Unknown Interference Research Articles

Improved Deep Residual Shrinkage Network for Intelligent Interference Recognition with Unknown Interference.

In complex battlefield environments, flying ad-hoc network (FANET) faces challenges in manually extracting communication interference signal features, a low recognition rate in strong noise environments, and an inability to recognize unknown interference types. To solve these problems, one simple non-local correction shrinkage (SNCS) module is constructed. The SNCS module modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local importance-based pooling (LIP) is introduced to enhance the useful features of interference signals and reduce noise in the downsampling process. Moreover, the joint loss function is constructed by combining the cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed. Meanwhile, the acceptance factor-based unknown class recognition simplified non-local residual shrinkage network (AFUCR-SNRSN) model with the capacity for both known and unknown class recognition is constructed by combining AFUCR and SNRSN. Experimental results show that the recognition accuracy of the AFUCR-SNRSN model is the highest in the scenario of a low jamming to noise ratio (JNR). The accuracy is increased by approximately 4-9% compared with other methods on known class interference signal datasets, and the recognition accuracy reaches 99% when the JNR is -6 dB. At the same time, compared with other methods, the false positive rate (FPR) in recognizing unknown class interference signals drops to 9%.

A Bayesian Approach to Characterize Unknown Interference Power in Wireless Networks

The existence of unknown interference is a prevalent problem in wireless communication networks. Especially in multi-user multiple-input multiple-output (MIMO) networks, where a large number of user equipments are served on the same time-frequency resources, the outage performance may be dominated by the unknown interference arising from scheduling variations in neighboring cells. In this letter, we propose a Bayesian method for modeling the unknown interference power in the uplink of a cellular network. Numerical results show that our method accurately models the distribution of the unknown interference power and can be effectively used for rate adaptation with guaranteed target outage performance.

Localization and Identification of EMI Sources in Shielding Enclosures Based on a Two-Step Source Reconstruction Method

Localization and identification of unknown electromagnetic interference (EMI) sources in shielding enclosures is a great challenge due to the complex electromagnetic environment. In this work, a two-step source reconstruction method is proposed to address this issue. The actual EMI sources are initially modeled by equally distributed equivalent electric dipoles in the shielding enclosure. Effects of surrounding environments are rigorously incorporated using the numerical Green's function which bridges between the equivalent dipoles and planar scanned tangential magnetic near-field. Next, the initial equivalent sources are downsized via evaluating the contribution of each equivalent dipole. The parameters of remaining equivalent dipoles are updated by another source reconstruction process, in which additional equivalent magnetic dipoles are added to improve precision of the equivalent model. The locations and geometries of EMI sources in the shielding enclosure can be identified clearly in terms of the updated equivalent dipoles. Furthermore, the radiated emissions from individual EMI sources can be estimated accurately. The effectiveness of this approach is validated by representative numerical examples.

Single parameter adaptive neural network control for multi-agent deployment with prescribed tracking performance

This study addresses the problem of deploying multi-agent systems using single-parameter adaptive neural network control in time and space where the system is modeled by a parabolic partial differential equation (PDE). We investigate the agent model, simplify the pointwise dynamics using a PDE model, and consider the deployment problem when the number of agents is relatively large. In order for the deployed agents to follow the desired trajectory, we augment the agent dynamics with individual control inputs, accounting for the unknown interference faced by each agent during the deployment process. In the proposed approach, a radial basis function neural network structure is introduced to enhance the systems’ adaptivity under unknown interference. The unknown parameter is estimated via the single-parameter idea for reducing the computation of the entire process and increasing the calculation speed. Asymmetric performance constraints are imposed on the tracking error of the system to ensure that each agent is deployed in the required position. The results of numerical simulation prove the effectiveness of the method.

Second-order calibration-assisted LC-MS method for simultaneous determination of multiple active components and origin difference analysis of vinegar-processed cyperi rhizoma

