Discrete Filter Research Articles

MonoVisual3DFilter: 3D tomatoes’ localisation with monocular cameras using histogram filters

Abstract Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects’ spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state to answer the research question: “How can we use and control monocular sensors to perceive objects’ position in the 3D task space?” Towards this aim, we approached histogram filters (Bayesian discrete filters) to estimate the position of tomatoes in the tomato plant through the algorithm MonoVisual3DFilter. Two kernel filters were studied: the square kernel and the Gaussian kernel. The implemented algorithm was essayed in simulation, with and without Gaussian noise and random noise, and in a testbed at laboratory conditions. The algorithm reported a mean absolute error lower than 10 mm in simulation and 20 mm in the testbed at laboratory conditions with an assessing distance of about 0.5 m. So, the results are viable for real environments and should be improved at closer distances.

Angle-of-attack and angle-of-sideslip estimation and complementary filter design for civil aircraft

Angle-of-Attack (AOA) and angle-of-sideslip (AOS) are critical flight parameters affecting the flight safety, and their accuracy and reliability directly impact the operating status and performance of some significant airborne systems. To enhance the redundancy and accuracy of AOA and AOS, this article investigates the problem of the airflow angles estimation and complementary filter design for civil aircraft. Specifically, an extended Kalman filter based AOA and AOS estimation method considering acceleration correction is developed to increase the redundancy. Subsequently, a novel inertial AOA and inertial AOS calculation method using attitude angles, azimuth angle, and flight path angle is introduced, and two schemes for designing the discrete complementary filter based on Tustin transform are presented to improve the accuracy. Through simulations, the developed algorithms are verified, and the results illustrate that the AOA estimation error is within ± 0.6°, and the AOS estimation error is within ± 0.3°.

Integrating discrete sub-grid filters with discretization-corrected particle strength exchange method for high Reynolds number flow simulations

Integrating discrete sub-grid filters with discretization-corrected particle strength exchange method for high Reynolds number flow simulations

IMPLEMENTATION OF A MATCHED FILTER FOR LFM SIGNALS USING A SYSTEM-ON-CHIP BASED SDR TRANSCEIVER

Background. Creation of communication channels with effective protection against intentional interference is the most urgent task for communication channels of unmanned aerial vehicles (UAVs). An effective solution is the use of signals with spectrum expansion, which allows reducing the impact of intentional interference on control and telemetry channels, thereby increasing the survivability of UAVs. The most optimal is the use of LCM modulation, which became possible with the development of SDR and SoC technologies. The main problem of using LFM modulation is the complexity of implementing matched filters. This requires research to create optimal digital implementations of matched filters. Objective. The purpose of the paper is to determine the parameters of the LFM signal for the implementation of the communication system using CSS modulation on the SDR transceiver. Optimal implementation of discrete matched filter (correlator) for CSS BOK modulation on FPGA (SoC). Methods. Analysis of the possibilities of implementing CSS BOK modulation for building a communication system based on SDR transceivers. Analysis of optimal options for building a discrete matched filter (correlator) for CSS BOK modulation on FPGA (SoC) and its implementation. Testing on a laboratory bench. Results The possibility of creating a communication system based on an SDR transceiver using CSS BOK modulation has been confirmed. The optimal parameters of the LFM signal, which can be implemented, have been studied. An implementation of a discrete matched filter (correlator) for CSS BOK modulation on FPGA has been developed. Testing carried out on a laboratory bench using SDR transceivers AD9361 (AD9364) showed the correctness and effectiveness of the decisions made. Conclusions The parameters of the LFM signal for the implementation in the communication system using CSS modulation on the SDR transceiver have been studied and determined. It was determined that when using the AD9361 (AD9364) transceiver, it is possible to implement an LFM signal with a band of up to 25 MHz, while the optimal symbol duration will be 16.6667 µsec at 1024 counts and a sampling frequency of 61.44 MHz. It was determined that the best CSS modulation method for creating a communication system is the binary orthogonal manipulation (BOK) method, which allows you to effectively use the advantages of SDR technologies, in particular, the ease of working with quadrature channels and, as a consequence, the ease of implementing phase synchronization. It has been determined that the best method of implementing a matched filter (correlator) on the basis of FPGA (SoC) is the application of fast convolution based on the fast Fourier transform. The developed implementation of a discrete matched filter (correlator) for CSS BOK modulation on FPGA showed the possibility of implementing a communication system with CSS BOK modulation on an SDR transceiver in full accordance with the research results. Experimental studies were carried out on a laboratory bench using SDR transceivers AD9361 (AD9364), which fully confirmed the results of simulation modelling.

