Cancellation Method Research Articles

Clutter Cancellation Methods for Small Target Detection Using High-Resolution W-band Radar.

Drones are currently being used for various applications. However, the detection of drones for defense or security purposes has become problematic because of the use of plastic materials and the small size of these drones. Any drone can be placed under surveillance to accurately determine its position by collecting high-resolution data using various detectors such as the radar system proposed in this paper. The W-band radar has a high carrier frequency, which makes it easy to design a wide bandwidth system, and the wideband FMCW signal is suitable for creating high resolution images from a distance. Unfortunately, the huge amounts of data gathered in this way also contain clutter (such as background data and noise) that is usually generated from unstable radar systems and complex environmental factors, and which frequently gives rise to distorted data. Accurate extraction of the position of the target from this big data requires the clutter to be suppressed and canceled, but conventional clutter cancellation methods are not suitable. Four clutter cancellation algorithms are assessed and compared: standard deviation, adaptive least mean squares (LMS), recursive least squares (RLS), and the proposed LMS. The proposed LMS has combined LMS with the standard deviation method. First, the big data pertaining to the target position is collected using the W-band radar system. Subsequently, the target position is calculated by applying these algorithms. The performance of the proposed algorithms is measured and compared to that of the other three algorithms by conducting outdoor experiments.

Design of a slot array antenna with ultra-wideband low-scattering characteristic

This paper presents a 4 × 4 slot array antenna with ultra-wideband low-scattering characteristic, which integrates a metasurface based on the multielement phase cancellation (MEPC) method. Limited by the characteristics of the slot antenna array, the contradiction between the ideal radiation performance and the requirement of low radar cross section (RCS) has not been well resolved. Therefore, in this paper, a MEPC-based metasurface is designed as the cover-layer of the proposed antenna, which can disperse the reflected energy to more directions without degrading the radiation performance. The proposed antenna operates in a bandwidth of 3.89–4.00 GHz with a maximum gain of 14.3 dBi. Additionally, the 7 dB RCS reduction bandwidth for TE/TM polarization at normal incidence is 5.5–40 GHz (f h / f l = 7.27:1), achieving ultra-wideband out-of-band RCS reduction. Furthermore, low-scattering performance can remain stable at large-angle oblique incidence. To verify the feasibility of the proposed design, a prototype is fabricated and measured for demonstration. The simulated and measured results are in good agreement.

Interference Cancelation for Coexistence of LoRaWAN With Wireless Power Transfer

Low power wide area networks (LPWANs), which realize long-distance communication with low power consumption, are suitable for wireless sensor networks (WSNs). In particular, long range wide area network (LoRaWAN) has been actively studied in recent years. Since wireless power transfer (WPT) charges the battery of terminals by sending high-frequency signals, it can avoid operational disruption for battery replacement and unnecessitate wiring. However, if it is not well designed, WPT may cause interference to other wireless communication systems using the same frequency band. This paper proposes a simple yet effective WPT interference cancellation method, which is suitable for LoRaWAN, to take a step to move the coexistence of LPWAN and WPT forward. The proposed method estimates the initial phase of the interfering signal by utilizing the packet frame structure of LoRaWAN and removes the interfering signal from the received signal during the signal detection process. Computer simulation results show that the proposed method can achieve almost the same symbol error rate (SER) performance as the system without interference, even under WPT interference.

Resonance reduction for linked train cars moving on multiple simply supported bridges

The dynamic interaction of a moving train over a series of railway bridges with equal spans can result in a coupled vibration problem of wave transmission between two periodic structures. Dual resonance occurs when both systems vibrate in phase and the train travels at a resonant speed. The objectives of this paper are to (1) develop an active pitching resonator (APR) to attenuate resonance waves in a train; (2) study the dispersion relation for pitching motion control of periodically linked train cars; (3) investigate the key parameters that affect the dispersion band gaps of wave attenuation in a train; (4) present an active vibration cancellation method to enhance the control performance of the APR; and (5) demonstrate the effectiveness of using the optimal APRs to reduce resonance of a moving train. Through dispersion analysis, the optimum APR can not only shift the target frequency level away from resonance of a moving train, but also create a wide resonance band gap to reduce wave transmission in the train. Finally, the effectiveness of the optimum APR unit in reducing the resonance and attenuating the waves of a train travelling over multiple-span simple beams is demonstrated by dynamic finite element analysis of the train-bridge interaction.

