Matched Filter Detector Research Articles

Spectrum Sensing Using Cooperative Matched Filter Detector in Cognitive Radio

The vast rise in the number of internet-connected devices necessitates a more accessible spectrum. As a result, Cognitive Radio was already proposed as a solution to the problem of restricted spectrum resources by utilizing available spectrum which is assigned to primary users. This method allows the secondary user to utilize the spectrum whenever the primary user is not using it, and it does so without intruding with the primary user. Whenever the secondary user detects the spectrum, it faces many issues, such as complexity in sensing, leading to a lack of noise value, and the primary user is hidden to all secondary users. In order to tackle these challenges, many spectrum sensing frameworks were introduced in the literature. In this paper, an adaptive threshold matched filter detector and a cooperative matched filter detector frameworks are utilized to detect the spectrum and resolve the issues above. The probability of detection (Pd), probability of miss detection (Pm), and probability of false alarm (Pf) are the metrics used to assess sensing accuracy. To simulate suggested detectors results and proficiency, the MATLAB R2020a software was utilized. In comparison to earlier studies, the simulation conclusions reveal that the detection process starts with lower SNR values compared to previous work.

ED-SS based Cognitive Radio (CR) over Various Fading Channels for Modern Wireless Communications

Cognitive Radio (CR) has already been proved to provide the better solution for the problem of spectrum insufficiency in which every wireless system occupies the limited bandwidth spectrum (LBS) allotting to the licensed users. Sometimes, this LBS is empty in a certain place at a certain time that is called as spectrum holes estimated by spectrum sensing (SS). The spectrum sensing is the main technique used in cognitive radio and various spectrum sensing techniques are available in the literature such as: energy detection (ED), matched filter detection (MFD) and cyclo-stationary feature detection (CFD). In the present work, energy detection-spectrum sensing (ED-SS) based cognitive radio system has been implemented which can analyze the energy of the spectrum. The various fading channels such as additive white gaussian noise (AWGN), Rayleigh, and Nakagami have been incorporated in the present work over which the performance of cognitive radio has been analyzed and compared using the ED-SS technique. The experimental results show thatt the detection of the presence of primary user signal using proposed ED-SS technique is easier with low computational complexities in Cognitive Radio (CR) networks. The present work also demonstrates when the performance of proposed ED-SS technique over aforesaid three fading channels are compared, Nakagami-m fading channels exhibits superior improvement in Pd compared to Rayleigh and AWGN fading channels

GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders

Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name ‘Gravitational Wave Anomalous Knowledge’ (GWAK). While the semi-supervised approach to this problem entails a potential reduction in accuracy compared to fully supervised methods, it offers a generalizability advantage by enhancing the reach of experimental sensitivity beyond the constraints of pre-defined signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources.

Clutter Covariance Matrix Estimation for Radar Adaptive Detection Based on a Complex-Valued Convolutional Neural Network

In this paper, we address the problem of covariance matrix estimation for radar adaptive detection under non-Gaussian clutter. Traditional model-based estimators may suffer from performance loss due to the mismatch between real data and assumed models. Therefore, we resort to a data-driven deep-learning method and propose a covariance matrix estimation method based on a complex-valued convolutional neural network (CV-CNN). Moreover, a real-valued (RV) network with the same framework as the proposed CV network is also constructed to serve as a natural competitor. The obtained clutter covariance matrix estimation based on the network is applied to the adaptive normalized matched filter (ANMF) detector for performance assessment. The detection results via both simulated and real sea clutter illustrate that the estimator based on CV-CNN outperforms other traditional model-based estimators as well as its RV competitor in terms of probability of detection (PD).

