Implementation Of Adaptive Filter Research Articles

Cascaded multiplier-free implementation of adaptive anti-jamming filter based on GNSS receiver

Evaluating the computational complexity is critical for assessing the time-domain anti-jamming performance of GNSS receivers. The multiplier is the core component that contributes to the computational complexity in time-domain anti-jamming. However, current algorithms aimed at reducing the complexity of time-domain anti-jamming typically concentrate on shortening the filter length, which fails to address the high computational complexity introduced by the use of multipliers. This paper introduces a cascaded multiplier-free approach for implementing time-domain anti-jamming in navigation receivers. We propose a numerical power decomposition technique based on optimal Canonical Signed Digit coding and coefficient decomposition. By substituting the multiplier with minimal adder and shift operations, the computational complexity of the anti-jamming filter with a high quantization bit-width can be considerably decreased. An optimization strategy is presented, and the low-complexity multiplier-free technique is applied to the time-domain anti-jamming filter. Compared to the traditional Canonical Signed Digit multiplier-free technique, our method can reduce the components required for a 12-bit quantization anti-interference filter by one adder, 20 shift operations, and five coded word lengths, while maintaining a pseudo-range measurement deviation below 0.27 ns.

DsPIC Implementation and Performances Evaluation of Adaptive Filters for System Identification

This paper aims to implement and evaluate performances of adaptive filters applied to linear systems identification on a dsPIC platform. To do so, we considered two different applications: the identification of DC motor transfer function and the acoustic echo cancellation. For the DC motor case, real-time identification is not necessarily required. However, for the acoustic echo cancellation case, real-time identification is crucial, making execution time a key parameter in this scenario. We have implemented three adaptive algorithms: LMS, NLMS, and RLS. The first two are known for their computational simplicity, while RLS is recognized for its convergence speed. Performances evaluation is primarily based on accuracy and computation speed. A comparison is carried out in both cases to determine which algorithm is more suitable for each application. The results obtained showed that RLS is better suited for the DC motor case, while NLMS is more suitable for acoustic echo cancellation, particularly when the impulse response of the echo path is long.

High Performance FPGA Implementation of Single MAC Adaptive Filter for Independent Component Analysis

Blind source separation (BSS) is the process of extracting sources from mixed data without or with limited awareness of the sources. This paper uses field programmable gate array (FPGA) to create an effective version of the Blind source separation algorithm (ICA) with a single Multiply Accumulate (MAC) adaptive filter and to optimize it. Recently, space research has paid a lot of attention to this technique. We address this problem in two sections. The first approach is ICA, which seeks a linear revolution that can enhance the mutual independence of the mixture to distinguish the source signals from mixed signals. The second is a powerful flexible finite impulse response (FIR) filter construction that makes use of a MAC core and is adaptable. The adjustable coefficient filters have been used in the proposed study to determine the undiscovered system utilizing an optimal least mean square (LMS) technique. The filter tap under consideration in this paper includes 32 taps, and hardware description language (HDL) and FPGA devices were used to carry out the analysis and synthesis of it. When compared to the described architecture, the executed filter architecture uses 80% fewer resources and increases clock frequency by nearly five times, and speed is increased up to 32%.

Evaluation of Low-Complexity Adaptive Full Direct-State Kalman Filter for Robust GNSS Tracking.

This paper evaluates the implementation of a low-complexity adaptive full direct-state Kalman filter (DSKF) for robust tracking of global navigation satellite system (GNSS) signals. The full DSKF includes frequency locked loop (FLL), delay locked loop (DLL), and phase locked loop (PLL) tracking schemes. The DSKF implementation in real-time applications requires a high computational cost. Additionally, the DSKF performance decays in time-varying scenarios where the statistical distribution of the measurements changes due to noise, signal dynamics, multi-path, and non-line-of-sight effects. This study derives the full lookup table (LUT)-DSKF: a simplified full DSKF considering the steady-state convergence of the Kalman gain. Moreover, an extended version of the loop-bandwidth control algorithm (LBCA) is presented to adapt the response time of the full LUT-DSKF. This adaptive tracking technique aims to increase the synchronization robustness in time-varying scenarios. The proposed tracking architecture is implemented in an GNSS hardware receiver with an open software interface. Different configurations of the adaptive full LUT-DSKF are evaluated in simulated scenarios with different dynamics and noise cases for each implementation. The results confirm that the LBCA used in the FLL-assisted-PLL (FAP) is essential to maintain a position, velocity, and time (PVT) fix in high dynamics.