A reference free non-negative adaptive learning system for health care monitoring and adaptive physiological artifact elimination in brain waves

Electroencephalogram (EEG), also referred to as brain wave (BW), is a physiological phenomenon that depicts how the human brain functions. Brain wave analysis is fundamental in applications like brain-computer interference (BCI), beamforming, sleep analysis, epilepsy detection, and emotion recognition. In real-time applications, the brain wave encounters many physiological and non-physiological artifacts during acquisition. Due to this phenomenon, the analysis method is complicated and obscures the brain wave’s tiny features. This study proposes an intelligent signal enhancement unit (SEU) for processing EEG signals to enable decision-making under certainty. The proposed SEU enables healthcare professionals to analyze high-resolution brain wave components for various applications. A new singular spectrum decomposition (SSD) based on score reconstruction (score RC) is used in the first phase of the SEU to generate the artifact nature, which is then used as a reference signal in the adaptive artifact cancellation (AAC) method. The SSD performs the embedding, decomposition, grouping, and reconstruction procedures to provide the reference signal. A modified Logarithmic Non-Negative Adaptive Learning Algorithm (MLNNAL) is employed in the second stage of the SEU to improve the EEG signal. With the help of this proposed adaptive learning, a system with lower computing complexity which is stable and has non-negative weights can be realized. The adaptive learning algorithm’s weight recursion continually reweights the weight vector for each iteration to eliminate artifacts from contaminated brain waves. Excess mean square error (EMSE), signal to noise ratio improvement (SNRI) and computational cost of the adaptive learning algorithm are used to evaluate how the proposed SEU performs.

DPCA-Based Doppler Radar Measurements from Space: Effect of System Errors on Velocity Estimation Performance

Abstract The displaced phased center antenna (DPCA) method of clutter cancellation for ground moving target detection from airborne platforms has been in use for a number of decades. Application of the DPCA method for spaceborne Doppler weather radar velocity estimation was suggested in 2007. The initial description and analysis of the technique was followed several years ago by demonstration using a multiantenna airborne radar. Recent reviews of methods and technology for spaceborne cloud and precipitation radar have also mentioned possible use of DPCA. However, to date, analyses of the application of DPCA to spaceborne Doppler weather radar have assumed that the two channels and antennas are identical, including perfect alignment, and that the DPCA condition is well-satisfied. This study uses simulation to examine the effects of relaxing these assumptions. The simulation method and its validation are discussed, with companion analytical calculations in the appendix. Next, simulations are used to show the effects on the Doppler estimates from errors in pointing and positioning relative to the ideal DPCA. The DPCA technique is relatively robust to possible errors, indicating that a practical DPCA radar system can provide precise Doppler measurements from space. Significance Statement Analytical and simulation results show that the displaced phase center antenna approach can enable spaceborne atmospheric Doppler radar measurements with good accuracy, even in the presence of antenna mispointing and other system errors.

Multi-channel frequency difference noise analysis and cancellation method for the balanced field electromagnetic pipeline inspection gauge.

The balanced field electromagnetic technique as an effective in-line inspection method for cracks in long-distance oil and gas pipelines uses the pipeline inspection gauge (PIG) as the detection tool. PIG is characterized by the employment of a large number of sensors, but as each channel uses its crystal oscillator as a signal source, it inevitably generates frequency difference noise, which affects crack detection. A method of eliminating the frequency difference noise by using same-frequency excitation is proposed to solve the problem. Combining the principle of electromagnetic field propagation with the detection signal processing process, the formation process and characteristics of the frequency difference noise are theoretically analyzed, and the specific impact of frequency difference noise on crack detection is analyzed. The method of unified clock excitation for all channels is adopted, and a same-frequency excitation system is developed. The correctness of the theoretical analysis and the validity of the proposed method are verified by platform experiments and pulling tests. The results show that the effect of the frequency difference on noise follows the whole detection process, and the smaller the frequency difference, the longer the noise period. The frequency difference noise distorts the crack signal and is of comparable magnitude to the crack signal, which tends to drown out the crack signal. The same-frequency excitation method can eliminate frequency difference noise at the source and has a high signal-to-noise ratio. The method can provide a reference for multi-channel frequency difference noise cancellation in other AC detection technologies.

Linking Experimental and Ab Initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy.