Preliminary Results from a Dense Short-Period Seismic Deployment around the Source Zone of the 1886 M 7 South Carolina Earthquake

Abstract The 1886 magnitude ∼7 Summerville, South Carolina, earthquake was the largest recorded on the east coast of the United States. A better understanding of this earthquake would allow for an improved evaluation of the intraplate seismic hazard in this region. However, its source fault structure remains unclear. Starting in May 2021, a temporary 19-station short-period seismic network was deployed in the Summerville region. Here, we present our scientific motivation, station geometry, and quality of the recorded seismic data. We also show preliminary results of microearthquake detections and relocations using recordings from both our temporary and four permanent stations in the region. Starting with 52 template events, including two magnitude ∼3 events on 27 September 2021, we perform a matched filter detection with the one year of continuous data, resulting in a catalog of 181 total events. We then determine precise relative locations of a portion of these events using differential travel-time relocation methods, and compare the results with relocation results of 269 events from a previous seismic deployment in 2011–2012. We also determine focal mechanism solutions for three events from 27 September 2021 with magnitudes 2.0, 3.1, and 3.3, and infer their fault planes. Our relocation results show a south-striking west-dipping zone in the southern seismicity cluster, which is consistent with the thrust focal mechanism of the magnitude 3.3 earthquake on 27 September 2021 and results from the previous study based on the temporary deployment in 2011–2012. In comparison, the magnitudes 3.1 and 2.0 events likely occur on a north–south-striking right-lateral strike-slip fault further north, indicating complex patterns of stress and faulting styles in the region.

Enhancement of spectral access of optical OFDM system using cognitive radio

Abstract This work presents the research on spectrum sensing in optical OFDM (orthogonal frequency division multiplexing) systems. Spectrum sensing is crucial for cognitive radio networks to efficiently utilize available spectral resources. Various spectrum sensing techniques are explored, including energy detection (ED), cyclostationary feature detection (CD), and matched filter detection (MF). These techniques enable the detection of occupied and unoccupied subcarriers in optical OFDM systems, facilitating dynamic spectrum access and spectrum sharing. The research highlights the importance of accurate spectrum sensing in maximizing spectral efficiency, optimizing network performance, and enabling coexistence of multiple users in optical OFDM-based cognitive radio networks. The findings contribute to the development of future wireless communication systems. The parameters such as probability of detection (pd), probability of false alarm (pfa), bit error rate (BER), and power spectral density (PSD) is analysed using computer simulation. The simulation results reveal that the MF achieved a gain of 2.8 dB and 3.6 dB as compared with conventional spectrum sensing algorithms.

Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promising approach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio (SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambient Radio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum for the transmission of data without loss or without collision at a specific time. In this paper, the authors proposed a novel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the AmBC. Novel Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inverse covariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBC to detect the presence of users at low power levels. The performance of the three detection techniques is measured using the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of Missed Detection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significant improvement via the HFDI technique for all the parameters.

Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter

This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter. Conven-tional target detection algorithms assume Gaussian-distributed clutter, but their performance is significantly degraded in the presence of correlated heavy-tailed distributed clutter. Derivation of optimal detection algorithms with heavy-tailed distributed clutter is analytically intractable. Furthermore, the clutter dis-tribution is frequently unknown. This work proposes a deep learning-based approach for multiple target detection in the range-Doppler domain. The proposed approach is based on a unified NN model to process the time-domain radar signal for a variety of signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions, simplifying the detector architecture and the neural network training procedure. The performance of the proposed approach is evaluated in various experiments using recorded radar echoes, and via simulations, it is shown that the proposed method outperforms the conventional cell-averaging constant false-alarm rate (CA-CFAR), the trimmed-mean CFAR (TM-CFAR), and the adaptive normalized matched-filter (ANMF) detectors in terms of probability of detection in the majority of tested SCNRs and clutter scenarios.

NEAR–OPTIMUM DETECTION OF SIGNALS IN UNDERWATER ACOUSTIC NOISE USING LOCALLY OPTIMAL DETECTOR IN TIGERS RIVER

Signal detection has been considered important in under-water signal processing and digital communications, and depending upon noise statistics’ knowledge, near-optimum signal detections in the under-water acoustic noises (UWANs) may be realized in a more effective manner. The theory of the normal (i.e. Gaussian) noise permits using matched filter (MF) detectors; for that reason, a locally optimal (LO) detector has been designed in the present work for improving the probability of the detection ( based on knowing the probability density function (PDF) of noise. Under-water noise that has been utilized for the validation represents the real data that had been gathered from the sea with the use of the broad-band hydrophones at Abo Dali district -Kazem Al Ali Village-Tigris Beaches-Baghdad-Iraq. The LO detector performance is compared after that to the conventional matched filter detector and those have been assessed based on their values. For time-varying signals, the probability of false alarms has been identified as 0.010, and of 90%, energy-to-noise ratios (ENRs) of LO are more efficient compared to the ones of Matched Filter by 4.1dB and for the signals with a fixed frequency, LO is more efficient compared to matched filter by 4.7dB.

