Adaptive Signal Processing Algorithms Research Articles

Spatio-temporal adaptive algorithm using Hamming code based on 3D-MIMO wireless channel model

Space-time coding is an effective method for constructing communication systems while saving bandwidth, taking into account the shortage of frequency resources and power in fading channels, which is implemented based on the advantages of using adaptive antenna arrays. Currently, WiMAX radio access technology has become widespread; its use is justified in conditions of direct visibility between the base and mobile stations, but in a city with dense buildings and in the presence of organized interference, its use leads to an increase in the likelihood of bit errors. At the same time, the combination of OFDM (orthogonal frequency division multiplexing) and MIMO (spatial coding) technologies based on adaptive antenna arrays significantly reduces the probability of bit errors, and therefore increases the throughput of the communication system. The use of adaptive space-time devices at the outputs of antenna arrays makes it possible to select a signal path with maximum power and with a minimum error in receiving OFDM pilot subcarriers, even in the absence of direct visibility between the base and mobile stations and in conditions of active interference. In addition, the use of a 3D model of the communication channel will allow us to evaluate the effectiveness of the communication system model in real operating conditions. The goal of the work is to solve the problem of additionally increasing noise immunity in communication systems in conditions of numerous reflections caused by dense buildings in urban environments, using a combination of Hamming code, OFDM and an algorithm for adaptive spatio-temporal signal processing in receivers such as SISO (Singl Input – Singl Output), and MIMO (Multiply Input – Multiply Output), built on adaptive antenna arrays. The work calculated the weight coefficients of the adaptation algorithm using the theory of eigenvalues and eigenvectors of the spatial correlation matrix of signals at the outputs of the adaptive antenna arrays. Modeling of algorithms and a 3D model of the communication channel in Matlab using the Hamming code was carried out, as a result, an increase in the efficiency of error correction for the Hamming code was demonstrated, as well as a significant reduction in the probability of code errors and an increase in the performance of the communication system when using an adaptive algorithm. At the same time, the article uses the term “multipath” channel in contrast to the generally accepted, but incorrect “multibeam” channel, since multibeam can be a characteristic of an antenna system. The presented results confirm that when processing signals in a receiving system in a multipath channel with dense urban development, the use of adaptive spatiotemporal algorithms provides increased noise immunity and radio communication capacity, especially for image transmission when using unmanned aerial vehicles.

Adaptive digital antenna array signal processing algorithm based on LDL decomposition

Two algorithms for inverting correlation matrices of digital antenna array signals are compared: LDL decomposition and the fringing method. The idea of comparing algorithms was to take into account the limitations on the length of the input data mantissa in the process of space-time processing using FPGAs. It has been found that rounding errors in the signal processing process in FPGAs can lead to the destruction of the directivity pattern. This is due to the fact that when implementing various algorithms for matrix inversion, the distribution of errors in the channels becomes interconnected. This means that when implementing adaptive antenna arrays on FPGAs, it is necessary to carefully choose the length of the mantissa and the conversion algorithm. In this regard, an algorithm for signal processing in an adaptive antenna array is proposed, its difference from the known ones is that when implemented on FPGAs, floating-point arithmetic of limited mantissa bit is used. When comparing two computationally efficient matrix inversion algorithms (the LDL method and the fringing method), the LDL method has higher stability. It was found that with an increase in the rank of the correlation matrix, the effect of rounding errors will be stronger. In this regard, it is necessary to determine in advance at what ratio of power sources and internal noise it is planned to use an adaptive processor.