Enthalpies of formation of adsorbates are crucial parameters in the microkinetic modeling of heterogeneously catalyzed reactions since they quantify the stability of intermediates on the catalyst surface. This quantity is often computed using density functional theory (DFT), as more accurate methods are computationally still too expensive, which means that the derived enthalpies have a large uncertainty. In this study, we propose a new error cancellation method to compute the enthalpies of formation of adsorbates from DFT more accurately through a generalized connectivity-based hierarchy. The enthalpy of formation is determined through a hypothetical reaction that preserves atomistic and bonding environments. The method is applied to a data set of 60 adsorbates on Pt(111) with up to 4 heavy (non-hydrogen) atoms. Enthalpies of formation of the fragments required for the bond balancing reactions are based on experimental heats of adsorption for Pt(111). The comparison of enthalpies of formation derived from different DFT functionals using the isodesmic reactions shows that the effect of the functional is significantly reduced due to the error cancellation. Thus, the proposed methodology creates an interconnected thermochemical network of adsorbates that combines experimental with ab initio thermochemistry in a single and more accurate thermophysical database.

Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization

To separate the noise and important signal features of the indoor carbon dioxide (CO2) concentration signal, we proposed a noise cancellation method, based on time-varying, filtering-based empirical mode decomposition (TVF-EMD) with Bayesian optimization (BO). The adaptive parameters of TVF-EMD, that is, bandwidth threshold ξ and B-spline order n, were determined by the BO algorithm, and the correlation coefficient for the kurtosis index (CCKur) constituted the objective function. Initially, the objective function CCKur was introduced to systematically identify anomalous signals while preserving signal feature extraction between the modes and the input signal. Subsequently, the proposed signal noise cancellation model based on TVF-EMD and the BO algorithm were employed, along with the Hurst exponent, to extract the sensitive mode. An examination of the optimization indices of the decomposed intrinsic mode functions (IMFs), namely CC, Kur, MI, EE, EEMI, and CCKur, revealed that the synthetic measurement index CCKur and objective function fitness were reasonable and effective. The proposed method exhibited better signal cancellation performance, compared to that of TVF-EMD with the default values, EMD, the moving average method, and the exponential smoothing method.

Analysis and compensation of frequency offset in co-site adjacent channel interference suppression

Due to the mismatch of the circuit oscillators, there will always be a frequency offset between the transmitter and the receiver. In the adjacent channel interference (ACI) suppression system using the reconstruction and cancellation method, the frequency offset decreases the accuracy of the reconstructed signal and introduces a time-varying term in the interference cancellation, resulting in poor performance of the ACI suppression. In this paper, the relationship between the normalized frequency offset, signal-to-noise ratio and the loss of interference suppression capability is analyzed through formula derivation and simulation. The validity of the frequency offset compensation method based on the pilot sequence is verified, and the relationship between the sequence length and the estimation accuracy is given. This paper provides necessary method reference and data support for the engineering of ACI suppression.

A Multi-machine Jamming Method for Side-lobe Cancellation

Adaptive side-lobe cancellation technology is an anti-jamming method commonly used in modern radars. In order to improve the jamming effect, it is necessary to study feasible jamming methods according to its characteristics. By analyzing the principle of adaptive side-lobe cancellation technology, this paper proposes a method that combines multi-directional jamming and correlated jamming. In order to verify the feasibility and advantages of this method, the simulation and analysis of the multi-machine interrupted-sampling cycle repeated jamming pattern signal against side-lobe cancellation system are carried out.

Designing FDA Radars Robust to Contaminated Shared Spectra

This paper considers the problem of jointly designing the transmit waveforms and weights for a frequency diverse array (FDA) in a spectrally congested environment in which unintentional spectral interferences exist. Exploiting the properties of the interference signal induced by the processing of the multi-channel mixing and low-pass filtering FDA receiver, the interference covariance matrix structure is derived. With this, the receive weights are formed using the minimum variance distortionless response (MVDR) method for interference cancellation. Owing to the fact that the resulting output signal-to-interference-plus-noise ratio (SINR) is a function of the transmit waveforms and weights, as well as due to the ever-greater competition for the finite available spectrum, a joint design scheme for the FDA transmit weights and the spectrally compatible waveforms is proposed to efficiently use the available spectrum while maintaining a sufficient receive SINR. The performance of the proposed technique is verified using numerical simulations in terms of the achievable SINR, spectral compatiblity, as well as several aspects of the synthesized waveforms.

