Novel matched filtering method and its application in radar system

The method of using a narrowband filter to realize matched filtering is derived.A novel method of using spectrum sampling to realize matched filtering is presented,and the method can conquer the disadvantages that the narrowband filter cannot adopt the adaptive scheduling of phased array radars and realize matched filtering for several waveforms.A novel error extraction method is proposed,which uses a time division multipath method to realize the intermediate frequency extraction.This method can save lots of space for vehicle-born radar systems,reduce the influence of amplitude and phase distortion caused by devices,and enhance the system reliability.Simulation results show that the spectrum sampling method is applicable,and the implementation of frequency spectrum sampling is elaborated.

Gravitational wave search by time-scale-recursive denoising and matched filtering

In our previous work [Physical Review D,2024,109(4):043009],we introduced MSNRnet,a framework integrating deep learning and matched filtering methods for gravitational wave(GW) detection.Compared with end-to-end classification methods,MSNRnet is physically interpretable.Multiple denoising models and astrophysical discrimination models corresponding to different parameter space were operated independently for the template prediction and selection.But the MSNRnet has a lot of computational redundancy.In this study,we propose a new framework for template prediction,which significantly improves our previous method.The new framework consists of the recursive application of denoising models and waveform classification models,which solve the problem of computational redundancy.The waveform classification network categorizes the denoised output based on the signal's time scale.To enhance the denoising performance for long-time-scale data,we upgrade the denoising model by incorporating Transformer and ResNet modules.Furthermore,we introduce a novel training approach that allows for the simultaneous training of the denoising network and waveform classification network,eliminating the need for manual annotation of the waveform dataset required in our previous method.Real-data analysis results demonstrate that our new method decreases the false alarm rate by approximately 25%,boosts the detection rate by roughly 5%,and slashes the computational cost by around 90%.The new method holds potential for future application in online GW data processing.

An extended processing scheme for coherent integration and parameter estimation based on matched filtering in passive radar

In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function(CAF) and the method based on matched filtering are the most common approaches. The method based on matched filtering is an approximation to CAF and the procedure is:(1) divide the signal into snapshots;(2) perform matched filtering on each snapshot;(3) perform fast Fourier transform(FFT) across the snapshots. The matched filtering method is computationally affordable and can offer savings of an order of 1000 times in execution speed over that of CAF. However, matched filtering suffers from severe energy loss for high speed targets. In this paper we concentrate mainly on the matched filtering method and we use keystone transform to rectify range migration. Several factors affecting the performance of coherent integration are discussed based on the matched filtering method and keystone transform. Modified methods are introduced to improve the performance by analyzing the impacts of mismatching, precision of the keystone transform, and discretization. The modified discrete chirp Fourier transform(MDCFT) is adopted to rectify the Doppler expansion in a multi-target scenario. A novel velocity estimation method is proposed, and an extended processing scheme presented. Simulations show that the proposed algorithms improve the performance of matched filtering for high speed targets.

A Novel Matched Filter Structure with Neuron MOS Devices

Based on the principle of the neuron MOS device,a novel matched filter structure which is easily realized by neuron MOS is presented and the details of circuit performance is analyzed.Compared to the conventional structure,the number of circuit elements is decreased greatly for the same function.The test chip is fabricated in 0.35μm process,and the measured result shows that the system structure is feasible and effective.

A high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi earthquake sequence, Yunnan, China: Application of AI picker and matched filter

We present a high-resolution seismic catalog for the 2021 M_(S)6.4/M_(W)6.1 Yangbi sequence.The catalog has a time range of 2021-05-01 to 2021-05-28,and contains~8,000 well located events.It captures the features of the whole foreshock sequence and the early aftershocks.We designed a detection strategy incorporating both an artificial intelligent(AI)picker and a matched filter algorithm.Here,we adopt a hybrid AI method incorporating convolutional and recurrent neural network(CNN&RNN)for event detection and phase picking respectively(i.e.CERP),a light-weight AI picker that can be trained with small volume of data.CERP is first trained with detections from a STA/LTA and Kurtosis-based method called PAL,and then construct a rather complete template set of~4,000 events.Finally,the matched filter algorithm MESS augments the initial detections and measures differential travel times with cross-correlation,which finally results in precise relocation.This process gives 9,026 detections,among which 7,943 events can be well relocated.The catalog shows as expected power-law distribution of frequency magnitude and reveals detailed pattern of seismicity evolution.The main features are:(1)the foreshock sequence images simple fault geometry with consistent strike,but also show a variable event depth along strike;(2)the mainshock ruptures the same fault of the foreshock sequence and activate conjugate faults further to the southeast;(3)complex seismicity are developed in the post-seismic period,indicating complex triggering mechanisms.Thus,our catalog provides a reliable basis for further investigations,such as b-value studies,rupture process,and triggering relations.