Совершенствование системы защиты от юза пассажирского электровоза

Objective: improving algorithms for protection against skidding and slipping using new methods for detecting excessive slipping of locomotive wheel pairs, allowing for early detection of the moment of loss of adhesion. Methods: the effectiveness of an anti-skid system is largely determined by the reliable detection of the occurrence of excessive wheel slip. Solving this problem requires the development of methods for isolating signals from the angular velocity of rotation and angular acceleration of the wheelset, taking into account dynamic processes in the mechanical part of the locomotive and interference in the measurement channel. Therefore, the article discusses the issues of processing rotation speed signals, extracting information about the amount of excess slip and angular acceleration of the wheelsets for use in anti-skid and skidding systems. One of the criteria that can improve the reliability of skid detection is proposed to use the “Estimated time before wheel locking”. Its value allows you to define the estimated time before the wheel jams (stops rotation). The introduction of this criterion makes it possible to more accurately determine the risk of a complete stop of wheelset rotation and increases the reliability of determining the occurrence of skidding. Results: it has been established that to eliminate errors in the measurement channel and isolate the signal about the angular acceleration of the wheelset in conditions of the presence of oscillations caused by the presence of the effects of the spatial dynamics of the locomotive, it is advisable to use a discrete Kalman filter. Signal processing using a Kalman filter in the presence of a reference signal makes it possible to significantly reduce the influence of spatial vibrations of the undercarriage of an electric locomotive when moving along a track with irregularities on the extracted signals of the angular velocity of rotation and angular acceleration of the wheelset. This makes it possible to reduce response thresholds and identify skidding and skidding before significant excess slip occurs. Practical importance: the necessity of using complex criteria based on the analysis of not only the slipping speed of wheelsets, but also angular accelerations, is shown to identify excessive slipping of locomotive wheelsets. Their use in the locomotive control system makes it possible to detect loss of adhesion in the early stages of slipping and skidding and improve the use of the adhesion weight of locomotives

Implementation of unknown parameter estimation procedure for hybrid and discrete non‐linear systems

AbstractThe application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non‐linear filter problems, when prediction equations are continuous‐time and the update equations are discrete‐time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete‐time non‐linear filter problems, when prediction equations and update equations are discrete‐time. In order to assess the performance of the filters, the authors consider the non‐linear dynamics for a re‐entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re‐entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re‐entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins‐Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter.

Overlap Time Compensation and Characteristic Analysis for Current Source Photovoltaic Grid-Connected Inverter

In the current source photovoltaic grid-connected system, to prevent the DC-link inductor from incurring an opening circuit fault, it is necessary to include the overlap time in the switching signals. However, current error and serious harmonic distortion in the inverter-side and grid-side currents are generated, which will cause additional losses and reduce the power quality of the grid, so it is important to compensate for the current error caused by the overlap time. In this paper, the relationship between the nonlinear current errors caused by the overlap time and the AC-side voltage is analyzed. Then, the mathematical expression of the low-order harmonics with losses caused by the overlap time is derived. On this basis, a current error compensation method with a discrete filter of AC-side voltage is proposed. Finally, a simulation and experiment are carried out to verify the correctness and effectiveness of the theoretical analysis and compensation scheme presented in this paper. With an overlap time of 3 μs, the THD of the grid-side current decreases from 5.93% to 1.59% after compensation.