Simultaneous Transmit and Receive Phased Array System Architecture and Prototype Comprehensive Verification

In the future multi-functional detection sensing and communication integrated systems, as well as 5G/6G systems, simultaneous transmit and receive technology, as an important key technology, is an important supporting measure to achieve the simultaneous work of different functions. Looking at the problem of strong self-interference in the simultaneous multi-function application of a phased array transceiver, this paper presents the integrated cancellation architecture and corresponding method of integrated transmission domain, RF domain and digital domain, and completes principle experiment verification of interference suppression and elimination by constructing the principle experiment system. Experimental results show that in the actual hardware system 0.5 db amplitude quantification and 6-bit phase quantization control conditions, through joint using passive and active beam transfer of the domain space optimization form, at the same time in the digital domain application of the adaptive system identification interference cancellation method for further interference cancellation, above 150 dB for interference suppression can be achieved. This research will provide a certain reference for the application of simultaneous transceiver technology in future 5G/6G phased array antenna systems.

Active Vibration Control of Motor Driven Power Steering for Reduction of Interior Noise

This study presents a novel active vibration control (AVC) system on motor driven power steering (MDPS) to reduce interior noise reduction caused by operating the MDPS in an electric vehicle. MDPS is electronic power steering (EPS). The MDPS attached to the rack gear of power steering system is called R-MDPS. Operating of the R-MDPS generates a structural vibration of R-MDPS, and the vibration is transmitted to car body through mounts of car subframe. The vibrating body of car becomes a monopole and dipole sources of vibroacoustic noise generated inside car. This vibracoustic noise is a structure borne noise and makes passenger annoyance. To reduce interior noise inside a car directly, active noise control (ANC) has been used as active method and is a useful method for active cancellation of the low frequency noises less than 400Hz. However, in this study, because the frequency range of interior noise due to operation of R-MDPS is higher than 400Hz, the AVC system is employed and is applied to active cancellation of the vibration transmitted to car body through the subframe mounts. For application of AVC to test car, the control force is required and the actuator for generation of control force should be install on the mounts. For application of AVC to test car, the control force is required and the actuator for generation of control force should be inserted in the mount between subframe and R-MDPS. It requires an extra rework of test car. In this study to study the feasibility for the application of AVC system to R-MDPS, the test jig, which composes of R-MDPS and subframe of test car, is made and is set up in the laboratory. All study on AVC is performed in the laboratory. The proposed method is successfully applied to the active cancellation of vibration at target point of subframe. The developed method is going to be applied to the AVC of real test vehicle.

Study on signal decomposition-based pump noise cancellation method for continuous-wave mud pulse telemetry

Continuous mud pulse telemetry is an advanced downhole data transmission technology in measurement while drilling (MWD) systems. However, various noise interference during transmission has resulted in continuous mud pulse telemetry often encountering difficulties in decoding under harsh working conditions, with the influence of pump noise being the most severe. On the bases of the narrowband signal characteristics of pump noise harmonics and a deep study of the variational mode decomposition (VMD) algorithm, this study proposes an improved version called constant center frequency VMD (CVMD), the mode function center frequencies of which are fixed in the initial state. The denoising performance of the proposed algorithm is thoroughly analyzed by simulating a series of continuous mud pulse signals with different noise intensities under stable/unstable mud pump conditions. Results demonstrate that the CVMD algorithm has an advantage over conventional filters because the former can maximize the preservation of useful information within the critical frequency band. Compared with the Kalman filter based on the same pump noise model, the CVMD algorithm has a higher signal-to-noise ratio, smaller mean square error, higher correlation with the ideal signal, and is also applicable when the pump state is unstable. In addition, the proposed method has been successfully applied to the processing of real-field experimental data transmitted over thousands of meters and disturbed by complex noise. Consequently, a distinctive waveform of the transmitted pulse is obtained, and powerful technical support is provided for the realization of continuous mud pulse downhole data transmission in harsh mud circulation environments.

Millimeter-Wave High-Gain Dual-Polarized Flat Luneburg Lens Antenna with Reflection Cancellation

Because of its good beam coverage and beam scanning abilities, the Luneburg lens (LL) is a promising multibeam antenna for the fifth-generation (5G) wireless communications. However, the conventional LL has a spherical formfactor, exhibiting a large volume, large weight, and curvature surface, all of which limit the adoption of an LL in practice. To alleviate the problem, a flat LL is proposed, with transformation optics to convert the conventional spherical structure into a flat one. However, a high permittivity distribution is usually required in a transformed flat LL, causing of server reflections, which further degenerates the performance of the LL in terms of both gain and efficiency. In this article, a millimeter-wave dual-polarized flat Luneburg lens antenna (FLLA) is proposed following the transformation of optics, and implemented using multilayer PCBs, where a reflection cancellation method is introduced to optimize the multilayer structure to improve its gain and efficiency. The designed FLLA is exemplified in the Ka-band and fed using a dual-polarized patch antenna. The measured results show that the designed FLLA has an impedance bandwidth (|S11| ≤ −10 dB) of 27.5–32.6 GHz, a gain of 16.8–18.8 dBi over the operating band, and a beam scanning range up to ±25°/±24° with a gain loss of 1.72 dB/1.7 dB in either E- or H-plane, respectively.