An experimental comparative study of adaptive sigma-point Kalman filters: Case study of a rigid–flexible four-bar linkage mechanism and a servo-hydraulic actuator

This paper investigates the application, design, and implementation of different adaptive sigma-point Kalman filters for nonlinear dynamic systems. It is a well-known fact that the performance of nonlinear Kalman filters depends on a prior knowledge of system noise statistics. In real-time applications, this knowledge is difficult and in some cases impossible to predefine. Therefore, an adaptation mechanism based on the random weighting method is proposed in this work to estimate, simultaneously, noise covariance and the state of the system. On the other hand, for real time execution, the computational efficiency of such filters should also be considered to minimize the burden on computational resources. Based on these two factors, in this paper, four different variants of the adaptive sigma-point Kalman filter are studied in detail: (I) an Adaptive Unscented Kalman filter (AUKF), (II) an Adaptive Square-root Unscented Kalman filter (ASr-UKF) with an Unscented Transformation sigma-point selection strategy, (III) an Adaptive Scaled Spherical Simplex Unscented Kalman filter (AS3UKF), and (IV) an Adaptive Square-root Scaled Spherical Simplex Unscented Kalman filter (ASr-S3UKF) with scaled spherical simplex sigma-point selection strategy. The performance of these adaptive sigma-point Kalman filters is analyzed here in depth and discussed for application to a simulated rigid–flexible four-bar linkage mechanism and a practical servo-hydraulic actuator setup under different scenarios. The simulation and the experimental results indicated that, for the cases studied in this article, the filters are competitive and present advantages and disadvantages that should be dealt with according to the requirements of the problem.

A Systematic Review on Distributed Arithmetic-Based Hardware Implementation of Adaptive Digital Filters

A Systematic Review on Distributed Arithmetic-Based Hardware Implementation of Adaptive Digital Filters

Low power low area VLSI implementation of adaptive FIR filter using DA for decision feed back equalizer

In this paper, a low-complexity pipelined adaptive FIR filter is designed using Distributed Arithmetic (DA) architecture for signal processing applications. Generally adaptive filters will occupy more area and power consumption because of using memories in the filters for partial product (PP) generation. To get rid of this, We use the pipeline concept to reduce the registers in the filters and also to reduce the area further compressor adders are used in the adaptive filter architectures instead of using normal adders. With these two concepts the area and power consumption of the adaptive filters will be reduced. The proposed design is coded in Verilog HDL language and synthesized in Synapsis design compiler tool with SAED 90 nm technology for finding the area, power, minimum sampling period, maximum sampling frequency, area delay product (ADP), power delay product (PDP). By using proposed adaptive filter we can design and implement higher order filters more easily and also the complexity of the proposed design is very less when compared with the existing designs. When we observe the synthesis results the proposed design will occupy 30% less area when compared with the two memories based existing architecture. Also the power consumption is 25% less when compared with the block based adaptive filters. The ADP and PDP of the proposed design is very less when compared with existing architectures. The proposed design is well suited for signal processing application designs such as adaptive decision feed back equalizers for removing the signal noises and inter symbol interference, hearing aids, ECG signal analysis and software defined radio.

FPGA implementation of hybrid recursive reversable box filter-based fast adaptive bilateral filter for image denoising