Multiple Discrete Adaptive Filter Based Position Error Reduction for Sensorless IPMSM Drives

The inverter nonlinearity and the flux spatial harmonics will lead to noticeable (6κ±1) harmonics in the observed back electromotive force (EMF), causing (6κ) pulsations in the rotor position estimation error and deteriorating the performance of sensorless interior permanent magnet synchronous motor (IPMSM) drives. To improve the sensorless IPMSM control accuracy, a multiple discrete adaptive filter (MDAF) embedded between sliding mode observer (SMO) and normalized quadrature phase-locked loop (PLL) is adopted to solve the above problem in this paper. The MDAF consists of a fundamental extraction block and a harmonic cancellation block. Harmonic cancellation works similarly to fundamental extraction, simply by removing the dominant 5th and 7th harmonics from the distorted back-EMF in a frequency multiplicative manner. The proposed algorithm is designed directly in the discrete domain, avoiding the complex discretization process. The open-loop amplitude-frequency characteristic curves show that the method is able to achieve infinite gain exactly at the expected frequency, thus allowing closed-loop zero steady-state error tracking of the back-EMF and providing higher precision for harmonic estimation. The excellent performance of the suggested method is validated based on a 1.5kW sensorless IPMSM control system.

Joint Estimation of SOC and SOH for Lithium-Ion Batteries Based on Dual Adaptive Central Difference H-Infinity Filter

The accurate estimation of the state-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries is crucial for the safe and reliable operation of battery systems. In order to overcome the practical problems of low accuracy, slow convergence and insufficient robustness in the existing joint estimation algorithms of SOC and SOH, a Dual Adaptive Central Difference H-Infinity Filter algorithm is proposed. Firstly, the Forgetting Factor Recursive Least Squares (FFRLS) algorithm is employed for parameter identification, and an inner loop with multiple updates of the parameter estimation vector is added to improve the accuracy of parameter identification. Secondly, the capacity is selected as the characterization of SOH, and the open circuit voltage and capacity are used as the state variables for capacity estimation to improve its convergence speed. Meanwhile, considering the interaction between SOC and SOH, the state space equations of SOC and SOH estimation are established. Moreover, the proposed algorithm introduces a robust discrete H-infinity filter equation to improve the measurement update on the basis of the central differential Kalman filter with good accuracy, and combines the Sage–Husa adaptive filter to achieve the joint estimation of SOC and SOH. Finally, under Urban Dynamometer Driving Schedule (UDDS) and Highway Fuel Economy Test (HWFET) conditions, the SOC estimation errors are 0.5% and 0.63%, and the SOH maximum estimation errors are 0.73% and 0.86%, indicating that the proposed algorithm has higher accuracy compared to the traditional algorithm. The experimental results at different initial values of capacity and SOC demonstrate that the proposed algorithm showcases enhanced convergence speed and robustness.

NIRK-based mixed-type accurate continuous–discrete Gaussian filters with deterministically sampled expectation and covariance for state estimation in continuous-time stochastic process models with discrete measurements

This paper advances further the idea of overall continuous–discrete Gaussian filtering with deterministically sampled expectation and covariance towards efficient mixed-type accurate Kalman-like schemes based on self-regulated Ordinary Differential Equation (ODE) solvers with automatic local and global error controls intended for treating continuous-time nonlinear stochastic process models with discrete measurements. The universal state estimation algorithms of such sort designed recently by Kulikov and Kulikova (2022) possess three potential limitations, which might affect their successful applications in practice. First, these deal with complicated Moment Differential Equations (MDEs) and, hence, can be rather time-consuming in estimating stochastic process models of large size, that compromises their efficiency for online computations. Second, the aforementioned filters do not employ any kind of global error regulation and, hence, can be insufficiently accurate in solving difficult MDEs arisen. Third, these are hardly effective in treating stiff continuous–discrete stochastic systems. Below, all the listed issues are addressed and solved via utilization of the much simpler but more robust MDEs arisen in the usual continuous–discrete Extended Kalman Filter (EKF) within the mixed-type accurate filtering approach initiated by Kulikova and Kulikov (2015), which is rooted in the variable-stepsize Nested Implicit Runge–Kutta (NIRK) methods. For the sake of stability of our novel methods to the numerical integration and round-off errors, we devise square-root versions of the mixed-type Gaussian filters, which are based either on hyperbolic QR factorizations or on hyperbolic Singular Value Decompositions (SVDs). Performances of these new mixed-type square-root Gaussian filters are assessed and compared to their original (non-mixed-type) counterparts as well as to their non-square-root versions in a simulated non-stiff ill-conditioned radar tracking scenario and on a stiff stochastic Oregonator reaction.

