will be beneficial to developers who may be interested in deploying effective ATTs for CR‐LPWAN systems.

A cuckoo search optimization-based forward consecutive mean excision model for threshold adaptation in cognitive radio

The forward consecutive mean excision (FCME) algorithm is one of the most effective adaptive threshold estimation algorithms presently deployed for threshold adaptation in cognitive radio (CR) systems. However, its effectiveness is often limited by the manual parameter tuning process and by the lack of prior knowledge pertaining to the actual noise distribution considered during the parameter modeling process of the algorithm. In this paper, we propose a new model that can automatically and accurately tune the parameters of the FCME algorithm based on a novel integration with the cuckoo search optimization (CSO) algorithm. Our model uses the between-class variance function of the Otsu’s algorithm as the objective function in the CSO algorithm in order to auto-tune the parameters of the FCME algorithm. We compared and selected the CSO algorithm based on its relatively better timing and accuracy performance compared to some other notable metaheuristics such as the particle swarm optimization, artificial bee colony (ABC), genetic algorithm, and the differential evolution (DE) algorithms. Following close performance values, our findings suggest that both the DE and ABC algorithms can be adopted as favorable substitutes for the CSO algorithm in our model. Further simulation results show that our model achieves reasonably lower probability of false alarm and higher probability of detection as compared to the baseline FCME algorithm under different noise-only and signal-plus-noise conditions. In addition, we compared our model with some other known autonomous methods with results demonstrating improved performance. Thus, based on our new model, users are relieved from the cumbersome process involved in manually tuning the parameters of the FCME algorithm; instead, this can be done accurately and automatically for the user by our model. Essentially, our model presents a fully blind signal detection system for use in CR and a generic platform deployable to convert other parameterized adaptive threshold algorithms into fully autonomous algorithms.

Probability of false alarm based interference suppression methods in Internet of Things (IoT) systems

There is going to be significant growth of connected devices, such as smart phones, tablets, and sensors. It has been predicted that by 2020, there are more than 50 billion connected devices in the world. Most of common home IoT devices require little interaction and produce minimal data. Or some healthcare or factory automation sensors need or want to measure and send data to and from the Internet regularly. These are not massive consumers of bandwidth. These can be called narrowband (NB) or ultra-narrowband (UNB) signals. Even if this saves both spectrum resources and power consumption, its drawback is that in the consequence of the IoT headway, the amount of devices and, thus, the amount of NB signaling as well as radio frequency (RF) noise are significantly growing. Thus, interference suppression (IS) is indispensable. In this paper, three probability of false alarm (PFA) based methods, namely Neyman–Pearson (NP) criterion, forward consecutive mean excision (FCME), and localization algorithm based on double-thresholding (LAD) are applied for a wireless NB-IoT network, especially, in the last 100 m region, i.e., from devices to an access point (AP). Besides the traditional fixed threshold approach, an adaptive threshold setting is also proposed to enhance the performances in frequency selective fading channels. The simulation results show that the proposed methods excellently work even in a dense NB-IoT network, where contains a large number of devices.

Enhanced FCME Thresholding for Wavelet-Based Cognitive UWB over Fading Channels

The cognitive ultra-wideband (UWB) network detects interfering narrowband systems and adapts its configuration accordingly. An inherently adaptive and flexible candidate for cognitive UWB transmission is the wavelet packet multicarrier modulation (WPMCM). In this letter, we use an enhanced forward consecutive mean excision thresholding algorithm to tackle the noise uncertainty in the wavelet-based sensing of WPMCM systems, and mathematical analysis is performed for primary user channel fading. As a benchmark, we compare the proposed system with a conventional fast Fourier transformation-based system, and performance investigation proves significant improvements when primary and secondary links are subjected to multipath fading and noise.

Narrowband signal localization based on enhanced LAD method

In this paper, an enhanced localization algorithm based on double thresholds (LAD) is proposed for localizing narrow band signals in the frequency domain. A simplified LAD method is first studied to reduce the computational complexity of the original LAD method without performance loss. The upper and lower thresholds of the simplified LAD method are directly calculated by running the forward consecutive mean excision algorithm only once. By combining the simplified LAD method and binary morphological operators, the enhanced LAD method is then proposed and its performance is simply discussed. The simulation results verify the correctness of discussion and show that the enhanced LAD method is superior to the LAD with adjacent cluster combining method, especially at low signal-to-noise ratio.

Analysis of the Consecutive Mean Excision Algorithms

The backward and forward consecutive mean excision (CME/FCME) algorithms are diagnostic methods for outlier (signal) detection. Since they are computationally simple, they have applications for both narrowband signal detection in cognitive radios and interference suppression. In this paper, a theoretical performance analysis framework of the CME algorithms is presented. The analysis provides simple tests of the detectability of the signals based on their shape in the considered domain (e.g., spectrum). As a consequence, results can be used to quickly check whether the CME/FCME algorithms are usable for a given problem or not without the need to resort to time consuming computer simulations. The computer simulations for random and orthogonal frequency division multiplexing (OFDM) signals show that the presented analysis is able to predict the detectability of signals well.

CFAR Outlier Detection With Forward Methods

Separation or classification of signal-present samples from noise-only samples is studied. The false-alarm probability implies how many noise-only samples are wrongly classified as outliers, and typically it should be smaller than some upper limit. The noise distribution parameters are not known a priori and have to be estimated. Multiple outliers have a strong influence to that estimation and may lead to uncontrollable false-alarm probability. The false-alarm probability control can be improved by robust estimators and/or by forward-detection methods. In this article, the false-alarm probability of the forward methods is analyzed. The forward consecutive mean excision (FCME) algorithm is enhanced to allow better false-alarm control. It is proposed that the forward method using the cell-averaging (CA) constant false-alarm rate (CFAR) technique can be applied for locating the outliers. The results show that its false-alarm probability stays close to the required value even in the presence of multiple outliers.

CFAR strategies for channelized radiometer

The channelized radiometer is a well-known intercept receiver. We analyze its performance when different constant false alarm rate (CFAR) strategies are used to set a detection threshold based on values of some reference cells. The studied conventional methods are cell-averaging (CA) and order statistics. The proposed iterative methods for setting the detection threshold are forward consecutive mean excision (FCME) with the CA scaling factors in (final) detection decision (FCME+CA), backward consecutive mean excision (BCME) with the CA scaling factors in detection (BCME+CA) and a method that uses the CA scaling factors in both censoring and detection (CA+CA). The results show that iterative CFAR methods can improve detection performance compared to the baseline methods.