A mathematical investigation of multi-carrier waveform with AI based matched filter detection

Research found that 70% of the spectrum is not being used. Energy, feature detection, and matched filters are just a few of the techniques that have been suggested in the past. A matching filter is used in this study to determine whether a spectrum is free or already in use by the primary user. In this case, if the licensed spectrum for a primary user is not needed, a secondary user may access it. The simulation is carried out by the MATLAB simulation software using the output signals from the OFDM model (proposed model) and NOMA model as inputs. Later in this study, a number of metrics are examined and contrasted with current technology, including probability of detection (PD) and probability of false alarm (PFA).

Sustainable Non-Cooperative User Detection Techniques in 5G Communications for Smart City Users

The 4G network is not sufficient for achieving the high data requirements of smart city users. The 5G network intends to meet these requirements and overcome other application issues, such as fast data transmission, video buffering, and coverage issues, providing excellent mobile data services to smart city users. To allocate a channel or spectrum to a smart city user for error-free transmission with low latency, the accurate information of the spectrum should be detected. In this study, we determined the range of non-cooperative detection techniques, such as matched filter detection with inverse covariance approach (MFDI), cyclostationary feature detection with inverse covariance approach (CFDI), and hybrid filter detection with inverse covariance approach (HFDI); based on the results of these methods, we provided highly accurate spectrum information for smart city users, enabling sustainable development. To evaluate the performance of the proposed detection techniques, the following parameters are used: probability of detection (PD), probability of false alarms (Pfa), probability of miss detection (Pmd), sensing time, and throughput. The simulation results revealed that the HFDI detection method provided sustainable results at low signal-to-noise ratio ranges and improved channel detection and throughput of approximately 17% and 10%, respectively.

A 15-year-Long catalog of seismicity in the Eastern Tennessee Seismic Zone (ETSZ) using matched filter detection

We present a detailed catalog of 13 671 earthquakes in the Eastern Tennessee Seismic Zone (ETSZ) that spans January 1, 2005 to July 31, 2020. We apply a matched filter detection technique on over 15 years of continuous data, resulting in arguably the most complete catalog of seismicity in the ETSZ yet. The magnitudes of newly detected events are determined by computing the amplitude ratio between the detections and templates using a principal component fit. We also compute the b-value for the new catalog and comparatively relocate a subset of newly detected events using XCORLOC and hypoDD, which shows a more defined structure at depth. We find the greatest concentration along and to the east of the New York-Alabama Lineament, as defined by the magnetic anomaly, supporting the argument that this feature likely is related to the generation of seismicity in the ETSZ. We examine seismicity in the vicinity of the Watts Bar Reservoir, which is located about 5 ​km from the epicenter of the MW 4.4 December 12, 2018 Decatur, Tennessee earthquake, and find possible evidence for reservoir modulated seismicity in this region. We also examine seismicity in the entire ETSZ to search for a correlation between shallow earthquakes and seasonal hydrologic changes. Our results show limited evidence for hydrologically-driven shallow seismicity due to seasonal groundwater levels in the ETSZ, which contradicts previous studies hypothesizing that most intraplate earthquakes are associated with the dynamics of hydrologic cycles.

A High-Resolution Earthquake Catalog for the 2004 Mw 6 Parkfield Earthquake Sequence Using a Matched Filter Technique