Suppression of Quantum Sensor Noise using Kalman Filter for Improved Sensitivity of Single‐Beam Atomic Magnetometers

AbstractSingle‐beam atomic magnetometers herald a new era of high‐precision magnetic field sensing, with applications spanning fundamental physics to biomagnetism. Nevertheless, their utility is often curtailed by quantum sensor noise, encompassing both technical and quantum‐mechanical noise. This research delves into the potential of the Kalman filter as a tool to subdue quantum sensor noise, thereby augmenting the sensitivity of single‐beam atomic magnetometers. Quantum‐mechanical noise is integrated into the system model as the process noise and measurement noise, and a discrete Kalman filter equipped with a time delay variable is employed. The findings reveal that the time delay variable significantly influences temporal signal tracing, while the discrete Kalman filter enhances sensitivity performance in frequency domain analysis, bypassing the typical sensitivity and time resolution trade‐off encountered in coherent sensing strategies. Partial‐knowledge signal scenarios are also taken into account, wherein a polynomial model is proposed to expansively render the discrete Kalman filter more relevant and adaptable to real‐world situations. Collectively, through experimentation involving sine‐like, non‐Gaussian, and medical magnetocardiography (MCG) signals, our results underscore the promising potential of the Kalman filter in enhancing the sensitivity of atomic magnetometers for practical sensing applications.

Indirect Adaptive Control Using Neural Network and Discrete Extended Kalman Filter for Wheeled Mobile Robot

This paper presents a novel approach to address the challenges associated with the trajectory tracking control of wheeled mobile robots (WMRs). The proposed control approach is based on an indirect adaptive control PID using a neural network and discrete extended Kalman filter (IAPIDNN-DEKF). The proposed IAPIDNN-DEKF scheme uses the NN to identify the system Jacobian, which is used for tuning the PID gains using the stochastic gradient descent algorithm (SGD). The DEKF is proposed for state estimation (localization), and the NN adaptation improves the tracking error performance. By augmenting the state vector, the NN captures higher-order dynamics, enabling more accurate estimations, which improves trajectory tracking. Simulation studies in which a WMR is used in different scenarios are conducted to evaluate the effectiveness of the IAPIDNN-DEKF control. In order to demonstrate the effectiveness of the IAPIDNN-DEKF control, its performance is compared with direct adaptive NN (DA-NN) control, backstepping control (BSC) and an adaptive PID. On lemniscate, IAPIDNN-DEKF achieves RMSE values of 0.078769, 0.12086 and 0.1672. On sinusoidal trajectories, the method yields RMSE values of 0.01233, 0.015138 and 0.088707, and on sinusoidal with perturbation, RMSE values are 0.021495, 0.016504 and 0.090142 in x, y and θ, respectively. These results demonstrate the superior performance of IAPIDNN-DEKF for achieving accurate control and state estimation. The proposed IAPIDNN-DEKF offers advantages in terms of accurate estimation, adaptability to dynamic environments and computational efficiency. This research contributes to the advancement of robust control techniques for WMRs and showcases the potential of IAPIDNN-DEKF to enhance trajectory tracking and state estimation capabilities in real-world applications.

Wavelet Based Energy Extraction Method for Determining Power Quality Disturbances

In this paper, energy of wavelet coefficients are used for determining power quality disturbances (PQD) which are important for power systems. These power quality disturbances are voltage with harmonics, transient and flicker. After analyzing energy coeffients based on wavelet transform, these PQD are compared with healty condition. 50 Hz pure sine is chosen as reference. These signals are generated by using MATLAB. Sampling frequency is 25.6 kHz. Energy distribution of detail coefficients are obtained by using 12 level Daubechies 4 discrete wavelet filter. Parseval theorem is applied to wavelet coefficients for obtaining energy distribution of detail coefficients of these relevant disturbances in different resolution levels. Satisfactory results are taken visually when examining energy distrubutions of these PQD. Also it is observed that these PQD can be distinguished visually when their amplitudes are increased. Energy coefficients of PQD and amplitude of PQD are in direct propotion.

