Filter Output Research Articles

Filter-Based Disturbance Observer and Adaptive Control for Euler–Lagrange Systems With Application to a Quadrotor UAV

In this paper, a filter-based control scheme is developed for Euler-Lagrange systems with unknown parameter and external disturbance. Two controllers are proposed under the filtering framework, namely the adaptive controller and the disturbance observer-based controller, respectively. A set of low-pass filters are employed in the control design to derive useful filter outputs, which can provide the design freedom for the parameter estimator or the disturbance observer. For the adaptive controller, a certainty equivalent adaptive law is designed in such way that dominates the cross term between the estimation error and the filtered regression matrix to achieve better performance compared with classical estimators. For the disturbance observer-based controller, a high-gain disturbance observer is constructed for the lumped uncertainty with an arbitrary order for users to determine. Experimental results are presented to verify the effectiveness of the proposed schemes on a quadrotor UAV in several cases.

Switch-Constrained Multiple-Model Algorithm for Maneuvering Target Tracking

In most of the existing state estimation algorithms for maneuvering target tracking, the formulation of the mode switching process fails to accurately capture the deterministic law that the target mode will persist for a period of time once it turns to be in effect. In this paper, two strategies are presented to describe this characteristic. First, a constraint that model switching occurs no more than once over three consecutive time steps is proposed to provide a deterministic description that mode does not switch continuously. Second, considering target mode always lasts for some time while mode switching is completed instantly, the transition probabilities from one model to itself are fixed at extremely large values to enhance the certainty in mode sojourn segments. Based on these strategies, two recursive multiple model (MM) filters are derived in the framework of generalized pseudo-Bayesian (GPB) estimation and interacting multiple model (IMM) estimation, respectively. In both algorithms, although the large probability of model staying unchanged causes high peak errors at mode switching instants, the introduction of the switching constraint prevents the input to the mode-matched filter from being affected by the model transition probability, thereby obtaining a near-optimal performance in mode sojourn segments. Additionally, in order to reduce the peak estimation errors, a mode switching detector based on likelihood function is presented to work in parallel with the recursive MM filters. Once the detector declares that a mode switch occurs, the final estimate is adjusted from the output of the recursive MM filters to the estimate of the corresponding elemental filter, performance of which is guaranteed by the switching constraint. Simulation results demonstrate the capability of the proposed recursive MM filters to obtain performance close to that of a filter without model uncertainty in mode sojourn segments and the effectiveness of the mode switching detector to reduce peak errors at the same time.

Deterministic Learning-Based Adaptive Neural Control for Nonlinear Full-State Constrained Systems.

In this article, an adaptive neural learning method is introduced for a category of nonlinear strict-feedback systems with time-varying full-state constraints. The two challenging problems of state constraints and learning capability are investigated and solved in a unified framework. To obtain the learning of unknown functions and satisfy full-state constraints, three main steps are considered. First, an adaptive dynamic surface controller (DSC) based on barrier Lyapunov functions (BLFs) is structured to implement that the closed-loop systems signals are bounded and full-state variables remain within the prescribed time-varying intervals. Moreover, the radial basis function neural networks (RBF NNs) are used to identify unknown functions. The output of the first-order filter, instead of virtual control derivatives, is used to simplify the complexity of the RBF NN input variables. Second, the state transformation is used to obtain a class of linear time-varying subsystems with small perturbations such that the recurrence of the RBF NN input variables and the partial persistent excitation condition are actualized. Therefore, the unknown functions can be accurately approximated, and the learned knowledge is kept as constant NN weights. Third, the obtained constant weights are borrowed into an adaptive learning scheme to achieve the batter control performance. Finally, simulation studies illustrate the advantage of the reported adaptive learning method on higher tracking accuracy, faster convergence rate, and lower computational expense by reusing learned knowledge.