In this work, a liquid chromatography-mass spectrometry (LC-MS) technique combined with alternating normalization weighted error algorithm (ANWE) method was proposed to realize the simultaneously fast quantification of 9 main active ingredients including isomers and origin difference analysis of vinegar-processed cyperi rhizoma (VCR). Overlapping peaks, similar mass spectra and fragment ions of isomers, and unknown matrix interference were all addressed by the proposed approach, which also yielded quantitatively and qualitatively acceptable results. Their average spiked recoveries range from 83.6% to 103.5%. It is worth mentioning that the proposed method can achieve accurate quantification of isomers α-cyperone and nocatone under simple chromatographic elution conditions. And the quantitative results of PARAFAC and ANWE algorithm were compared. Compared with the LC-MS/MS method, the quantitative results of ANWE-LC-MS method are not significantly different, and the required time is shorter. Meanwhile, the ANWE algorithm was used to resolve the 42 VCR's LC-MS fingerprints and determine the relative concentrations of components. Heatmap analysis reveals the difference in the content of the extracted variables in VCR samples, and the unsupervised hierarchical clustering analysis (HCA) method was used for their origins cluster. In addition, the partial least squares discriminant analysis (PLS-DA) method was adapted for the classification of Guang VCR and Non-Guang VCR accurately and five representative difference marker compounds were screened out with the variable importance in projection (VIP). In a word, this work is anticipated to serve as a reference for the development of quality standards for medications made from VCR or other Chinese herbs, as well as for the study of the origin discrimination.

Augmentations for selective multi-species quantification from infrared spectroscopic data

Sensitivity and selectivity are arguably the two most important qualities in a new sensor design. While many spectroscopic sensors developed in laboratory conditions achieve high sensitivity and selectivity, they are not always applicable to real-world conditions. Challenges in real-world applications come from corruptions like noise and interference. This study leverages machine learning methods for accurate and robust quantification under such corruptions. We propose simple yet effective augmentation strategies that promote robustness against unknown interference. The performance of the proposed augmentations is compared under varying levels of interference and noise. We demonstrate our methodology for a gas sensing application using infrared spectroscopy data. We focus on quantifying common volatile organic compounds (VOCs) in a realistic scenario with several unknown interfering species. The findings of this work put us a step closer to creating a robust and widely-applicable sensing platform.Synopsis: Interference is a major obstacle in the development of reliable spectroscopic gas sensors. This study explores data augmentation strategies to tackle unknown interference and noise to propose a sensing strategy for volatile organic compounds.

Trajectory Tracking Control of Autonomous Vehicles Based on an Improved Sliding Mode Control Scheme

This paper addresses the issue of external unknown environmental interference affecting the trajectory tracking performance and driving stability of autonomous vehicles. This seriously impacts the performance and stability of the vehicle while driving. In order to provide precise, reliable, and safe trajectory tracking performance for autonomous vehicles, this paper proposes a recursive integral terminal sliding mode control (RITSMC) method. The proposed RITSMC combines the advantages of recursive integral sliding mode (RISM), terminal sliding mode (TSM), and adaptive algorithms, and can effectively achieve precise trajectory tracking and driving stability of autonomous vehicles. Furthermore, compared with traditional methods, an adaptive algorithm is introduced on the recursive sliding surface to enable real-time adaptation of the control parameters of the recursive controller, further improving the trajectory tracking accuracy and driving stability of autonomous vehicles. The stability of this control system is demonstrated by using a Lyapunov function. Finally, multiple simulation tests were conducted on different lane speeds on both wet and dry asphalt road sections. By comparing the simulation results, it was found that the proposed RITSMC exhibits excellent performance in terms of the precision of tracking trajectories and the stability of driving, in contrast to traditional sliding mode controllers (SMC) and integral terminal sliding mode controllers (ITSMC).

Event-triggered adaptive finite time trajectory tracking control for Underactuated Vessel considering unknown time-varying disturbances

Abstract This paper aims to solve the finite-time trajectory tracking problem of underactuated surface ships under the influence of dynamic uncertainty, unknown external time-varying interference and limited communication resources, an event-triggered robust adaptive finite-time trajectory tracking control method for underactuated ships is designed by combing the existing trajectory tracking control methods and engineering needs in actual navigation. It can obviously improve the tracking accuracy of the ship, and complete the tracking task safely and efficiently. This scheme transforms the composite uncertain vector synthesized by uncertain parameters and external disturbances into a linear parameterized form. Next, considering the limitation of communication resources, a relative threshold event triggering mechanism is introduced to further extend the triggering time interval on the basis of the fixed threshold triggering scheme. Then, an event-triggered robust adaptive finite-time trajectory tracking control scheme is designed for underactuated ships, and a rigorous stability proof is provided for the finite-time trajectory tracking control scheme designed above through Lyapunov stability theory. Simulation experiment is carried out in MATLAB and the results show that the finite-time trajectory tracking control scheme proposed in this paper can effectively solve the problems of dynamic uncertainty, unknown time-varying interference from the outside world, and limitation of communication resources. This paper can provide theoretical support for the autonomous navigation of intelligent ships.