A High-Performance, Low-Cost, and Integrated Hairpin Topology RF Switched Filter Bank for Radar Applications.

Switched filter banks find widespread application in frequency-hopping radar systems and communication networks with multiple operating frequencies, especially in situations demanding elevated filter element isolation. In this paper, the design and implementation of a highly isolated switchable narrow-bandpass filter bank architecture using hairpin microstrip topology is presented. The filter bank has four discrete bandpass filters with passbands of 2.0-2.2 GHz, 2.3-2.5 GHz, 3.1-3.3 GHz, and 3.9-4.1 GHz. These filters span the radar S-frequency band (2.0-4.0 GHz). In order to switch between channels with a switching speed of nanoseconds, low-loss and highly isolated SP4T switches are implemented. Advanced design system (ADS) software is used to design the various filter functionalities, and the entire system is tested on a vector network analyzer (VNA). The proposed architecture makes it much easier to put the filter bank into practice and switch it to the desired frequency, which is useful for radar receiver applications.

Efficient Harmonic Neural Networks With Compound Discrete Cosine Transform Filters and Shared Reconstruction Filters.

The harmonic neural network (HNN) learns a combination of discrete cosine transform (DCT) filters to obtain an integrated feature from all spectra in the frequency domain. HNN, however, faces two challenges in learning and inference processes. First, the spectrum feature learned by HNN is insufficient and limited because the number of DCT filters is much smaller than that of feature maps. In addition, the number of parameters and the computation costs of HNN are significantly high because the intermediate spectrum layers are expanded multiple times. These two challenges will severely harm the performance and efficiency of HNN. To solve these problems, we first propose the compound DCT (C-DCT) filters integrating the nearest DCT filters to retrieve rich spectrum features to improve the performance. To significantly reduce the model size and computation complexity for improving the efficiency, the shared reconstruction filter is then proposed to share and dynamically drop the meta-filters in every frequency branch. Integrating the C-DCT filters with the shared reconstruction filters, the efficient harmonic network (EH-Net) is introduced. Extensive experiments on different datasets demonstrate that the proposed EH-Nets can effectively reduce the model size and computation complexity while maintaining the model performance. The code has been released at https://github.com/zhangle408/EH-Nets.

Path Planning for a Cooperative Navigation Aid Vehicle to Assist Multiple Agents Sequentially

This paper considers planning a path for a single underwater cooperative navigation aid (CNA) vehicle to sequentially aid a set of N agents to minimize average navigation uncertainty. Both the CNA and agents are modeled as constant-velocity vehicles. The agents travel along known nominal trajectories and the CNA plans a path to sequentially intercept them. Navigation aiding is modeled by a scalar discrete time Kalman filter. During path planning, the CNA considers surfacing to reduce its own navigation uncertainty. A greedy planning algorithm is proposed that uses a heuristic to schedule agents to the CNA that is based on the optimal time-to-aid, the overall navigation uncertainty reduction, and the transit time. The approach is compared to an optimal (exhaustive enumeration) algorithm through a Monte Carlo experiment with randomized agent trajectories and initial navigation uncertainty.

АНАЛИЗ ПРИНЦИПОВ ОБРАБОТКИ НАВИГАЦИОННОЙ ИНФОРМАЦИИ И ПОСТРОЕНИЯ РАБОЧЕЙ ЗОНЫ МНОГОПОЗИЦИОННОЙ СИСТЕМЫ НАБЛЮДЕНИЯ

The use of modern surveillance equipment in accordance with ICAO recommendations is aimed at improving the efficiency of air transportation, increasing the capacity of airspace and airfields, and improving the safety of flights and ground operations. The introduction in the Russian Federation of a multi-position surveillance system (MPSS) based on automatic dependent surveillance of the broadcast type (ADS-B) is intended to facilitate the implementation of state and regional aviation development programs in terms of creating conditions for improving flight safety, availability and quality of air navigation services for airspace users. Therefore, the analysis of the principles of constructing a working area and processing navigation information in a multi-position surveillance system for improving the accuracy of aircraft position-fixing is an urgent research task. An approach to improving the efficiency of the MPSS operation when processing information in conditions of noise and interference has been considered. Analysis of the results of modeling the proposed algorithm based on the discrete Kalman filter shows high accuracy of estimating the planned coordinates of an aircraft. Specialized software has been developed to automate the process of calculating and constructing working areas of a multi-position surveillance system.

