scholarly journals FIR to FIR Model Reduction with Linear Group Delay in Passband by SDP Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Haijiang Hu ◽  
Shaojing Song ◽  
Fengdeng Zhang
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Model Reduction ◽  
Digital Filter ◽  
Group Delay ◽  
Digital Signal ◽  
Optimization Method ◽  
Filter Model ◽  
Fir Digital Filter ◽  
Fir Model

Filter model reduction is an important optimization method in digital signal processing. A method of FIR to FIR model reduction using SDP optimization is proposed in this paper. At first, we use SDP to design an original FIR filter. Then we name a general K-order FIR digital filter H1z−1 with coefficient values equal to the first K + 1 filter coefficient values of H0z−1. Finally, we design a new general K-order FIR digital filter H2z−1 connected in parallel with H1z−1 using SDP optimization. The experiment results show this method has good performance on the magnitude error and the linear phase in passband. Therefore, this method can be used in the field of digital signal processing.

Design and Realization of FIR Digital Filter Based on MATLAB

2012 ◽  
Vol 490-495 ◽  
pp. 1867-1870 ◽  
Author(s):  
Shan Ren ◽  
Xin Zhao ◽  
Wen Bin Zhang
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Basic Principle ◽  
Digital Filter ◽  
Digital Signal ◽  
Fir Filter ◽  
Window Function ◽  
Measured Signal ◽  
Fir Digital Filter ◽  
Filtering Effect

Digital filter is one of the most important parts of digital signal processing. This paper proposes the method of using window function to design FIR filter based on MATLAB, according to the design basic principle of FIR digital filter. Filtering processing for measured signal showed that filtering effect of the filter achieved the expected results.

scholarly journals A Method of Increasing Digital Filter Performance Based on Truncated Multiply-Accumulate Units

2020 ◽  
Vol 10 (24) ◽  
pp. 9052
Author(s):  
Pavel Lyakhov ◽  
Maria Valueva ◽  
Georgii Valuev ◽  
Nikolai Nagornov
Keyword(s):  
Signal Processing ◽  
Theoretical Analysis ◽  
Digital Signal Processing ◽  
Digital Filter ◽  
Digital Filters ◽  
Digital Signal ◽  
Digital Filtering ◽  
Filter Performance ◽  
Filter Architecture ◽  
Hardware Costs

This paper proposes new digital filter architecture based on a modified multiply-accumulate (MAC) unit architecture called truncated MAC (TMAC), with the aim of increasing the performance of digital filtering. This paper provides a theoretical analysis of the proposed TMAC units and their hardware simulation. Theoretical analysis demonstrated that replacing conventional MAC units with modified TMAC units, as the basis for the implementation of digital filters, can theoretically reduce the filtering time by 29.86%. Hardware simulation showed that TMAC units increased the performance of digital filters by up to 10.89% compared to digital filters using conventional MAC units, but were associated with increased hardware costs. The results of this research can be used in the theory of digital signal processing to solve practical problems such as noise reduction, amplification and suppression of the frequency spectrum, interpolation, decimation, equalization and many others.

New Methodology for Smoothing Freeway Loop Detector Data: Introduction to Digital Filtering

1996 ◽  
Vol 1554 (1) ◽  
pp. 142-152 ◽  
Author(s):  
Benjamin A. Coifman
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Filter ◽  
Filter Design ◽  
Digital Signal ◽  
Data Set ◽  
Digital Filter Design ◽  
Loop Detector ◽  
Smoothing Process ◽  
Time Averages

A new methodology has been developed for smoothing loop detector data based on digital signal processing. After introducing basic signal processing theory, existing smoothing techniques like fixed-time averages, cumulative sums, and moving-time averages—all subsets of the larger digital signal processing methodology—are described. Nontraditional smoothing techniques based on custom digital filter design are then presented, specifically, low-pass filters that “pass” slowly changing features of the detector data unchanged while attenuating rapidly changing features. Custom digital filter design gives more control over the smoothing process than do traditional smoothing methods. In particular, the amount of amplification or attenuation at a given frequency (e.g., rapidly or slowly changing features) can be set. Whether a custom filter or a traditional smoothing process is used, an understanding of the frequency response can increase the usefulness of the resulting data by clarifying the limitations of the given smoothing process. To illustrate this, several smoothing processes are presented and contrasted using the same data set. The use of a custom digital filter is demonstrated in three practical applications: an examination of the bivariate flow-occupancy relationship and use of the filtering process to eliminate unstable disturbances from the data set, speed estimation from a single detector under congested conditions, and temporal issues relating to shock wave and fluctuation propagation specifically.

Design and Realization of FIR Band-Pass Digital Filter Based on MATLAB

2012 ◽  
Vol 503-504 ◽  
pp. 228-231 ◽  
Author(s):  
Shan Ren ◽  
Xin Zhao ◽  
Jie Jiang ◽  
Dong Jin Zhao
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Digital Filter ◽  
Digital Signal ◽  
Window Function ◽  
Measured Signal ◽  
Time Interval ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Band Pass

Digital filter is one of the most important parts of digital signal processing. In practice, digital signal processing often need to limit the signal observation time interval within a certain time, choose only one period of signal that signal data will be truncated, this process is equivalent to plus window function operation to signal. In order to obtain finite unit sample response, need to truncate the infinite unit sample response sequence by window function. This paper proposes the method of using window function to design FIR Band-pass filter based on MATLAB, according to the design basic principle of FIR digital filter. Filtering processing for measured signal showed that filtering effect of the filter achieved the expected results.

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