Radar detection and speed estimation of high‐speed targets using phase‐modulated waveforms

AbstractSpecifying parameters of high‐speed targets as range and velocity in radar systems operating with phase‐modulated signals is a problematic process because of the serious effect of high Doppler frequency on these types of waveforms. In the existence of a high‐speed target, its Doppler frequency will be very high such that it totally changes the phase sequence of the received signal. As a result, the matched filter output will be distorted. Therefore, estimating target parameters will be impossible. In this letter, a new method is proposed for detecting and estimating velocity of high speed targets using phase‐modulated signals. The suggested technique methodology depends on compensating the target high‐Doppler frequency for signal recovery using a customized waveform shape. Moreover, the Doppler frequency value is estimated without ambiguity from one pulse, and without the need for a range‐Doppler module. As such, the implementation of the proposed method is simple and inexpensive. The performance evaluation of the new method shows its superiority in compensation and estimation compared to other techniques when examined under higher Doppler frequency and noise compared to the conventional radar.

Determination of Meteor Vector Velocity Using MU Interferometry Measurements of Head Echoes

A new method for measuring the vector velocity of meteoroids using meteor head echoes is proposed in this study. The lateral velocity is determined by utilizing the phase interference measurement between channels, while the radial velocity is obtained using a conventional Doppler frequency shift measurement. Compared to previous studies, this method does not require multi-site observations and can calculate the vector velocity of meteors in real-time. This paper provides the complete process for the inversion of the meteor vector velocity, detailing the analyzing process using MU radar head echo data. First, the MUSIC algorithm was used to estimate the DOA of the meteor target, which is a parameter required for lateral velocity measurement. Channel calibration is required before this estimation. Next, delay-Doppler matched filter processing was performed on each receiving channel’s data to determine the distance and radial velocity of the meteor target. Subsequently, the lateral velocity component was synthesized using the least squares method from the phase difference rate extracted from the matched filter output results of multiple channel pairs. Then, the vector velocity and trajectory of the meteor could be determined. The method was verified using MU radar head echo data. Different groups of channel pairs were selected for calculating the lateral velocity, and the results were found to be close, demonstrating the self-consistency of the method. Additionally, the calculated vector velocity is consistent with the direction and magnitude of the meteor’s motion trajectory, confirming the feasibility of the proposed approach. The method allows for the observation of more prominent characteristics of meteoroid motion, providing a more detailed observation capability of velocity variations in other directions than previous methods.

ВРЕМЕННАЯ СИНХРОНИЗАЦИЯ В СИСТЕМЕ МОБИЛЬНОЙ РАДИОСВЯЗИ ПЯТОГО ПОКОЛЕНИЯ

In this paper, the issue of time synchronization between the base station and user equipment in a fifth-generation cellular communication system is considered. In order to reduce the complexity of the current method of estimating the start symbol offset, a modification of the algorithm is proposed, in which the cross-correlation is computed in parallel for each DMRS (Demodulation Reference Signal) symbol. Furthermore, calculating the cross-correlation for each DMRS symbol allows for the development of a new method of estimating the sampling frequency offset based on the time interval estimation between two consecutive DMRS signals at the output of the matched filter. It is demonstrated that using a parabolic interpolation of the local regions around the correlation peak at the matched filter outputs yields an accurate estimation of the temporal distance between the received DMRS signals. Numerical simulation results show that the suggested technique can estimate the sampling frequency offset with an accuracy of less than 1 ppm over the entire range of signal-to-noise ratio values.

A model of colour appearance based on efficient coding of natural images.

An object's colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and "illusions" have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for in quantitative models of colour appearance. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial band's lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next, we systematically test the model's ability to qualitatively predict over 50 brightness and colour phenomena, with almost complete success. This implies that much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a well-founded basis for modelling the vision of humans and other animals.