Research on control of electro-hydraulic servo system based on third-order linear ADRC

PurposeThe purpose of this paper is to design a third-order linear active disturbance rejection controller (LADRC) to improve the response characteristics and robustness of the electrohydraulic servo system.Design/methodology/approachThe LADRC was designed by replacing the nonlinear functions in each part of ADRC with linear functions or linear combinations, and the parameters of each part of the LADRC were connected with their bandwidth through the pole configuration method to reduce the required tuning parameters, and used an improved grey wolf optimizer to tune the LADRC parameters.FindingsThe anti-interference control simulation and experiment on the LADRC, ADRC and proportion integration differentiation (PID) were carried out to test the robustness, anti-interference ability and superiority of the designed LADRC. The simulation and experiment results showed that the LADRC control and anti-interference control had excellent performance, and because of its simple structure and fewer parameters, LADRC was easier to implement and had a better control effect and anti-interference.Originality/valueFor the problems of parameter perturbation, unknown interference and inaccurate model in the electrohydraulic position servo system, the designed third-order LADRC has good tracking accuracy and anti-interference, has few parameters and is conducive to promotion.

Sparsity-Based Classification Approaches for Radar Data in the Presence of Clutter Edges and Discretes

Modern radars face challenging scenarios characterized by high levels of heterogeneity that make conventional detection/estimation algorithms no longer effective since most of them rely on the well-known homogeneous environment. In this paper, we propose innovative classification schemes capable of identifying the radar operating scenario in terms of clutter properties. To this end, different situations are accounted for at the design stage ranging from the homogeneous environment to the simultaneous presence of clutter edges and clutter discretes/outliers. Then, we conceive decision rules based upon the so-called penalized log-likelihood ratio test that exploits the inherent sparse nature of the observed scene. In this context, two different sparsity-promoting priors are used to model the behavior of clutter discretes/outliers and the unknown parameters are estimated by means of cyclic procedures. The performance analysis, conducted on both simulated and real-recorded data, highlights the effectiveness of the proposed classifiers at least for the considered operating parameter values. Remarkably, radar systems can take advantage of such classification results by selecting homogeneous data that can yield reliable estimates (namely, that are functions of homogeneous data) of the unknown interference parameters and, hence, enhanced detection performance with respect to the case where unclassified heterogeneous data are used.

266 nm ultraviolet communication under unknown interference using UVC micro-LED.

Ultraviolet C (UVC) micro light-emitting diode (LED) can achieve symbol communication rate up to 100Msps at distance 40 meters without transmitter-side lens to guarantee certain communication mobility. We consider what we believe to be a new scenario where high speed UV communciation is realized under unknown low-rate interference. The signal amplitude properties are characterized, and the interference intensity is categorized into three cases, namely weak, medium and high interference intensity. The achievable transmission rates for the three cases are derived, where the achievable transmission rate for medium interference intensity can approach those in weak interference intensity and strong interference intensity cases. We provide Gaussian approximation and related log-likelihood ratio (LLR) calculation, which are fed into the subsequent message-passing decoder. In the experiment, the data is transmitted with symbol rate 20 Msps under unknown interference with symbol rate 1 Msps, received by one photomultiplier tube (PMT). Experimental results show that the proposed interference symbol estimation approach shows negligibly higher bit error rate (BER) compared with those with perfect knowledge on the interference symbols.

DDPG-based controlling algorithm for upper limb prosthetic shoulder joint

ABSTRACT The development of intelligent prostheses has effectively improved the life of amputees. However, the current prosthetics mainly focus on restoring the basic mobility of amputees, without considering the use habits of the wearer and the diversity of arm movements, which makes the unknown interference and complex control requirements in daily life an obstacle to the use of prosthetics. To solve this problem, this paper proposes a combination method of adaptive control algorithms of bionic arm shoulder joint based on DDPG to realise intelligent control of the shoulder joint of upper limb prosthesis. Based on using adaptive control to reduce the interference of external variables, the accuracy of the joint module system is improved through reinforcement learning. The results show that the controller has a good effect on improving the dynamic performance of the mechanical system and can be widely used in bionic mechanical control.