A Bilateral filter (BF) is a kind of edge-preserving filter, which is mostly used in computer vision, computational photography, and image processing applications. Recently, Reversible Logic Gates (RLG) technology has emerged as an attractive area in the field of field-programmable gate array (FPGA) based implementations. The RLG technology consumes less Quantum Cost (QC) as compared to conventional CMOS-based logic gates. As a result, this paper proposed an FPGA implementation of a hybrid recursive reversible box filter (HRRBF)-based fast adaptive bilateral filter (FABF) using RLG technology with joint geometric and photometric weight calculation and kernel result calculation operations. The RLGs are used to develop the reversable modified carry select adder (RMCSA), reversable adder subtractor (RAS), and multiplier, which significantly improves the speed and reduces the execution time, area, power, and delay as compared to the basic methods. In addition, ternary content addressable memory (TCAM) is used as the main memory to improve the speed of filtering. Obtained hardware and simulation results show that proposed RLG-based HRRBF-FABF results in enhanced performance as compared to state-of-the-art architectures. In addition, the image denoising application was also implemented with the proposed RLG-based HRRBF-FABF and achieved structural similarity (SSIM) index of 0.999 and peak signal-to-noise ratio (PSNR) of 78.348 dB. The simulation results showed that the proposed method resulted in better denoising performance as compared to conventional filtering approaches.

Swarm Intelligence-based Optimized Adaptive Filtering Technique for ECG Data Analysis System

The ECG (Electrocardiogram) signal is the most important signal in the biomedical signal processing, because it is used to diagnose the wellness of heart’s activity. Now a days the ECG signal processing devices have become more compact and smaller in size. Implementing the algorithms in devices is a complex task. Various blocks like preprocessing, extraction and diagnosis have to be implemented in hardware. The ECG signal will be corrupted by different types of noises. The filter to be implemented should be efficient in removal of the noise and extraction of medical information. In this paper, implementation of adaptive filtering using Swarm-based optimization techniques for biomedical system on chip architecture is presented. The proposed algorithm was implemented in different hardware and the performance is compared. The filters show linear phase and high rejection ratio. Field programmable gate arrays were used for implementation. These adaptive filters form the part of filter circuits for preprocessing and extraction of ECG signals. Implementation was done using Intel Altera innovative Programmable Power Technology tool Quartus.

VLSI Implementation of DENLMS Adaptive Filter for Biomedical Applications

Very Large Scale Integration (VLSI) implementation of the Delayed Error Normalized LMS (DENLMS) adaptive filter using a pipelined architecture is proposed and it can be used for biomedical applications. The proposed pipelined VLSI architecture increases the performance of adaptive filters by lowering the amount of time spent on critical path calculation. When compared to the current Error Normalized Least Mean Square (ENLMS) method for pipelining purposes, the DENLMS technique requires an additional delay element, and because of the delay element, the algorithm functions faster and more effective in low-power applications. The proposed pipelined architecture of the DENLMS filter, on the other hand, improves the efficiency while consuming less power overall. The cell leakage power reduction and total dynamic power reduction obtained by using the DENLMS filter are 31.8% and 33.5%, respectively, although the overall area of the filter has increased by 20.4%.

High-performance and ultra-compact spike-based architecture for real-time acoustic echo cancellation

In recent years, the implementation of advanced adaptive filters in embedded devices for acoustic echo cancellation has increased because most of them are used in portable devices, especially in Internet of Things systems, in which high-performance and low-area digital hardware implementations are required. In this work, we present a high-performance and ultra-compact spike-based hardware architecture to efficiently compute adaptive filters to be used as an acoustic echo canceller (AEC). To achieve this architecture, we address two factors. (1) We propose a new data-selective adaptive filter along with subband decomposition least mean square (LMS) method to reduce the computational cost by minimizing the number of operations required to efficiently update the filter coefficients. The proposed method requires approximately 40% fewer updates when compared with conventional subband adaptive filters. As a consequence, the spike-based hardware architecture simulates the proposed adaptive filter at high processing speeds. (2) We propose a compact and high-precision neural multiplier since acoustic echo cancellers require a large number of high-precision multiplications to efficiently identify the echo path. The proposed neural multiplier expends up to 15 times fewer synapses when compared with existing approaches, which represents a significant improvement in terms of area. In addition, this improvement avoids routing problems by implementing large-scale synapse connectivity in advanced FPGAs. Herein, we carry out exhaustive testing by simulating several acoustic echo scenarios. The results demonstrate that the features of the model and the hardware implementation techniques potentially allow easy integration into portable devices for use in real-world acoustic echo cancellation scenarios.

A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters.