Selection of the convergence step of the Fx-LMS algorithm

The choice of a suitable convergence step that allows the fast convergence of the filtered-x LMS algorithm in practical scenarios is usually carried out by trial and error, since the theoretical developments provide only indicative values. The particularity of the filtered-x structure, which adds a delay between the output of the adaptive filter and the error signal and colors the reference signal, increases the inaccuracy of the theoretical models. Furthermore, the use of band-limited reference signals is beyond the scope of most theoretical studies, which focus on the cases of full-band reference signals, making it difficult to extrapolate these models to band-limited signals. In this paper, we propose a low-cost and meaningful method for selecting the convergence step that is well tuned in most practical cases. It can be used regardless of the type of the reference signal, the system delays, and the nature of the filter required in the filtered-x structure. Numerical simulations are performed to validate the effectiveness of the proposed approach.

An adaptive digital filter for quasi-Gaussian pulse shaping

The radiation detector output is often shaped into Gaussian or quasi-Gaussian shape to improve the measurement performance. An adaptive digital Step-Gaussian filter is presented for quasi-Gaussian pulse shaping in this paper. It can be applied for both exponential decay input and step input. The transfer function of the filter is established. The amplitude-frequency features of the new filter are compared with the PZC-(RC) n filter and the CR-(RC) n filter. The results indicate that the new filter has the same amplitude-frequency features as the PZC-(RC) n filter and it possesses more effective filter performance than the CR-(RC) n filter. An X-ray fluorescence measurement system based on a FAST-SDD detector is set up for experiments. The proposed filter is verified by the measured pulses that are generated from a manganese sample. The shape of the filter output shows that the Step-Gaussian filter can eliminate the undershoot that exists in the CR-(RC) n filter output. The amplitude spectrums with different shaping times are created to study the shape of the characteristic peak. The results show that the Step-Gaussian filter can cancel the low-energy peak tailing of the 5.89 keV peak. The peak shape parameter, FWTM/FWHM ratio, is also introduced to evaluate the performance of peak shape improvement. The 5.89 keV peaks of the spectrums that are generated by using Step-Gaussian filter are closer to Gaussian distribution than that of the CR-(RC) n filter.

Event-Based Output Feedback Consensus Control for Multiagent Systems With Unknown Non-Identical Control Directions

This article studies the event-triggered output feedback consensus problem for a class of high-order uncertain nonlinear multiagent systems with totally unknown non-identical control directions. Firstly, the reduced-order filters are constructed by utilizing the dynamic gain technique to compensate the system uncertainties and unmeasurable states of the followers. Based on the filter states and output information, the asymptotic output consensus result is guaranteed with the developed distributed adaptive output feedback control protocol, while the inherent issue “complexity explosion” in backstepping design is avoided. Then, to solve the interaction of multiple Nussbaum functions in a single inequality, the Nussbaum functions with different frequencies are adopted, with which the unknown directions of multiagent system are no longer limited in identical. In addition, we propose a dynamic triggering strategy with time-varying threshold parameters to reduce the updates of controller without Zeno phenomenon. Finally, simulation results are provided to illustrate the effectiveness of theoretical algorithm.

GNSS array receiver faced with overloaded interferences: anti-jamming performance and the incident directions of interferences

Anti-jamming solutions based on antenna arrays enhance the anti-jamming ability and the robustness of global navigation satellite system (GNSS) receiver remarkably. However, the performance of the receiver will deteriorate significantly in the overloaded interferences scenario. We define the overloaded interferences scenario as where the number of interferences is more than or equal to the number of antenna arrays elements. In this paper, the effect mechanism of interferences with different incident directions is found by studying the anti-jamming performance of the adaptive space filter. The theoretical analysis and conclusions, which are first validated through numerical examples, reveal the relationships between the optimal weight vector and the eigenvectors of the input signal autocorrelation matrix, the relationships between the interference cancellation ratio (ICR), the signal to interference and noise power ratio (SINR) of the adaptive space filter output and the number of interferences, the eigenvalues of the input signal autocorrelation matrix. In addition, two simulation experiments are utilized to further corroborate the theoretical findings through soft anti-jamming receiver. The simulation results match well with the theoretical analysis results, thus validating the effect mechanism of overloaded interferences. The simulation results show that, for a four elements circular array, the performance difference is up to 19 dB with different incident directions of interferences. Anti-jamming performance evaluation and jamming deployment optimization can obtain more accurate and efficient results by using the conclusions.

