Finite Impulse Response Filtering Algorithm Research Articles

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems.

Optimal High Pass FIR Filter Based on Adaptive Systematic Cuckoo Search Algorithm

Abstract This paper presents the design of a desired linear phase digital Finite Impulse Response (FIR) High Pass (HP) filter based on Adaptive Systematic Cuckoo Search Algorithm (ACSA). The deviation, or error from the desired response, is assessed along with the stop-band and pass-band attenuation of the filter. The Cuckoo Search algorithm (CS) is used to avoid local minima because the error surface is typically non-differentiable, nonlinear, and multimodal. The ACSA is applied to the minimax criterion (L∞-norm) based error fitness function, which offers a better equiripple response for passband and stopband, high stopband attenuation, and rapid convergence for the developed optimal HP FIR filter algorithm. The simulation findings demonstrate that when compared to the Parks McClellan (PM), Particle Swarm Optimization (PSO), CRazy Particle Swarm Optimization (CRPSO), and Cuckoo Search algorithms, the proposed HP FIR filter employing ACSA leads to better solutions.

Block Fast FIR Algorithm and Circuit Design

The performance of finite impulse response (FIR) filtering is an important index of the real-time processing ability of digital signal processing system. In this paper, various FIR filtering algorithms are analyzed, and based on fast fir algorithm (FFA), block FFA (BFFA) is proposed. A 128 order configurable FIR filter circuit with 16 order as a block is designed. The analysis of experimental data shows that the 16 bit integer FIR filter performance reaches 881.78Gop/s.

A Novel $H_2$ Approach to FIR Prediction Under Disturbances and Measurement Errors

A novel approach is proposed to $H_2$ finite impulse response (FIR) prediction in discrete-time state-space. The biased-constrained $H_2$ optimal unbiased FIR ( $H_2$ -OUFIR) predictor derived under disturbances and measurement errors is shown to have the maximum likelihood form and be equivalent to the OUFIR predictor under Gaussian noise. The derivation is provided using the backward Euler method by minimizing the squared weighted Frobenius norm. A bias-constrained suboptimal $H_2$ FIR filtering algorithm using the linear matrix inequality is also designed. The $H_2$ -OUFIR predictor performance is investigated by simulations and experimentally in a comparison with the Kalman and unbiased FIR predictors.

Self-Tuning Unbiased Finite Impulse Response Filtering Algorithm for Processes With Unknown Measurement Noise Covariance

An unbiased finite impulse response (UFIR) filtering algorithm is designed in the discrete-time state-space for industrial processes with unknown measurement data covariance. By assuming an inverse-Wishart distribution, the data noise covariance is recursively estimated using the variational Bayesian (VB) approach. The optimal averaging horizon length N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> is estimated in real time by incorporating the estimated data noise covariance into the full-horizon UFIR filter and specifying N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> at a point, where the estimation error covariance reaches a minimum. The proposed VB-UFIR algorithm is applied to a quadrupled water tank system and moving target tracking. It is demonstrated that the VB-UFIR filter self-estimates N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">opt</sub> more accurately than known solutions. Furthermore, the VB-UFIR filter is not prone to divergence and produces more stable and more reliable estimates than the VB-Kalman filter.

Design and evaluation of a high-sensitivity digital receiver with the finite impulse response filter algorithm for free-space laser communication

We discuss the design of a digital laser communication receiver and its sensitivity test process. Laser communication is especially suitable for long-distance low-power micro–nano satellite links due to its narrow beam width, but the electro-optical noise situation of the micro–nano satellite platform is complicated and bad. Hence, it is necessary to introduce a digital laser communication receiver to flexibly suppress interference and noise to improve communication sensitivity. A digital laser communication receiver based on the avalanche photodiode (APD) is designed. Based on the principle of digital communication error probability, a high-sensitivity APD device is selected, and a digital receiving channel based on field programmable gate array is designed. The finite impulse response, the dynamic threshold, the baseband median, and other filtering algorithms are used to optimize the signal-to-noise ratio and enhance the detection sensitivity. At the same time, an 800-nm laser communication receiver sensitivity test experimental architecture is designed and the detection sensitivity experiment is carried out. The experiment indicates that the APD receiver achieves detection sensitivity at an Mbps rate better than −60 dBm. The digital filtering algorithm provides a filtering signal-to-noise ratio improvement of about 15 dB, which supplies a reference for the receiver design of micro–nano satellite laser communication.

Optimal and Unbiased Filtering With Colored Process Noise Using State Differencing

This letter develops the Kalman and unbiased finite impulse response filtering algorithms for linear discrete-time state-space models with Gauss-Markov colored process noise (CPN) employing state differencing. The approach avoids problems caused by matrix augmentation, but requires solving a nonsymmetric algebraic Riccati equation to specify the system matrix modified for CPN. Higher accuracy of the algorithms proposed is demonstrated by simulation. A comparative analysis of filtering estimates is provided based on navigation data of walking humans.