Abstract We present the high-resolution Parkfield matched filter relocated earthquake (PKD-MR) catalog for the 2004 Mw 6 Parkfield earthquake sequence in central California. We use high-quality seismic data recorded by the borehole High Resolution Seismic Network combined with matched filter detection and relocations from cross-correlation derived differential travel times. We determine the magnitudes of newly detected events by computing the amplitude ratio between the detections and templates using a principal component fit. The relocated catalog spans from 6 November 2003 to 28 March 2005 and contains 13,914 earthquakes, which is about three times the number of events listed in the Northern California Seismic Network catalog. Our results on the seismicity rate changes before the 2004 mainshock do not show clear precursory signals, although we find an increase in the seismic activity in the creeping section of the San Andreas fault (SAF) (about ∼30 km northwest of the mainshock epicenter) in the weeks prior to the mainshock. We also observe a decrease in the b-value parameter in the Gutenberg–Richter relationship in the creeping section in the weeks prior to the mainshock. Our results suggest stress is increasingly released seismically in the creeping section, accompanied by a decreasing aseismic creeping rate before the mainshock occurrence. However, b-value and seismicity rates remain stable in the Parkfield section where the 2004 mainshock ruptured. This updated catalog can be used to study the evolution of aftershocks and their relations to afterslip following the 2004 Parkfield mainshock, seismicity before the mainshock, and how external stresses interact with the Parkfield section of the SAF and the 2004 sequence.

A Fast Data Transmission Method of Vehicle-Road Cooperative Communication Based on the Clustering Algorithm.

In order to improve the security and stability of data transmission in vehicle-road cooperative communication and reduce the error rate of data transmission, a fast data transmission method based on the clustering algorithm is proposed. First, the multisensor information acquisition method of the vehicle networking is used to realize data acquisition and data structure analysis of the vehicle-road cooperative communication, and a data transmission channel model for vehicle-road cooperative communication is constructed. The interference suppression in the data transmission process of the vehicle-road cooperative communication is realized by the matched filter detection algorithm. Then, according to the symbol characteristic distribution of the vehicle-road cooperative communication channel, the baud interval equalization method is used to realize the piecewise equalization adjustment of data transmission for the vehicle-road cooperative communication. With the adaptive error compensation and channel fading suppression, the K-means clustering algorithm is used to carry out the coordinated adjustment to data packets during the rapid data transmission of the vehicle-road cooperative communication. Finally, the adaptive control of data transmission for the vehicle-road cooperative communication is carried out according to the dynamic distribution characteristics of the clustering center so as to reduce the influence of channel fading and intersymbol interference caused by channel spread. The simulation results show that the bit error rate of this method is kept at about 0.05, and the data transmission rate continues to increase, most of which remain above 0.95. This method has strong anti-interference ability for the rapid data transmission of vehicle-road cooperative communication, with lower communication bit error rate, less end-to-end time delay, and higher stability and accuracy of data transmission.

Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection

The radio spectrum is one of the world’s most highly regulated and limited natural resources. The number of wireless devices has increased dramatically in recent years, resulting in a scarcity of available radio spectrum due to static spectrum allocation. However, many studies on static allocation show that the licensed spectrum bands are underutilized. Cognitive radio has been considered as a viable solution to the issues of spectrum scarcity and underutilization. Spectrum sensing is an important part in cognitive radio for detecting spectrum holes. To detect the availability or unavailability of primary user signals, many spectrum sensing techniques such as matched filter detection, cyclostationary feature detection, and energy detection have been developed. Energy detection has gained significant attention from researchers because of its ease of implementation, fast sensing time, and low computational complexity. Conventional detectors’ performance degrades rapidly at low SNR due to their sensitivity to the uncertainty of noise. To mitigate noise uncertainty, Shannon, Tsallis, Kapur, and Renyi entropy-based detection has been used in this study, and their performances are compared to choose the best performer. According to the comparison results, the Renyi entropy outperforms other entropy methods. In this study, two-stage spectrum sensing is proposed using energy detection as the coarse stage and Renyi entropy-based detection as the fine stage to improve the performance of single-stage detection techniques. Furthermore, the performance comparison among conventional energy detection, entropy-based detection, and the proposed two-stage techniques over AWGN channel are performed. The parameters such as probability of detection, false alarm probability, miss-detection probability, and receiver operating characteristics curve are used to evaluate the performance of spectrum sensing techniques. It has been shown that the proposed two-stage sensing technique outperforms single-stage energy detection and Renyi entropy-based detection by 11 dB and 1 dB, respectively.