In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to reflection and expansion steps of the NM technique in each iteration, NM used fixed value of , that is, = 1 for reflection and = 2 for expansion and replace the worst point of the simplex with that new point in each iteration. In this way NM search the optimum point. In proposed algorithm the optimum value of the parameter is computed and then centroid of new simplex is originated at this optimum point and regenerate the simplex with this centroid in each iteration that optimum value of will ensure the fast convergence of the proposed technique. The proposed algorithm has been applied to the real time implementation of the transversal adaptive filter. The application used to demonstrate the performance of the proposed technique is a well-known convex optimization problem having quadratic cost function, and results show that the proposed technique shows fast convergence than the Nelder-Mead method for lower dimension problems and the proposed technique has also good convergence for higher dimensions, that is, for higher filter taps problem. The proposed technique has also been compared with stochastic techniques like LMS and NLMS (benchmark) techniques. The proposed technique shows good results against LMS. The comparison shows that the modified algorithm guarantees quite acceptable convergence with improved accuracy for higher dimensional identification problems.

Adaptive notch filter under indirect and direct current controls for active power filter

This study presents the implementation of adaptive notch filter (ANF) as reference signal extraction for shunt active power filter (APF) in indirect current control (ICC) and direct current control (DCC) modes for three phase system. The ANF functions to filter the signal that inputted to it by producing a fundamental signal and harmonics signal. The advantage of applying the ANF algorithm is based on its simple design that giving the ANF advantages to be utilize in microcontroller. The performance of the ANF is validated though MATLAB simulation in ICC dan DCC configurations. Based on the simulation results, the ANF is capable to work efficiently for both ICC and DCC modes, but in term of efficiency, the ICC mode is clearly showing a better harmonics mitigation result. Base on the result also it shown that the ANF is capable of mitigate the harmonics below the standard required by the IEEE 519-92. The application of ANF is useful to be applied due to its simple design and filtering method.

Design and analysis of improved high-speed adaptive filter architectures for ECG signal denoising

In this paper, improved high-speed adaptive filter-based denoising architectures are proposed and implemented using the Xilinx Virtex-6 series FPGA platform. The performance of the proposed DF-RDLMS, TDF-RDLMS and TF-RDLMS implementations is analyzed and compared with that of the existing adaptive filter architectures as well as state-of-the-art wavelet-based denoising architectures. The proposed adaptive filter implementations are found to perform better than existing adaptive filter architectures and wavelet-based architectures. As compared to the existing adaptive filter implementations, the proposed architectures facilitate design flexibility in choice of the step-size parameter and also allow processing of input signals in fixed-point format as well as floating-point format. Simulation results exhibit the effectiveness of the proposed architectures in efficiently denoising ECG signals in noisy environments and prove their high suitability for low-cost high-performance denoising applications in medical field. Moreover, it is observed that the proposed high-speed adaptive filter architectures require significantly less hardware as compared to state-of-the-art wavelet-based architectures.

BIBO-stable implementation of adaptive function expansion bilinear filter for nonlinear active noise control

The preeminent merit of function expansion bilinear (FEB) filters is their ability to represent nonlinear distributions for active noise control (ANC) systems. Unfortunately, the huge computational burden and bounded input bounded output (BIBO) unstable problem hinder its practical applications. In this paper, we propose a general channel reduced efficient implementation algorithm for FEB filter and two BIBO stable control methods, to further reduce the computational load and overcome the unstable problem. The efficiency of the proposed methods is verified by computational complexity analyses and computer simulations for nonlinear ANC (NANC) systems. The results of computational analysis and computer simulation show the satisfied control performances with significant computational reduction.

Identifying and Characterizing Cyclicality and Seasonality in Environmental Data with An Adaptive Filter Implementation of A Phase Locked Loop

Phase Locked Loops are provided as a manner of assessing, monitoring and forecasting with seasonal or cyclical data. Phase Locked Loops using adaptive filtering were initially utilized for gravitational wave data analysis. In this paper we examine this implementation as an alternative approach for identifying the presence of one or more cycles in environmental data. Unlike most techniques for assessing seasonality phase lock loops can identify the presence of cycles without prior suspicion of their nature and existence. This is important for identifying either the initiation of new cycles or changes in existing cycles. As environmental data can be impacted by the presence of numerous cyclical phenomena and these cycles can change over time, this is a suitable and valuable technique. The technique can be used on existing data sets or assessing data streams. Phase Locked Loops are advantageous over many currently as the presence of multiple different cycles can be identified without prior suspicion and specification of the nature of the cyclicality of the data. The speed and parsimonious nature of the technique makes Phase Lock Loops ideal for Big Data and data flows that are so large that they must be assessed on a real time basis. The technique can manage and advise of changes in frequency, amplitude and phase shifts. The technique is illustrated utilizing weather data.