Thinned Smart Antenna of a Semi-circular Dipole Array for Massive MIMO Systems

Massive MIMO (multiple-input multiple-output) is a multi-user MIMO technology that can provide high-speed multimedia services in 5G wireless networks using sub-6 GHz and millimeter wave bands. The massive MIMO (MMIMO) installs array antennas in the base stations, using hundreds of transceivers with other RF modules. One of the drawbacks of the MMIMO system is its huge power consumption, and the beamforming network with RF modules for a large number of antennas is the main contributor to the power consumption. In this paper, a novel beamforming method is proposed for the low power consumption of an MMIMO system. The proposed thinned smart antenna (TSA) of a semi-circular array produces a secure beam toward the user’s terminal with reduced interference. By thinning the antenna array, some of the antenna elements are kept off, resulting in less power consumption, while the array pattern remains the same as a fully populated array with a reduced side lobe level (SLL). The sub-6 GHz band of 5 GHz is used for the design of thinned array antennas. The genetic algorithm (GA) is used to determine the array sequence in thinning, and the adaptive signal processing algorithms least mean square (LMS), recursive least square (RLS), and sample matrix inversion (SMI) are used for the beamforming of the TSA, and the corresponding algorithms are GA-LMS, GA-RLS, and GA-SMI. The power saving of 40% to 55% is achieved using TSA. The maximum SLL reductions of 13 dB, 12 dB, and 14 dB are achieved for TSA using GA-LMS, GA-RLS, and GA-SMI algorithms, respectively.

Optical adaptive power control based on atmospheric channel reciprocity for mitigating turbulence disturbances in free-space optics communication.

A continuous time-domain adaptive power model of transmitter optical and control algorithm based on atmospheric turbulence channel reciprocity are explored for mitigating the free-space optical communication (FSOC) receiver optical intensity scintillation and bit error rate (BER) deterioration. First, a transmitter optical adaptive power control (OAPC) system architecture using four wavelength optical signals based on atmospheric turbulence channel reciprocity is proposed, and electronically variable optical attenuator (EVOA) and erbium-doped fiber amplifier (EDFA) are employed as the main OAPC units for power adaptation. Moreover, a reciprocity evaluation model for gamma-gamma (G-G) continuous-time signals is generated using the autoregressive moving average (ARMA) stochastic process, which takes into account the delay time and system noise, and a reciprocity-based OPAC algorithm is proposed. Numerical simulations were also performed to analyze the signal reciprocity characteristics under different turbulence, noise, and sampling time mismatch at both ends, as well as the scintillation index (SI) performance under OAPC system operation. Simultaneously, the time-domain signals of continuous quadrature amplitude modulation -16 (QAM-16) and QAM-32 real states are fused with the gamma-gamma (G-G) reciprocal turbulence continuous signals to analyze the probability density function (PDF) and bit error ratio (BER) performance after OAPC correction. Finally, a 64 Gpbs QAM-16 OPAC communication experiment was successfully executed based on an atmospheric turbulence simulator. It is shown that the OAPC correction is carried out using reciprocity at millisecond sampling delay, the light intensity scintillation of the communication signal can be well suppressed, the signal-to-noise ratio (SNR) is greatly improved, the suppression is more obvious under strong turbulence, the overall BER reduction is greater than 2.8 orders of magnitude with the OAPC system, and this trend becomes more pronounced as the received power increases, even reach 6 orders of magnitude in some places. This work provides real time-domain continuous signal samples for real signal generation of communication signals in real turbulence environments, adaptive coding modulation using reciprocity, channel estimation, and optical wavefront adaptive suppression, which are the basis of advanced adaptive signal processing algorithms.

Leaky Partial Update LMS Algorithms in Application to Structural Active Noise Control.

Adaptive signal processing algorithms play an important role in many practical applications in diverse fields, such as telecommunication, radar, sonar, multimedia, biomedical engineering and noise control. Recently, a group of adaptive filtering algorithms called partial update adaptive algorithms (partial updates) has gathered considerable attention in both research and practical applications. This paper is a study of the application of PUs to very demanding, structural active noise control (ANC) systems, which are of particular interest due to their ability to provide for a global noise reduction. However, such systems are multichannel, with very high computational power requirements, which may be reduced by the application of partial updates. The paper discusses the modifications necessary to apply PUs in structural ANC systems and the potential computational power savings offered by this application. As a result, leaky versions of the PU LMS algorithms are introduced to the general public. The paper also presents two simulation examples, based on real laboratory setups, confirming high performance of the proposed algorithms.