Design of High Precision Interval Observer for Robot System

In order to solve the problem of parameter uncertainty and unknown external interference of wheeled mobile robots (WMR) in a complex environment, the design of a high-precision interval observer for the robot system is proposed. In this paper, the kinematics and dynamics model of a wheeled mobile robot is derived first, and then the control strategy of high-precision interval observer is introduced to estimate and compensate for the unknown state and uncertainty of the system in real-time, which realizes the robustness of the system to disturbance and high adaptability to the environment. The stability of the system is proved by Lyapunov’s theory. The experimental results show that other methods based on coordinate transformation, though the design conditions are relaxed to a certain extent, bring some conservatism. The method proposed in this paper can obtain more accurate interval estimation, so the performance of the method proposed in this paper is better. In conclusion, the control method proposed in this paper can make the mobile robot system have good tracking control performance and strong robustness.

Adaptive Predefined-Time Sliding Mode Control for QUADROTOR Formation with Obstacle and Inter-Quadrotor Avoidance

In this paper, aiming at the problem of control and obstacle avoidance in quadrotor formation when mathematical modeling is not accurate, the artificial potential field method with virtual force is used to plan the obstacle avoidance path of quadrotor formation to solve the problem that the artificial potential field method may fall into local optimal. The adaptive predefined-time sliding mode control algorithm based on RBF neural networks enables the quadrotor formation to track the planned trajectory in a predetermined time and also adaptively estimates the unknown interference in the mathematical model of the quadrotor to improve the control performance. Through theoretical derivation and simulation experiments, this study verified that the proposed algorithm can make the planned trajectory of the quadrotor formation avoid obstacles and make the error between the true trajectory and the planned trajectory converge within a predetermined time under the premise of adaptive estimation of unknown interference in the quadrotor model.

Performance Comparison of Two Subspace-Based GLRTs for Rank-1 Signal Detection in Unknown Interference With Multiple Observations

This article compares the receiver operating characteristics (ROC) of two subspace-based generalized likelihood ratio tests (GLRTs) referred to in this article as the unconstrained and constrained GLRTs. The tests are derived for slightly different assumptions and are applied to detect an unknown rank-1 signal that belongs to a known <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula> -dimensional subspace in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {C}^{N \times 1}$</tex-math></inline-formula> . The receiver is given multiple (say <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P$</tex-math></inline-formula> ) statistically independent observations of unknown multivariate zero-mean complex Gaussian interference-plus-noise that may contain a rank-1 signal as the test matrix and signal-free training data to estimate the interference-plus-noise covariance matrix. The two detectors are shown to coincide when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M = 1$</tex-math></inline-formula> and/or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P = 1$</tex-math></inline-formula> . New analytical expressions for the probability of false alarm and probability of detection are derived for the unconstrained GLRT and to the best of our knowledge, no such analytical expressions are available for evaluating the performance of the constrained GLRT in the general case, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M &gt; 1$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$P &gt; 1$</tex-math></inline-formula> . The ROC of the unconstrained GLRT is a lower bound on the performance of the constrained GLRT. For small signal-to-interference-plus-noise ratios (SINRs) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M/N \ll 1$</tex-math></inline-formula> , the lower bound is tight, which makes the derived analytical results both interesting and useful.

Orthogonal and complementary measurements of properties of drug products containing nanomaterials.

Quality control of pharmaceutical and biopharmaceutical products, and verification of their safety and efficacy, depends on reliable measurements of critical quality attributes (CQAs). The task becomes particularly challenging for drug products and vaccines containing nanomaterials, where multiple complex CQAs must be identified and monitored. To reduce (i) the risk of measurement bias and (ii) the uncertainty in decision-making during product development, the combination of orthogonal and complementary analytical techniques are generally recommended by regulators. However, despite frequent reference to "orthogonal" and "complementary" in guidance documents, neither term is clearly defined. How does one determine if two analytical methods are orthogonal or complementary to one another? Definitions are needed to design a robust characterization strategy aligned to regulatory needs. Definitions for "orthogonal" and "complementary" are proposed that are compatible with existing metrological terminology and are applicable to complex measurement problems. Orthogonal methods target the quantitative evaluation of the true value of a product attribute to address unknown bias or interference. Complementary measurements include a broader scope of methods that reinforce each other to support a common decision. Examples of the application of these terms are presented, with a focus on measurement of physical properties of nano-enabled drug products, including liposomes and polymeric nanoparticles for cancer treatment, lipid-based nanoparticles (LNPs) and virus-like particles for nucleic acid delivery. The proposed framework represents a first step in advancing the assessment of the orthogonality and complementarity of two measurements and it can potentially serve as the basis for a future international standard. This framework may help product developers to implement more efficient product characterization strategies, accelerate the introduction of novel medicines to the clinic and be applicable to other therapeutics beyond nanomaterial-containing pharmaceuticals.