DCT domain filtering method for multi-antenna code acquisition

For global navigation satellite system （GNSS） signals in Gaussian and Rayleigh fading channel, a novel signal detection algorithm is proposed. Under the low frequency uncertainty case, after performing discrete cosine transform （DCT） to the outputs of the partial matched filter （PMF） for every antenna, the high order com- ponents in the transforming domain will be filtered, then the equalgain （EG） combination for the inverse discrete cosine transform （IDCT） reconstructed signal would be done subsequently. Thus, due to the different frequency distribution characteristics between the noise and signals, after EG combination, the energy of signals has almost no loss and the noise energy is greatly reduced. The theoretical analysis and simulation results show that the detection algorithm can effectively improve the signal-to-noise ratio of the captured signal and increase the probability of detection under the same false alarm probability. In addition, it should be pointed out that this method can also be applied to Rayleigh fading channels with moving antenna.

Matched Filter Based Spectrum Sensing When Primary User Has Multiple Power Levels

In this paper,we investigate the matched filter based spectrum sensing in a more reasonable cognitive radio(CR) scenario when the primary user(PU) has more than one transmit power levels,as regulated in most standards,i.e.,IEEE 802.11 Series,GSM,LTE,LTE-A,etc.This new multiple primary transmit power(MPTP) scenario is specialized by two different targets:detecting the presence of PU and identifying the power level.Compared to the traditional binary sensing where only the presence of PU is checked,SU may attain more information about the primary network(making CR more "intelligent") and design the subsequent optimization strategy.The key technology is the multiple hypothesis testing as opposed to the traditional binary hypothesis testing.We discuss two situations under whether the channel phase is known or not,and we derive the closed form solutions for decision regions and several performance metrics,from which some interesting phenomenons are observed and the related discussions are presented.Numerical examples are provided to corroborate the proposed studies.

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

We present a detailed catalog of 13671 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 M_(W) 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 hydrologicallydriven 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.

Algorithm of far-field centre estimation based on phase-only matched filter

Estimation of the far-field centre is carried out in beam auto-alignment. In this paper, the features of the far-field of a square beam are presented. Based on these features, a phase-only matched filter is designed, and the algorithm of centre estimation is developed. Using the simulated images with different kinds of noise and the 40 test images that are taken in sequence, the accuracy of this algorithm is estimated. Results show that the error is no more than one pixel for simulated noise images with a 99% probability, and the stability is restricted within one pixel for test images. Using the improved algorithm, the consumed time is reduced to 0.049 s.

Enhanced spectrum sensing using a combination of energy detector,matched filter and cyclic prefix

In this paper,we propose a sensing scheme based on energy detection,matched filter and cyclic prefix.Both Equal Gain Combining(EGC)and optimal combination of the aforementioned detectors are investigated in cooperative and non-cooperative spectrum sensing scenarios.In packet transmission systems such as OFDM(Orthogonal Frequency Division Multiple access)systems,the proposed scheme takes advantage of utilizing more samples than individual detectors,i.e.,cyclic prefix,training or pilot samples,and data payload samples.The proposed combine-sensing scheme offers higher detection probability and lower false alarm probability,as compared with the performance of individual detectors over the same frame duration.Simulation results are congruent with the theoretical curves and confirm the validity of our derivations.

Probability density analysis of SINR in massive MIMO systems with matched filter beamformer

This paper derives an approximate formula for probability density function(PDF) of received signal-to-interference-and-noise ratio(SINR) at user terminal when matched filter(MF) is adopted at a base station(BS).This distribution of SINR can be used to make an analysis of average sum-rate,outage probability,and symbol error rate of massive MIMO downlink with MF at BS.From simulation,it is found that the derived approximate analytical expression for PDF of SINR is consistent with the simulated exact PDF from the definition of SINR in medium-scale and large-scale MIMO systems.