Meta-AF: Meta-Learning for Adaptive Filters

Adaptive filtering algorithms are pervasive throughout signal processing and have had a material impact on a wide variety of domains including audio processing, telecommunications, biomedical sensing, astrophysics and cosmology, seismology, and many more. Adaptive filters typically operate via specialized online, iterative optimization methods such as least-mean squares or recursive least squares and aim to process signals in unknown or nonstationary environments. Such algorithms, however, can be slow and laborious to develop, require domain expertise to create, and necessitate mathematical insight for improvement. In this work, we seek to improve upon hand-derived adaptive filter algorithms and present a comprehensive framework for learning online, adaptive signal processing algorithms or update rules directly from data. To do so, we frame the development of adaptive filters as a meta-learning problem in the context of deep learning and use a form of self-supervision to learn online iterative update rules for adaptive filters. To demonstrate our approach, we focus on audio applications and systematically develop meta-learned adaptive filters for five canonical audio problems including system identification, acoustic echo cancellation, blind equalization, multi-channel dereverberation, and beamforming.We compare our approach against common baselines and/or recent state-of-the-art methods. We show we can learn high-performing adaptive filters that operate in real-time and, in most cases, significantly outperform each method we compare against – all using a single general-purpose configuration of our approach.

Quality Analysis of Space and Space-Frequency Adaptive Signal Processing Algorithms in Satellite Navigation Systems

The problem of interference mitigation in satellite navigation signals is considered. Construction algorithms for space- and space-frequency adaptive filters are described. The processing of real satellite signals with broadband interference by means of the constructed filters is carried out in MATLAB. As a result of comparing different processing quality indicators, the most efficient filtering algorithms are identified.

Micro-ultrasonic Assessment of Early Stage Clot Formation and Whole Blood Coagulation Using an All-Optical Ultrasound Transducer and Adaptive Signal Processing Algorithm.

Abnormal formation of solid thrombus inside a blood vessel can cause thrombotic morbidity and mortality. This necessitates early stage diagnosis, which requires quantitative assessment with a small volume, for effective therapy with low risk to unwanted development of various diseases. We propose a micro-ultrasonic diagnosis using an all-optical ultrasound-based spectral sensing (AOUSS) technique for sensitive and quantitative characterization of early stage and whole blood coagulation. The AOUSS technique detects and analyzes minute viscoelastic variations of blood at a micro-ultrasonic spot (<100 μm) defined by laser-generated focused ultrasound (LGFU). This utilizes (1) a uniquely designed optical transducer configuration for frequency-spectral matching and wideband operation (6 dB widths: 7-32 MHz and d.c. ∼ 46 MHz, respectively) and (2) an empirical mode decomposition (EMD)-based signal process particularly adapted to nonstationary LGFU signals backscattered from the spot. An EMD-derived spectral analysis enables one to assess viscoelastic variations during the initiation of fibrin formation, which occurs at a very early stage of blood coagulation (1 min) with high sensitivity (frequency transition per storage modulus increment = 8.81 MHz/MPa). Our results exhibit strong agreement with those obtained by conventional rheometry (Pearson's R > 0.95), which are also confirmed by optical microscopy. The micro-ultrasonic and high-sensitivity detection of AOUSS poses a potential clinical significance, serving as a screening modality to diagnose early stage clot formation (e.g., as an indicator for hypercoagulation of blood) and stages of blood-to-clot transition to check a potential risk for development into thrombotic diseases.

Generation of temporal fading envelope sequences for the FSOC channel based on atmospheric turbulence optical parameters.