Inlet NOx and NH3 Concentration Estimation for Diesel-engine SCR Systems by Combining Data-Driven Model and Unbiased FIR Filter

Selective catalytic reduction (SCR) systems have been widely used in diesel engine applications. In an SCR system, input NOx and NH3 concentration information are of critical importance for the urea dosage controller design and system fault detection. Generally, the NOx and NH3 concentration are obtained by physical sensors. However, the physical sensors do not only increase the cost of overall system, but also induce measurement delays. To deal with this issue, an input observer combining a data-driven model and an unbiased finite impulse response (FIR) filter is proposed. The structure of data-driven model is auto-regressive exogenous (ARX) model and partial least square (PLS) is utilized to identify the parameters in the ARX model. Nevertheless, fuzzy c-means (FCM) is also employed to partition the data and obtain multiple local linear models for describing the nonlinearities of the system. At last, an unbiased FIR filter is adopted to estimate the input NOx and NH3 concentration simultaneously due to its strong robustness against the noise. The comparisons between the unbiased FIR filter algorithm and Kalman filter algorithm are carried out in MATLAB/SIMULINK. The simulation results demonstrate that the performance of proposed estimator is outstanding.

Fast bias‐constrained optimal FIR filtering for time‐invariant state space models

SummaryThis paper combines the finite impulse response filtering with the Kalman structure (predictor/corrector) and proposes a fast iterative bias‐constrained optimal finite impulse response filtering algorithm for linear discrete time‐invariant models. In order to provide filtering without any requirement of the initial state, the property of unbiasedness is employed. We first derive the optimal finite impulse response filter constrained by unbiasedness in the batch form and then find its fast iterative form for finite‐horizon and full‐horizon computations. The corresponding mean square error is also given in the batch and iterative forms. Extensive simulations are provided to investigate the trade‐off with the Kalman filter. We show that the proposed algorithm has much higher immunity against errors in the noise covariances and better robustness against temporary model uncertainties. The full‐horizon filter operates almost as fast as the Kalman filter, and its estimate converges with time to the Kalman estimate. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Research of Sea Clutter Suppression Algorithm Based on GPU

This paper presents an algorithm of sea clutter suppression using graphics processing unit (GPU) to meet the real-time requirement in the general radar terminal system. The main idea is to convert an infinite impulse response (IIR) filter to a finite impulse response (FIR) filter, which is suitable for the parallelization processing of GPU. Finally, the converted FIR filter algorithm is implemented on the GPU efficiently, achieving a speed approximately twice as fast as that of the previous IIR filter algorithm implemented on the CPU.

Frequency tracking techniques of power systems in coloured noises

A technique for tracking the frequency of power systems in sine noises using numerical differentiation is presented. A voltage or current sinusoidal signal corrupted by sine noises and white noises is considered. For the signal corrupted by one or two sine noises, a central numerical differentiation-based method is proposed. For the signal corrupted by multiple sine noises and white noises, a hybrid method of numerical differentiation and a digital finite impulse response (FIR) filter are proposed. The digital FIR filtering algorithm is used to remove the white noises and the sine noises, and the numerical differentiation algorithm is used to estimate the fundamental frequency of power systems when the fundamental component is decomposed out of the signal. The proposed algorithm shows an advantage in time and speed when compared with other existing techniques and shows better dynamics and higher accuracy in frequency estimation. Carried out in Matlab, the simulation results are satisfactory.

Efficient Implementation Methodology of Fast FIR Filtering Algorithms on DSP

A class of Finite Impulse Response (FIR) filtering algorithms based either on short Fast Fourier Transforms (FFT) or on short length FIR filtering algorithms was recently proposed. Besides the significant reduction of the arithmetic complexity, these algorithms present some characteristics which make them useful in many applications, namely a small delay processing (independent on the FIR filter length) as well as a multiply-add based computational structure. These algorithms are presented in a unified framework, thus allowing an easy combination of any of them. However, a remaining difficulty concerns the implementation of the fast algorithms on Digital Signal Processors (DSP), given the DSP finite resources (number of pointers, registers and memory), while keeping as much as possible the improvement brought by the reduction of the arithmetic complexity. This paper provides an efficient implementation methodology, by organizing the algorithm in such a way that the memory data access is optimized on a DSP. As a result, our implementation requires a constant number of pointers whatever the algorithm combination. This knowledge is used in a DSP code generator which is able to select the appropriate algorithm meeting the application constraints, as well as to generate automatically an optimized assembly code, using macro-instructions available in a DSP-dependent library. An improvement of more than 50% in terms of throughput (number of machine cycles per point) compared to the implementation of the direct convolution is generally achieved.