Wireless Picosecond Time Synchronization for Distributed Antenna Arrays

Distributed antenna arrays have been proposed for many applications ranging from space-based observatories to automated vehicles. Achieving good performance in distributed antenna systems requires stringent synchronization at the wavelength and information levels to ensure that the transmitted signals arrive coherently at the target, or scattered and received signals can be appropriately processed via distributed algorithms. In this article, we address the challenge of high-precision time synchronization to align the operations of elements in a distributed antenna array and overcome time-varying bias between platforms primarily due to oscillator drift. We use a spectrally sparse two-tone waveform, which obtains approximately optimal time estimation accuracy, in a two-way time transfer process. We also describe a technique for determining the true time delay using the ambiguous two-tone matched filter output, and we compare the time synchronization precision of the two-tone waveform with the more common linear frequency modulation (LFM) waveform. We experimentally demonstrate wireless time synchronization using a single-pulse 40-MHz two-tone waveform over a 90-cm 5.8-GHz wireless link in a laboratory setting, obtaining a timing precision of 2.26 ps.

High-Resolution Ultrasound Characterization of Local Scattering in Cancer Tissue.

Ultrasound (US) has afforded an approach to tissue characterization for more than a decade. The challenge is to reveal hidden patterns in the US data that describe tissue function and pathology that cannot be seen in conventional US images. Our group has developed a high-resolution analysis technique for tissue characterization termed H-scan US, an imaging method used to interpret the relative size of acoustic scatterers. In the present study, the objective was to compare local H-scan US image intensity with registered histologic measurements made directly at the cellular level. Human breast cancer cells (MDA-MB 231, American Type Culture Collection, Manassas, VA, USA) were orthotopically implanted into female mice (N=5). Tumors were allowed to grow for approximately 4 wk before the study started. In vivo imaging of tumor tissue was performed using a US system (Vantage 256, Verasonics Inc., Kirkland, WA, USA) equipped with a broadband capacitive micromachined ultrasonic linear array transducer (Kolo Medical, San Jose, CA, USA). A 15-MHz center frequency was used for plane wave imaging with five angles for spatial compounding. H-scan US image reconstruction involved use of parallel convolution filters to measure the relative strength of backscattered US signals. Color codes were applied to filter outputs to form the final H-scan US image display. For histologic processing, US imaging cross-sections were carefully marked on the tumor surface, and tumors were excised and sliced along the same plane. By use of optical microscopy, whole tumor tissue sections were scanned and digitized after nuclear staining. US images were interpolated to have the same number of pixels as the histology images and then spatially aligned. Each nucleus from the histologic sections was automatically segmented using custom MATLAB software (The MathWorks Inc., Natick, MA, USA). Nuclear size and spacing from the histology images were then compared with local H-scan US image features. Overall, local H-scan US image intensity exhibited a significant correlation with both cancer cell nuclear size (R2 > 0.27, p < 0.001) and the inverse relationship with nuclear spacing (R2 > 0.17, p < 0.001).

Chiral topological add–drop filter for integrated quantum photonic circuits

The integration of quantum emitters within topological nanophotonic devices enables the control of light–matter interactions at the single photon level. Here, we experimentally realize an integrated topological add–drop filter and observe multiport chiral emission from single photon emitters (quantum dots) embedded within the device. The filter is imprinted within a valley-Hall photonic crystal membrane and comprises a resonator evanescently coupled to a pair of access waveguides. We show that the longitudinal modes of the resonator enable the filter to perform wavelength-selective routing of light, protected by the underlying topology. Furthermore, we demonstrate that for a quantum dot located at a chiral point in the resonator, selective coupling occurs between well-defined spin states and specific pairs of the filter output ports. The combination of multiport routing, allied with the inherent nonreciprocity of the device at the single photon level, presents opportunities for the formation of complex quantum optical devices, such as an on-chip quantum optical circulator.