The temporal characteristics of the free space optical communication (FSOC) turbulence fading channel are essential for analyzing the bit error rate (BER) performances and compiling the rationale of adaptive signal processing algorithms. However, the investigation is still limited since the majority of temporal sequence generation fails to combine the autocorrelation function (ACF) of the FSOC system parameters, and using the simplified formula results in the loss of detailed information for turbulence disturbances. In this paper, considering the ACF of engineering measurable atmospheric parameters, we propose a continuous-time FSOC channel fading sequence generation model that obeys the Gamma-Gamma (G-G) probability density function (PDF). First, under the influence of parameters such as transmission distance, optical wavelength, scintillation index, and atmospheric structural constant, the normalized channel fading models of ACF and PSD are established, and the numerical solution of the time-domain Gaussian correlation sequence is derived. Moreover, the light intensity generation model obeying the time-domain correlation with statistical distribution information is derived after employing the rank mapping, taking into account the association between the G-G PDF parameters and the large and small scales turbulence fading channels. Finally, the Monte Carlo numerical method is used to analyze the performances of the ACF, PDF, and PSD parameters, as well as the temporal characteristics of the generated sequence, and the matching relationships between these parameters and theory, under various turbulence intensities, propagation distances, and transverse wind speeds. Numerical results show that the proposed temporal sequence generation model highly restores the disturbance information in different frequency bands for the turbulence fading channels, and the agreement with the theoretical solution is 0.999. This study presents essential numerical simulation methods for analyzing and evaluating the temporal properties of modulated signals. When sophisticated algorithms are used to handle FSOC signals, our proposed temporal sequence model can provide communication signal experimental sample data generating techniques under various FSOC parameters, which is a crucial theoretical basis for evaluating algorithm performances.

Adaptive sign algorithm for graph signal processing

Efficient and robust online processing techniques for irregularly structured data are crucial in the current era of data abundance. In this paper, we propose a graph/network version of the classical adaptive Sign algorithm for online graph signal estimation under impulsive noise. The recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity. The Graph-Sign algorithm proposed in this work is based on the minimum dispersion criterion and therefore impulsive noise does not hinder its estimation quality. Unlike the recently proposed graph adaptive least mean pth power algorithm, our Graph-Sign algorithm can operate without prior knowledge of the noise distribution. The proposed Graph-Sign algorithm has a faster run time because of its low computational complexity compared to the existing adaptive graph signal processing algorithms. Experimenting on steady-state and time-varying graph signals estimation utilizing spectral properties of bandlimitedness and sampling, the Graph-Sign algorithm demonstrates fast, stable, and robust graph signal estimation performance under impulsive noise modeled by alpha stable, Cauchy, Student’s t, or Laplace distributions.

A Split-Type FMICW-Based Guided Wave Radar With Multisegmental Probe for Liquid Level Measurement

In this paper, a specially designed guided wave radar device for measuring the liquid level in the nuclear industry is proposed. A split-type design is introduced to adapt to a strong radiation environment. An L-band frequency modulated interrupted continuous wave (FMICW) radar is designed to ensure the signal-to-noise ratio of echo signal after long-distance signal transmission, which provides the isolation between transmitter and receiver of more than 60 dB using the gating technology. To overcome the shortcomings of limited installation and maintenance space, a novel coaxial waveguide probe with a multi-segmental structure is proposed and simulated. An adaptive signal processing algorithm is developed to remove false echoes and eliminate measurement fluctuations. A prototype of the proposed measurement system is fabricated and evaluated. Static and dynamic liquid level experiments are performed in an outdoor environment to assess performance and verify the effectiveness of the proposed radar. The results show that the standard deviation measurement error of the proposed radar is within 0.4 cm. The proposed system is expected to have a high potential for applications in liquid level measurement under complex conditions.

Multirate Adaptive Equalization

Finite Impulse Response (FIR) filter model emulates the Inter Symbol Interference (ISI) in a wireless communication channel. An equalizer, typically an Infinite Impulse Response (IIR) filter, behaves as an inverse filter to the FIR filter to remove the effects of the ISI. IIR filters are generally avoided due to tractability issues, and an FIR filter, with an adaptive signal processing algorithm to minimize the error due to the ISI, is deployed at the receiver. However, the filter is observed to quickly reach a steady state where further iterations do not yield a reduction in the error. This can be attributed to relatively slow variations in the steady state error which prevent further reduction of the errors. This work focuses on converting the low frequency error variations to high frequency variations by the use of multirate signal processing. As such, the steady state error can be damped as well, providing further reduction in the error and an enhanced adaptive filter performance.