Improved uniform error bounds on time-splitting methods for the long-time dynamics of the weakly nonlinear Dirac equation

Abstract Improved uniform error bounds on time-splitting methods are rigorously proven for the long-time dynamics of the weakly nonlinear Dirac equation (NLDE), where the nonlinearity strength is characterized by a dimensionless parameter $\varepsilon \in (0, 1]$. We adopt a second-order Strang splitting method to discretize the NLDE in time, and combine with the Fourier pseudospectral method in space for the full-discretization. By employing the regularity compensation oscillation (RCO) technique, where the high frequency modes are controlled by the regularity of the exact solution, and the low frequency modes are analyzed by phase cancellation and energy method, we establish improved uniform error bounds at $O(\varepsilon ^{2}\tau ^{2})$ and $O(h^{m-1}+ \varepsilon ^{2}\tau ^{2})$ for the second-order Strang splitting semidiscretization and full-discretization up to the long-time $T_{\varepsilon } = T/\varepsilon ^{2}$ with $T>0$ fixed, respectively. Furthermore, the numerical scheme and error estimates are extended to an oscillatory NLDE, which propagates waves with $O(\varepsilon ^{2})$ wavelength in time and at $O(\varepsilon ^{-2})$ wave speed in space. Finally, numerical examples verifying our analytical results are given.

Bistatic Sea Clutter Suppression Method Based on Compressed Sensing Optimization

In order to reduce the sea clutter interference in the detection of sea surface targets, we propose a bistatic sea clutter suppression method based on compressed sensing optimization in this paper. The proposed method mitigates the interference effect by reconstructing and cancelling out the sea clutter. Since the fixed sparse base is not always completely applicable for the sparse representation of sea clutter, we propose a sparse base optimization algorithm based on transfer learning to convert the fixed sparse base into an adaptive one. Moreover, we introduce a sensing matrix optimization algorithm to decrease the cross-correlation coefficient between the measurement matrix and the sparse base matrix, which can enhance the signal reconstruction quality. Finally, we use the orthogonal matching pursuit algorithm to reconstruct the sea clutter and employ the reconstructed results to cancel and suppress the sea clutter. The simulation results demonstrate that the proposed method outperforms the traditional methods such as the root time-domain cancellation method and the singular value decomposition method (SVD). Therefore, the proposed method has great practical significance for the detection of bistatic sea surface targets.

Method for Digital Cancellation of System Interference in a Full-Duplex Power Line Communication System

Every year the number of IoT devices is growing, which gives a boost to development of such technology as Power Line Communication (PLC). The term PLC refers to the use of a power supply circuit to transmit data. Full-duplex technology can be applied to increase the data transfer rate in a PLC. The difficulty in using a full duplex is the need to suppress the transmitter’s own signal (interference signal). Analog cancellation and digital cancellation are used for this purpose. Common algorithms for digital cancellation are adaptive algorithms. In this paper adaptive algorithms such as Particle Swarm Optimization (PSO) and Recursive Least Squares (RLS) are considered and a modification of the classical RLS is proposed. The proposed algorithm uses precalculated weight coefficients together with periodic reinitialization of the filter. This avoids the influence of significant changes in the input signal on the further process of filter adaptation and provides the highest level of echo suppression. The proposed algorithm provides an improvement of parameters such as Error Vector Magnitude (EVM) and Signal-to-Interference Ratio (SIR) in comparison with classical RLS and PSO, according to the results of the study.

Time Synchronization and Integration of Bearing Fault Impacts upon Stator Currents

As regards bearing fault detection of induction motors using stator current, anything unrelated to the faults is the “noise”. The noise attenuation leads to an improved estimation of the bearing fault signal. The gist of this study is the integration and synchronization of the three residues obtained from the current noise cancelation methods: Time Shifting and Linear Prediction. This procedure results in a richer bearing fault signal than any of the individual residues through cross-correlation. The developed method efficiently synchronizes and integrates the current residues so that fault characteristic frequencies can be more conspicuous in spectral analysis. Both simulation and experimental results attest to the merit and effectiveness of the developed method in detecting both outer and inner race bearing faults.