Design of New SAWDQPSK Matched Filter

A new surface acoustic Wave differential quadraphase shift key（SAW DQPSK） spread spectrum （SS） signal matched filter based on the fusion of SS and differential modulation is reported. The design of multi-phase coded SAW matched filter is proposed rather than another design of SAW DQPSK filter, which can cut in a half of the delay time of SAW DQPSK matched filter and SAW fixed delay line（FDL） used for differential demodulation. This breakthrough is made the system largely reduce a size and process much easily. This method can also be feasible in other SAW MPSK matched filter design especially when the modulation phase number is larger than 4. The design example and its experimental results are given.

Windowed overlapped frequency domain block filtering approach for direct sequence signal acquisition

This study applies a windowed frequency domain overlapped block filtering approach to acquire direct sequence signals. As a novel viewpoint, the windows not only allow pulse shaping without front-end pulse-shaping filter, but also increase the performance of the spectrum sensing unit, which can efficiently be implemented into this frequency domain receiver and may further be used for spectrum sensing in cognitive radios or narrowband interference cancellation in military radios. The proposed receiver is applicable for the initial time synchroni- zation of different signals containing a preamble. These signals include single carrier, constant envelope single carder, multicarrier, and even generalized multicarrier signals, making the proposed receiver structure a universal unit. Furthermore, the receiver can be used to perform filtering with long codes and compute the sliding correlation of an unknown periodic preamble. The receiver can further be modified to handle large Doppler shifts. We will also demonstrate herein the computational complexity and analysis of the acquisition performance in Rayleigh and Rician fading channels.

Data Analysis Methods and Signal Processing Techniques in Gravitational Wave Detection

Gravitational wave detection is one of the most cutting-edge research areas in modern physics, with its success relying on advanced data analysis and signal processing techniques. This study provides a comprehensive review of data analysis methods and signal processing techniques in gravitational wave detection. The research begins by introducing the characteristics of gravitational wave signals and the challenges faced in their detection, such as extremely low signal-to-noise ratios and complex noise backgrounds. It then systematically analyzes the application of time-frequency analysis methods in extracting transient gravitational wave signals, including wavelet transforms and Hilbert-Huang transforms. The study focuses on discussing the crucial role of matched filtering techniques in improving signal detection sensitivity and explores strategies for template bank optimization. Additionally, the research evaluates the potential of machine learning algorithms, especially deep learning networks, in rapidly identifying and classifying gravitational wave events. The study also analyzes the application of Bayesian inference methods in parameter estimation and model selection, as well as their advantages in handling uncertainties. However, the research also points out the challenges faced by current technologies, such as dealing with non-Gaussian noise and improving computational efficiency. To address these issues, the study proposes a hybrid analysis framework combining physical models and data-driven methods. Finally, the research looks ahead to the potential applications of quantum computing in future gravitational wave data analysis. This study provides a comprehensive theoretical foundation for the optimization and innovation of gravitational wave data analysis methods, contributing to the advancement of gravitational wave astronomy.

Interpolation of near offset using surface-related multiples

In this research, we present a seismic trace interpolation method which uses seismic data with surface-related multiples. It is different from conventional seismic data interpolation using information transformation or extrapolation of adjacent channels for reconstruction of missing seismic data. In this method there are two steps, first, we construct pseudo-primaries by cross-correlation of surface multiple data to extract the missing near- offset information in multiples, which are not displayed in the acquired seismic record. Second, we correct the pseudo-primaries by applying a Least-squares Matching Filter （LMF） and RMS amplitude correction method in time and space sliding windows. Then the corrected pseudo-primaries can be used to fill the data gaps. The method is easy to implement, without the need to separate multiples and primaries. It extracts the seismic information contained by multiples for filling missing traces. The method is suitable for seismic data with surfacerelated multiples.

L1 norm optimal solution match processing in the wavelet domain

Greater attention has been paid to vintage-merge processing of seismic data and extracting more valuable information by the geophysicist. A match filter is used within many important areas such as splicing seismic data, matching seismic data with different ages and sources, 4-D seismic monitoring, and so on. The traditional match filtering method is subject to many restrictions and is usually difficult to overcome the impact of noise. Based on the traditional match filter, we propose the wavelet domain L1 norm optimal matching filter. In this paper, two different types of seismic data are decomposed to the wavelet domain, different detailed effective information is extracted for Ll-norm optimal matching, and ideal results are achieved. Based on the model test, we find that the L1 norm optimal matching filter attenuates the noise and the waveform, amplitude, and phase coherence of result signals are better than the conventional method. The field data test shows that, with our method, the seismic events in the filter results have better continuity which achieves the high precision seismic match requirements.