Design and implementation of Least Mean Square adaptive FIR filter using offset binary coding based Distributed Arithmetic

Distributed Arithmetic (DA) based architecture is an efficient technique to attain high throughput without hardware multiplier and also it is essential for bit serial operation. The DA based Finite Impulse Response (FIR) adaptive filter is well suited for hardware implementation in Field Programmable Gate Array (FPGA) device. In conventional DA the partial products of the filter coefficients have been pre-calculated and stored in Look up Table (LUT) which in turn will increase the logic elements and power. To overcome this problem DA based Least Mean Square (LMS) adaptive filter using offset binary coding (OBC) without LUT is proposed. The proposed method will reduce the logic elements by half when compared to the conventional DA based OBC filter. The Carry Save Accumulator (CSA) is used to carry out the operation of shift and accumulation. The proposed architecture is implemented in Quartus II 9.1 with the device as Stratix-EP2S15F484C3 which offers 13.72% high throughput, 56.92% reduction in logic elements, 42.84% reduction in power, 57.74% reduction in logical registers for N = 16 and for N = 32 the number of logical element is reduced to 80.87%, 66.66% reduction in power and 24.12% high throughput.

FPGA implementation of adaptive Kalman filter for industrial ultrasonic applications

This paper achieves an FPGA system, which is specifically designed for the industrial ultrasonic application. The FPGA system contains a feedback sensing circuit module and an adaptive Kalman filter (AKF) module. Industrial ultrasonic application system can be described as a time-varying nonlinear system, which directly relates to the temperature and the pressure of objects at certain timing. The frequency controller of the FPGA circuit generates the optimal working frequency based on continuously sensing the value of feedback voltage, current and the phase difference among both. This work aims to design an FPGA filter circuit to phases detecting as well as the value of its current and voltage feedback. This paper also implements an AKF to feedbacks to prevent feedback signal noise and jitters, which are causing by electromagnetic interference of the ultrasonic circuit. In the experimental results of this work, the feedback results by AKF successfully lead the implementation of MPPT (maximal power point track, MPPT) algorithm to achieve very clean and stable feedback results on the industrial ultrasonic system.

Adaptive echo suppression based on LabVIEW and C# implementation

This paper deals with the using of adaptive filtering in the area of telecommunication technology. The study focuses on the possibilities of design and implementation of adaptive filters based on virtual instrumentation in the LabVIEW development environment from the American company National Instruments. The selected adaptive algorithms (LMS, NLMS, RLS, QR-RLS) are implemented both in LabVIEW and C# programming language in the form of dynamically linked DLL (Dynamic-Link Library) libraries. The experiments test the reliability and speed of the implemented algorithms using them for the suppression of the echo in sound signal. Based on the findings from the implementation, a comparison of both options is made by evaluation of the computation time and improvement of signal-to-noise ratio (SNR).

Multiple Sub-Filter Adaptive Noise Canceller for Fetal ECG Extraction

Abstract Non-invasive monitoring of fetal cardiac signs through the signal processing of maternal abdominal signals has been emerging as a promising technique in the field of obstetrics and gynaecology. The underlying challenges in obtaining the Fetal Electrocardiogram (FECG) from maternal abdominal signals include elimination of dominant maternal ECG and other noises. The simple technique based on adaptive noise cancellers uses Single Long Filter (SLF) as adaptive filter for extracting the FECG. In this paper, we propose an algorithm for implementation of adaptive filter in adaptive noise canceller (ANC) as Multiple Sub-Filters (MSF). Each of the adaptive sub-filters are updated using the Least Mean Square (LMS) algorithm. The proposed MSF architecture is compared with traditional SLF ANC by testing using a real data from Daisy database. From the obtained results, it is observed that MSF-ANC performs better than SLF-ANC with 91.66% positive predictive value and 84.61% accuracy.