Extraction of Temperature Distributions in Brillouin Optical Time Domain Analysis Sensors Using 2D Wiener Filter Based Matched Filter Detection

In this paper, the use of 2D Wiener filter based matched filter detection (WMFD) is proposed and demonstrated for the extraction of temperature distributions in Brillouin optical time domain analysis (BOTDA) sensors. The experimental Brillouin gain spectra (BGSs) are obtained along a 38.2 km sensing fiber by adopting ten different numbers of BOTDA-trace averaging (NTA). These BGSs are first denoised by applying Wiener filter (WF) to enhance the signal-to-noise ratio (SNR) of the BOTDA-traces. The improvement of trace-SNR for using WF is quantified and analyzed experimentally. The matched filter detection (MFD) which is free from time-consuming iterative optimization procedure is then applied to the denoised BGSs for the ultrafast extraction of temperature distributions along the fiber. The measurement uncertainty, spatial resolution and temperature extraction speed provided by WMFD are also analyzed in detail and compared with that provided by widely-used curve fitting method (CFM). The results show that WF can improve the trace-SNR within the range from ~6.78 dB to ~8.28 dB depending on NTA. Consequently, WMFD can improve the measurement uncertainty within the range from ~48.70% to ~59.41% without sacrificing the spatial resolution as compared to CFM. Moreover, the speed in extracting temperature distributions from the experimental BGSs acquired with different NTA for using WMFD can be improved within the range from ~47.36 times to ~50.69 times as compared to that for using CFM. Thus, the proposed WMFD can be an effective approach for highly accurate and ultrafast extraction of temperature distributions along the sensing fiber. DUJASE Vol. 6 (2) 30-38, 2021 (July)

Matched-Filter Detector With Quadrature Interference Cancellation for Coded Uplink Multiuser MIMO Systems With Massive IoT Devices

Matched-Filter Detector With Quadrature Interference Cancellation for Coded Uplink Multiuser MIMO Systems With Massive IoT Devices

Optimum and Near-Optimum Coherent CFAR Detection of Radar Targets in Compound-Gaussian Clutter With Generalized Inverse Gaussian Texture

In this article, we propose optimum and near-optimum adaptive coherent detectors of radar targets in compound-Gaussian clutter with generalized inverse Gaussian texture (CG-GIG clutter). The target amplitude and the speckle covariance matrix are modeled as unknown quantities to be estimated. On the basis of the two-step generalized likelihood ratio test (GLRT) and the estimate of the speckle covariance matrix, the optimum coherent detector and its adaptive version are designed in this article. It is demonstrated that the proposed optimum coherent detector contains three common detectors, which are the optimum K detector, the generalized likelihood ratio test linear-threshold detector (GLRT-LTD), and the GLRT detector for compound-Gaussian clutter with inverse Gaussian texture. Due to the existence of the modified Bessel function depending on data, the optimum coherent detector is computationally unrealizable. Therefore, a near-optimum coherent detector with a new structure named $\alpha$ matched filter ($\alpha$-MF) detector and its adaptive version are proposed, which decreases the computational cost. The proposed near-optimum coherent detector contains two common detectors, the GLRT-LTD and the $\alpha$-MF detector in K-distributed clutter, and has a comparable detection performance of the near-optimum detector in compound-Gaussian clutter with inverse Gaussian texture, which was proposed before. Theoretical analysis and numerical experiments illustrate that the proposed two detectors for CG-GIG clutter have the constant false alarm ratio property relative to the estimate of the speckle covariance matrix and the Doppler steering vector. Moreover, the detection performance of the two coherent detectors are evaluated by the simulated and real data.

Comprehensive review on spectrum sensing techniques in cognitive radio

In wireless communication, spectrum sensing (SS) is one of the important techniques to study about the radio environment. Spectrum sensing phenomenon is used to estimate the unutilized frequency band of whole radio spectrum. Utilization of available spectrum can be improved by enhancing the spectrum sensing techniques. The performance of available spectrum sensing techniques may degrade substantially in low signal to noise ratio, multipath fading & shadowing environments. This may lead to increase in probability of false alarms and decrease in probability of detection. This paper gives the categorization of different spectrum sensing techniques (SST) and it also explores the different methods used in spectrum sensing. Comparison of energy detector (ED), matched filter detection (MFD), cyclostationary feature detection (CFD), waveform detection (WD) and eigen value detection (EVD) based on their methodology and accuracy is also present in this paper. Various issues associated with these spectrum sensing techniques are highlighted here and futuristic solutions proposed for improvement in existing spectrum sensing techniques.

An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multi-path fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-to-interference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.