Revisiting Link Quality Metrics and Models for Multichannel Low-Power Lossy Networks.

Multichannel communication has great potential in environments with unknown interference patterns. However, existing link quality metrics and models are generally established and verified under a single-channel scenario, which does not consider the impacts of radio interference and channel change. Therefore, it is hard to directly judge whether these metrics and models are still valid under a multichannel scenario. This paper empirically analyzes the applicability of popular link quality metrics and models in multiple channels with different interference levels. Results show that the link quality estimation (LQE) capability of traditional metrics will be affected by the interference level of the channel, which makes the conclusions obtained under a single-channel scenario no longer valid. Meanwhile, traditional LQE models are basically not adaptive to radio interference and channel change. They are only valid for channels with similar interference under which they are modeled. If these models are directly used under a multichannel scenario, the link quality will be overestimated inevitably. In other words, traditional LQE metrics and models cannot be directly used in the multichannel scenario. It is necessary to deeply analyze the statistical characteristics of popular link quality metrics in multiple typical channels and design channel and interference adaptive metrics and models to support effective multichannel communication.

A Day/Night Leader-Following Method Based on Adaptive Federated Filter for Quadruped Robots

The quadruped robots have superior adaptability to complex terrains, compared with tracked and wheeled robots. Therefore, leader-following can help quadruped robots accomplish long-distance transportation tasks. However, long-term following has to face the change of day and night as well as the presence of interference. To solve this problem, we present a day/night leader-following method for quadruped robots toward robustness and fault-tolerant person following in complex environments. In this approach, we construct an Adaptive Federated Filter algorithm framework, which fuses the visual leader-following method and the LiDAR detection algorithm based on reflective intensity. Moreover, the framework uses the Kalman filter and adaptively adjusts the information sharing factor according to the light condition. In particular, the framework uses fault detection and multisensors information to stably achieve day/night leader-following. The approach is experimentally verified on the quadruped robot SDU-150 (Shandong University, Shandong, China). Extensive experiments reveal that robots can identify leaders stably and effectively indoors and outdoors with illumination variations and unknown interference day and night.

Stochastic fuzzy predictive fault‐tolerant control for time‐delay nonlinear system with actuator fault under a certain probability

AbstractOwing to the characteristics of time‐delay, fault randomness, uncertainty, nonlinearity and unknown interference in industrial production processes, a stochastic fuzzy predictive fault‐tolerant control algorithm is put forward based on the traditional fault‐tolerant control algorithm. The main idea of this algorithm is to integrate actuator fault into the established Takagi‐Sugeno (T‐S) model to solve actuator fault under a certain probability for the nonlinear industrial processes with time‐varying delays by combining stochastic control theory and relevant theorems. First, the actuator fault under a certain probability is considered as a T‐S model, which can be used as a description of the fault situation in the nonlinear industrial processes. Afterwards, the augmented state space model is established through integrating state deviation and output tracking error. Second, the stochastic fuzzy predictive fault‐tolerant control law can be designed on the basis of the augmented model. Meanwhile, the actuator control mode for different faults is given. If the actuator fault is under a small probability, the control mode is switched to normal control; if the actuator fault is under a large probability, the control mode is switched to fault‐tolerant control. To this end, this control algorithm can reduce energy consumption and raw material consumption. On this basis, the designed control law can be solved by using the given stochastic stability conditions in terms of linear matrix inequality. Finally, the temperature control process of a strongly nonlinear continuous stirred tank reactor is selected as a simulation object to prove the feasibility and effectivity of this algorithm.

Adaptive Control for Joint Module of Bionic Arms

Minimizing the influence of unknown external disturbances while satisfying complex control requirements in daily life is one of the difficulties of bionic arm control. However, the control of modular joints often ignores the dynamic characteristics and coupling relationship of the system, which can not meet the control requirements of a bionic arm prosthesis. In addition, the large vibration caused by unknown interference is not conducive to human-computer interaction. In order to solve this problem, we proposed an adaptive feedback control (AFC) method based on magnetic field-oriented control for the joint module to realize intelligent control of the bionic arm. The disturbance of the joint module is obtained by real-time parameter calculation. At the same time, the controller regulated the output voltage to eliminate the influence of uncertain disturbances. Theoretical analysis and simulation results show that the controller has a good effect on improving the dynamic performance of the joint module system, and maintaining the stability of the system in the interference, satisfying the requirements of bionic arm control.