Ultra-Narrow Bandwidth Fabry-Perot Optical Filter Illuminated by a Gaussian Beam

We implement an ultra-narrow bandwidth (UNB) Fabry-Perot (FP) optical filter that has two flat mirrors illuminated by a normally incident Gaussian beam from an optical fiber. Owing to the finite waist radius of the Gaussian beam, the UNB FP optical filter’s transmittance curve deviates significantly from that of the plane wave incidence case. Applying an amplified spontaneous emission to the UNB FP optical filter, we find the waist radius of the Gaussian beam from the beat noise spectrum of the UNB FP optical filter output. Then, the transmittance of the UNB FP optical filter is evaluated by expanding the Gaussian beam as infinitely many plane waves. With this measurement method, we implement the UNB FP optical filter having its optical bandwidth down to 270 MHz.

Statistics and probabilities for spectral analysis

Spectral analysis of data plays a central role in many signal processing applications, and comparing the relative output levels between filters often provides clues about the actual signals present in the data. Since data are often corrupted by noise, interference, or some other source of randomness, these filter outputs are also subject to random variations. Proper interpretation of these filter outputs is made possible by an awareness of the nature of their statistical variability. Thus, homework problems that illustrate how basic fundamental properties of random variables/vectors lend significant insight into the nature of such filter output comparisons are of great value in engineering practice. This talk will describe a handful of interesting problems that help with the interpretation of filtered data outputs used in some form of spectral analysis (e.g., FFT filters, beamformers, etc.). The problems considered will leverage notions of invariances, regeneration properties, and a basic algebra of random variables.

Robust iterative adaptive filtering against intrapulse Doppler shifts

Recently, a two-dimensional joint iterative adaptive filtering (2-D JIAF) has been proposed to address the masking effect of large targets on adjacent small targets in multi-target scenarios. However, its performance is impaired for the cases with fast moving targets due to intrapulse Doppler mismatch. In this paper, we consider the intrapulse Doppler shifts in filtering design and propose a robust iterative adaptive filtering algorithm based on matched filter outputs, termed robust iterative adaptive filtering against intrapulse Doppler shifts (RIAF-IDS), to suppress the range sidelobes induced by intrapulse Doppler mismatch and improve the filtering performance. The proposed algorithm can jointly suppress range-Doppler sidelobes and obtain accurate estimation of range-Doppler image even in cases with fast moving targets. The derivation of RIAF-IDS is provided in detail, and its filtering performance is validated through several simulations, which demonstrate that RIAF-IDS has superior Doppler tolerance over 2-D JIAF at the cost of more computation.

Astronomical time scale of the lower Miocene depositional sequences (Aquitanian-Burdigalian) in the Jambour Field, northern Iraq