Notice of Violation of IEEE Publication Principles: Washout Filter-Based Distributed Resilient Current Control for Cyber-Physical Microgrids

Notice of Violation of IEEE Publication Principles: <br><br>“Washout Filter-Based Distributed Resilient Current Control for Cyber-Physical Microgrids," <br>by X. Lu and J. Lai, <br> in IEEE Systems Journal, Early Access <br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br>"A Combined Distributed Cooperative Method and Washout Filter-Based Method for Power Sharing and Voltage Balancing in Bipolar DC Microgrids" <br> by Ramin Babazadeh-Dizaji, Mohsen Hamzeh, Nima Mahdian Dehkordi, <br>submitted to IEEE Transactions on Energy Conversion, August 2020. <br><br> <br/> This article proposes a washout filter-based distributed resilient current control scheme that can achieve current sharing of distributed generations (DG) and voltage restoration of dc bus almost surely under communication channel noise interferences. Since the cyber channels are exposed to inherent noise interferences, the stability and reliability of cyber-physical dc microgrids may terribly be reduced. To dispel the adverse influences of noise interferences, a novel discrete time washout filter is developed. Accordingly, a discrete-time-distributed noise-resiliency control algorithm is developed for dc microgrids based on washout filters, in which only the current state variable is allowed to exchange among DGs with the local decentralized voltage controller. This makes the proposed control mode different from the conventional parallel mode usually requiring a leader–follower consensus-based voltage control by exchanging two state variables (i.e., terminal voltage and current output). Thus, the proposed control mode decreases the complexity to the formulation and enhances the robustness to the leader communication failure. By introducing stochastic theory and Lyapunov stability function, the sufficient conditions considering noise interferences is derived to ensure the stability operation of the whole closed-loop dc network. The obtained results show the effectiveness of the proposed strategy by a tested dc microgrid in OPAL-RT real-time simulator.

A Novel Prediction Method of Transfer-Assisted Action Oriented to Individual Differences for the Excretion Care Robot.

The excretion care robot's (ECR) accurate recognition of transfer-assisted actions is crucial during its usage. However, transfer action recognition is a challenging task, especially since the differentiation of actions seriously affects its recognition speed, robustness, and generalization ability. We propose a novel approach for transfer action recognition assisted by a bidirectional long- and short-term memory (Bi-LSTM) network combined with a multi-head attention mechanism. Firstly, we utilize posture sensors to detect human movements and establish a lightweight three-dimensional (3D) model of the lower limbs. In particular, we adopt a discrete extended Kalman filter (DEKF) to improve the accuracy and foresight of pose solving. Then, we construct an action prediction model that incorporates a fused Bi-LSTM with Multi-head attention (MHA Bi-LSTM). The MHA extracts key information related to differentiated movements from different dimensions and assigns varying weights. Utilizing the Bi-LSTM network effectively combines past and future information to enhance the prediction results of differentiated actions. Finally, comparisons were made by three subjects in the proposed method and with two other time series based neural network models. The reliability of the MHA Bi-LSTM method was verified. These experimental results show that the introduced MHA Bi-LSTM model has a higher accuracy in predicting posture sensor-based excretory care actions. Our method provides a promising approach for handling transfer-assisted action individual differentiation in excretion care tasks.

Series Motor Four Quadrants Drive DC Chopper Controllers Noise Cancellation

DC Series motor armature reaction causes EMI. EMI generates noise signals that affect feedback sensor readings, feedback controller function, and controller halting. This paper discusses software discrete filter and RLC hardware filter noise cancellation and filtering. The filter eliminates EMI-induced IGBT gate firing noise. Matlab/Simulink tests the filter circuit and digital filter control method, confirming software and hardware filter implementation.