Simultaneous denoising and resolution enhancement of seismic data based on elastic convolution dictionary learning

Enhancing seismic resolution is a key component in seismic data processing, which plays a valuable role in raising the prospecting accuracy of oil reservoirs. However, in noisy situations, existing resolution enhancement methods are difficult to yield satisfactory processing outcomes for reservoir characterization. To solve this problem, we develop a new approach for simultaneous denoising and resolution enhancement of seismic data based on convolution dictionary learning. First, an elastic convolution dictionary learning algorithm is presented to efficiently learn a convolution dictionary with stronger representation capability from the noisy data to be processed. Specifically, the algorithm introduces the elastic L1/2 norm as a sparsity constraint and employs a steepest gradient descent strategy to efficiently solve the frequency-domain linear system with substantial computational cost in a half-quadratic splitting framework. Then, based on the learned convolution dictionary, a weighted convolutional sparse representation paradigm is designed to encode the noisy data to acquire an optimal sparse approximation of the effective signal. Subsequently, a high-resolution dictionary with a broadband spectrum is constructed by the proposed parameter scaling strategy and matched filtering technique on the basis of atomic spectrum modeling. Finally, the optimal sparse approximation of the effective signal and the constructed high-resolution dictionary are used for data reconstruction to obtain the seismic signal with high resolution and high signal-to-noise ratio. Synthetic and field dataset examples are executed to check the effectiveness and reliability of the developed method. The results indicate that this method has a more competitive performance in seismic applications compared with the conventional deconvolution and spectral whitening methods.

Application of Newtonian approximate model to LIGO gravitational wave data processing

With the observation of a series of ground-based laser interferometer gravitational wave(GW)detectors such as LIGO and Virgo,nearly 100 GW events have been detected successively.At present,all detected GW events are generated by the mergers of compact binary systems and are identified through the data processing of matched filtering.Based on matched filtering,we use the GW waveform of the Newtonian approximate(NA)model constructed by linearized theory to match the events detected by LIGO and injections to determine the coalescence time and utilize the frequency curve for data fitting to estimate the parameters of the chirp masses of binary black holes(BBHs).The average chirp mass of our results is 22.05_(-6.31)^(+6.31)M_(⊙),which is very close to 23.80_(-3.52)^(+4.83)M_(⊙)provided by GWOSC.In the process,we can analyze LIGO GW events and estimate the chirp masses of the BBHs.This work presents the feasibility and accuracy of the low-order approximate model and data fitting in the application of GW data processing.It is beneficial for further data processing and has certain research value for the preliminary application of GW data.

Walsh Threshold Matched-Filtering Based Anti-collision for Surface Acoustic Wave Tags

Surface acoustic wave(SAW) tags are truly passive devices and do not contain any intelligence,so the question of multiple read in the reading range comes up.The paper suggests a Walsh matched-filtering method in Walsh field to distinguish the collision tags with a threshold.In advance,the code states with special sequency are selected from large possible states and the sequency variables of these tags are saved in a database.When a few tags are read simultaneously in read range,the received signal is filtered by the known sequency of single tag in database,and then these tags can be distinguished by the filtered result.Proper threshold selection improves operation speed.Experiment proves that this method is useful and reliable.

Study on Azimuth Variation of Airgun Signal Propagation

Based on the data recorded by the observation network during the intensive excitation period from November to December 2015 at Binchuan Earthquake Signal Transmitting Seismic Station(BESTSS)in Yunnan Province,the noise in waveform recording is removed by S-transform template filtering method,and the azimuth of airgun signal propagation is calculated and analyzed from the horizontal waveform recordings.The results show that:①the azimuth angle of airgun signal after propagation is sensitive to stress change,and can clearly reflect the diurnal variation of tidal stress,which can be used to monitor the change of stress state in crustal medium;②the azimuth angle of airgun signal in some stations has changed abruptly after propagation on December 4,which may be related to the change of airgun source;③five-shot superposition or fivepoint smoothing of azimuth angle of single shot are carried out for airgun signals in stations far away from epicenter,and results show that azimuth angle from superposition or smoothing is more stable and has clear diurnal variation characteristics after propagation.