DETECTION OF A HARMONIC SIGNAL AGAINST THE BACKGROUND OF A NONSTATIONARY GAUSSIANINTERFERENCE WITH COMPLEX SPECTRUM

Background. Modern radar stations for various purposes operate in the conditions of interference created by the imprints of the radar signal from the background surface, from metrological formations (precipitation, clouds, etc.) and artificial radiation sources. Ensuring the operation of the radar in such difficult conditions requires the construction of adaptive signal processing algorithms that have high efficiency and maintain them when changing signal-to-noise situations. Objective. The purpose of the paper is creation of an adaptive algorithm for detecting a harmonic signal against the background of spatially correlated interference and estimating its parameters. Methods. Construction of a two-dimensional autoregressive model of a mixture of correlated spatial noise and harmonic signal and application of the empirical Bayesian approach to the synthesis of an adaptive algorithm for detecting and evaluating signal and noise parameters. Results. A two-dimensional adaptive space-time algorithm for detecting a radar signal reflected from a moving target against the background of a space-correlated interference is synthesized. The analysis of the efficiency of the algorithm by the Monte Carlo method is carried out. Conclusions. It is shown that the empirical Bayesian approach is an effective working methodology in solving the problem of detecting a harmonic signal and estimating its parameters under conditions of interference with a complex frequency spectrum under different conditions of a priori uncertainty of their parameters.

All-Ceramic LC Resonator for Chipless Temperature Sensing Within High Temperature Systems

The primary focus of this work is to fabricate and evaluate electroceramic based LC resonators for wireless sensing at high temperatures. An all-ceramic planar LC resonator was fabricated on a polycrystalline Al <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> substrate by using tin-doped indium oxide (ITO) as the electrode material. The LC resonators were deposited on the substrate by a novel two-step micro-casting technique. The resonator was designed using the ANSYS Maxwell package to operate within a wide frequency bandwidth from 10 to 80 MHz. A similar all-ceramic inductor was fabricated and used as the interrogator antenna to collect the wireless response at high operating temperature. The wireless characterization was performed by a radio frequency (RF) signal generator and an in-situ spectrum analyzer from 500 - 1200°C. The microstructural, chemical, and electrical stability of the LC resonator was investigated by SEM, 4-point conductivity, and XRD. Additionally, a robust adaptive signal processing algorithm was developed to analyze the wireless response of multiple LC resonator. The adaptive algorithm developed in this work is data driven and does not require a predefined model to analyze the wireless response from the LC resonator.

WITHDRAWN: Performances of some adaptive signal processing algorithms for the design of smart antenna

Abstract A smart antenna is a digital wireless communications antenna system that comprises of number of antenna elements or antenna arrays of having similar orientation and using adaptive beamforming algorithms to optimize the radiation pattern. It nullifies the pattern of the targets that are undesired and adding constructively the phases of the signals in the direction of the target. Smart antenna includes signal processing techniques that serves the demand-based frequency and flexible radiation patterns. The aim of this paper is to compare the performance of Least Mean Square (LMS) and Normalized Least Mean Square (NLMS) Adaptive algorithms. Beamforming by varying parameters are presented here. Comparative results show that NLMS performs better than LMS algorithm.