The Miocene succession is considered one of the major shallow hydrocarbon reservoirs in Iraq. It consists primarily of lagoonal evaporites and carbonate alternations, with many barren intervals and non-diagnostic fauna. An astronomical time scale (ATS) was established in two wells in Jambour Field, Kurdistan, using high-resolution cyclostratigraphic analysis of GR data for the first time in Iraq to determine the absolute age of the Miocene sequences and their boundaries. Sixteen unique carbonate and evaporite facies types were discovered and classified into five facies associations, from the mid-outer ramp to the peritidal carbonate and evaporite ramps. The most significant of these implies that, after astro-climatic frequencies, sea-level change was the dominant regulator of lower Miocene deposition. The difference in GR and facies type is mostly due to third eustatic sequences, which describe a rich series of long and short-period eccentricities in orbitally defined terms. The eustatic sea-level variations of the third-order sequences correlate with the sedimentary noise model applied over the tuned GR. A sharp sediment accumulation rate (SAR) was caused by an abrupt shift in the depositional environment, as demonstrated by the applied eCOCO within the lower Aquitanian part of the Dhiban Formation; consequently, each well was split into two datasets. The SAR ranges from 3.1 to 1.97 cm/kyr in the Ja46 well and 2.3 to 3.3 in the Ja49 well. The MTM spectral analysis of the four datasets revealed the eccentricity cycles' dominance with a high degree of confidence level. The present study employs high-resolution ATS using tuning of the GR with the new merged short and long eccentricity cycle filter output method, which is supported by previous strontium isotope studies and compared to the La 2004 astronomical solution. According to the ATS, the Aquitanian-Burdigalian sequences range from 5.08 myr at Ja46 Well to 4.66 myr at Ja49 Well. The tuning output indicated the presence of two hiatuses, the first in the Dhiban Formation with a time gap of about 0.21 and 0.3 myr in the Ja46 and Ja49 wells, respectively, and the second at the top of the Jeribe Formation with a time gap of about 0.34 myr in the Ja49 well. This pause corresponds to a significant sea-level drop in the Arabian Platform Basin at 21.4–21.0 and 18.5 Ma.

Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Image by Median Filtering over Wiener Filtering

Aim: The purpose of this study is to use median filters and wiener filters to minimize noise in liver images in order to improve them. In addition, the output of both filters was analyzed based on their Peak Signal to Noise Ratio (PSNR). Materials and Methods: The research includes two groups; each group has a sample size of 20. Grayscale medical images collected from the kaggle website were used in this research. Samples were considered as (N=20) for guided filter and (N=20) for fast bilateral filter with total sample size 40 calculated using clinicalc.com. Image enhancement is used to enhance the niceness of a picture for the visible notion of human beings. The kaggle website was used to collect data for this study. According to clinical.com, samples were considered as size 20 for PSNR ratio of image G power of 80%, and total sample size determined. Using matlab programming and a standard data set, the Linear filtering, Median filtering were computed. Results: According to Matlab simulation results, unique median filters have a PSNR of 48.1240, while wiener filters have a PSNR of 67.8360. Comparison of PSNR values are done by independent sample test using IBM-SPSS software. There is a statistical insignificant difference between both techniques. The significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p&gt;0.05 was found in the statistical analysis. Conclusion: On ultrasound liver pictures, the innovative median filter gives greater PSNR than the wiener filter for medical image enhancing purposes, according to this study.

A Model Randomization Approach to Statistical Parameter Privacy

In this article, we study a privacy filter design problem for a sequence of sensor measurements whose joint probability density function (p.d.f.) depends on a private parameter. To ensure parameter privacy, we propose a filter design framework which consists of two components: a randomizer and a nonlinear transformation. The randomizer takes the private parameter as input and randomly generates a pseudo parameter. The nonlinear mapping transforms the measurements such that the joint p.d.f. of the filter's output depends on the pseudo parameter rather than the private parameter. It also ensures that the joint p.d.f. of the filter's output belongs to the same family of distributions as that of the measurements. The design of the randomizer is formulated as an optimization problem subject to a privacy constraint, in terms of mutual information, and it is shown that the optimal randomizer is the solution of a convex optimization problem. Using information-theoretic inequalities, we show that the performance of any estimator of the private parameter, based on the output of the privacy filter, is limited by the privacy constraint. The structure of the nonlinear transformation is studied in the special cases of independent and identically distributed, Markovian, and Gauss-Markov measurements. Our results show that the privacy filter in the Gauss-Markov case can be implemented as two one-step ahead Kalman predictors and a set of minimum mean square error predictors. A numerical example on occupancy privacy in a building automation system illustrates the approach.