Adaptive Displacement Calibration Strategies for Field Structural Health Monitoring Based on Doppler Radars

This article presents two displacement calibration algorithms for Doppler radar demodulated data, which contribute to accurate monitoring of small vibrations even when the conventional arctangent demodulated displacement exhibits abrupt jumps. The field structural health monitoring of a traffic signal support structure was carried out by a 5.8-GHz Doppler radar to verify the effectiveness of each algorithm. The radar sensor captures and downconverts the microwave signal, which is phase modulated by the mast arm motion after being reflected on the ground. When conventional nonlinear phase demodulation is employed to retrieve the mast arm displacement from the Doppler radar baseband responses, sudden jumps are encountered in the displacement measurement. Two novel algorithms, referred to as the adaptive low-pass filtering algorithm (ALFA) and the adaptive joint signal processing algorithm (AJSPA), respectively, are suggested to eliminate jumps in phase-demodulated Doppler radar data. These strategies are compared in terms of accuracy and computational cost. The simulated and experimental results are provided to validate the proposed techniques. The presented methods can have wide applications in radar-based vibration monitoring.

Nearest Kronecker Product Decomposition Based Generalized Maximum Correntropy and Generalized Hyperbolic Secant Robust Adaptive Filters

Robust adaptive signal processing algorithms based on a generalized maximum correntropy criterion (GMCC) suffers from high steady state misalignment. In an endeavour to achieve lower steady state misalignment, in this letter we propose a generalized hyperbolic secant function (GHSF) as a robust norm and derive the generalized hyperbolic secant adaptive filter (GHSAF). The new algorithm is seen to offer robust system identification performance over the conventional GMCC algorithm. To further improve the convergence performance under non-Gaussian noise environments, we propose the nearest Kronecker product decomposition based GMCC and GHSAF algorithms. Extensive simulation study show the improved convergence performance provided by the proposed algorithms for system identification.

Synthesis of the Radio-navigation Signals Parameters Measured by an Exploded Antenna Elements

Modern GNSS navigation equipment has a significant drawback — low noise immunity. To solve this problem, they are based on phased array antennas using adaptive signal processing algorithms. The final effect of such processing is determined by the level and completeness of using the existing differences, as well as the quality of accounting for each of them, depending on the degree of fame of the statistical characteristics of signals and interference. The article discusses the theory of synthesis of an algorithm for measuring the parameters of radio navigation signals received on spaced antennas under the influence of interference

CMUT-based biosensor with convolutional neural network signal processing

The improvement of the micromachined ultrasound transducer based (CMUT) biosensor fabrication technology and signal processing, which led to higher signal to noise ratio is reported. The biosensor contains interdigitally arranged CMUT structure with gold-coated analytical area. It is assembled with the plexiglass microchannels. CMUTs were fabricated with the wafer bonding technology for 5 MHz operation in immersion. For signal processing the convolutional neural network (CNN) was developed and trained to classify the sensor data to different propagation delay values. For training of the network 750 thousand signals representing different properties of the bioanalyte and different noise conditions was simulated by the finite time difference domain (FDTD) model. The capability of the CNN algorithm to classify the propagation delay data was compared with the adaptive passband filter signal processing algorithm used in our previous version of the senor. Both sensing channels were run simultaneously with the reference liquids in the microchannel: deionized water switching to 0.9% saline. It was found that CNN channel is capable to improve the signal to noise ratio for this experiment to 75 dB, when the same property for the passband filter channel was only 60 dB. This led to the generalization about the advantage of CNN channel to provide 15 dB less of instrumental noise. Finally, the real-time detection ability of the bovine serum albumin (BSA) deposition on the analytical area of improved sensor was demonstrated.

Radar maneuvering target tracking algorithm based on human cognition mechanism

Radar Maneuvering Targets Tracking (RMTT) in clutter is a quite challenging issue due to the errors in the models and the varying dynamics of the processes. Modern radar tracking system calls for the adaptive signal and data processing algorithm urgently to adapt the uncertainty of the environment. The mechanism of human cognition can help persons cope with the similar difficulties in visual tracking. Inspired by human cognition mechanism, a comprehensive method for RMTT is proposed. In the method, the model transition probability in Interacting Multiple Model (IMM) and the validation gate can be adjusted dynamically with target maneuver; the waveform in radar transmitter can vary with the perception of the environment. Experimental results in cluttered scenes show that the proposed algorithm is more accurate for perceiving the environment and targets, and the waveform selection algorithm is better than that with fixed waveform.