Comparison of delayless digital filtering algorithms and their application to multi-sensor signal processing (original) (raw)
Related papers
1972
A three part survey is made of the state-of-the-art in digital filtering. Part one presents background material including sampled data transformations and the discrete Fourier transform. Part two, digital filter theory, gives an in-depth coverage of filter categories, transfer function synthesis, quantization and other nonlinear errors, filter structures and computer aided design. Part three presents hardware mechanization techniques. Implementations by general purpose, mini-, and special-purpose computers are presented.
Digital filters with equiripple magnitude and group delay
and is currently a Professor there. He is the Responsible Administrator for the research group dealing with Communications Circuits and Systems for the Gouvernement du QuBbec, Minist&re de l'Education, a scientific consultant to a number of North American companies, and Principal Investigator for a research contract recently completed for the Bedford Institute of Oceanography. He has authored many papers on detection and estimation theory, statistical pattern recognition, and digital signal processing in the classified and unclassified literature, and has contributed papers to three books. His recent research interests include pattern recognition feature selection and data compression, and'fast algorithms for digital signal processing problems. Dr. Morgera is an IEEE Information Theory Group representative to the Society of Oceanic Engineering and, most recently, a Special Session Chairman and Technical Program Committee member for the 1983 International Symposium on Information Theory.
… Signal Processing, 2010
The general research aims of this group are directed toward the development and application of techniques for digital signal processing and digital filtering. There has been a clear trend over the past several years toward increased use of digital rather than analog processing of signals, largely because of the inherent flexibility and reliability of digital processing. With the continuing development of integrated circuit technology, faster digital hardware with reduced cost and size is constantly becoming available. Digital signal-processing techniques have found application in a wide variety of areas including speech and picture processing, radar and sonar signal processing, and seismic data analysis. A summary of some of our present research projects and plans follows.
Optimizing Digital Filter for Effective Signal Processing
Implementing hardware design in Field Programmable Gate Arrays (FPGAs) is a formidable task. There is more than one way to implement the digital FIR filter. Based on the design specification, careful choice of implementation method and tools can save a lot of time and work. MatLab is an excellent tool to design filters. There are toolboxes available to generate VHDL descriptions of the filters which reduce dramatically the time required to generate a solution. Time can be spent evaluating different implementation alternatives. Proper choice of the computation algorithms can help the FPGA architecture to make it efficient in terms of speed and/or area.
Design and Applications of Digital Filters
Encyclopedia of Information Science and Technology, Second Edition
Digital signal processing (DSP) is an area of engineering that “has seen explosive growth during the past three decades” (Mitra, 2005). Its rapid development is a result of significant advances in digital computer technology and integrated circuit fabrication (Jovanovic Dolecek, 2002; Smith, 2002). Diniz, da Silva, and Netto (2002) state that “the main advantages of digital systems relative to analog systems are high reliability, suitability for modifying the system’s characteristics, and low cost”. The main DSP operation is digital signal filtering, that is, the change of the characteristics of an input digital signal into an output digital signal with more desirable properties. The systems that perform this task are called digital filters. The applications of digital filters include the removal of the noise or interference, passing of certain frequency components and rejection of others, shaping of the signal spectrum, and so forth (Ifeachor & Jervis, 2001; Lyons, 2004; White, 200...
On-line digitalization technologies for monitoring activities in the marine environment
Proceedings of 6th International Electronic Conference on Sensors and Applications, 2019
This proceeding shows the results of the investigation of the techniques of the integration, management, and visualization of massive data from the digitalization of environmental and procedural parameters of facilities that operate in the marine environment. The work focuses on three main lines: (1) research on the development of a cloud-based system for big data, which allows the hosting of the data generated by different devices to be monitored (GPS, sounds, vibrations, video, temperature, emissions, consumption, power, etc.); (2) the implementation of a first layer of analysis and visualization of information; and (3) big data analytics research for the post-processing of information. The studies will be applied to underwater noise monitoring. With this, progress has been made in another of the pillars of Web 4.0—the use of context information—as the application is in charge of intelligently processing the data of the different variables together although they are not, in princi...
The digital synchronous filtering technique
Mechanical Systems and Signal Processing, 1987
An original signal processing technique, digital synchronous filtering (DSF) is described. DSF makes it possible to estimate the time history of such periodic components of the signal being analysed, the corresponding frequencies of which are integer multiples of some triggering frequency (external or defined by the signal itself). These signal components, which are not synchronous with the triggering signal, are simultaneously attenuated. The DSF is based upon the well known time domain averaging technique. The DSF is especially suitable, for example, in the analysis of vibro-acoustic signals acquired from a rotating machine observed in run-up/coast-down (i.e. non-stationary) conditions. In this paper the DSF is briefly described and properties of the resulting filters are discussed in detail. The bank of filters obtained makes it possible to estimate the averaged time courses of the periodic components of the signal (in the example presented orders of the components are no greater than six in relation to the triggering signal used). Amplitude and phase characteristics of the filters are given. Only simple fixed-point arithmetic operations were used in order to prepare the necessary software for signal processing. An example of the application of the DSF in investigations of a turbocompressor observed during the coast-down is given.
Cutting-edge Mathematical Tools in Processing and Analysis of Signals in Marine and Navy
Transactions on Maritime Science, 2012
Signal processing plays a pivotal role in information gathering and decision making. This paper presents and compares different signal processing techniques used in marine and navy applications, primarily based on using wavelets as kernel. The article covers Fourier transform, time frequency wavelet based techniques such as bandelets, contourlets, curvelets, edgelets, wedgelets, shapelets, and ridgelets. In the example section of the paper, several transform techniques are presented and commented on the harbour surveillance video stream example.
Digital signal processing for precision wide-swath bathymetry
IEEE Journal of Oceanic Engineering, 2000
A mathematical model is formulated which accurately represents the envelope function of bottom return signals received from a number of spatial directions comprising a wide swath. The bottom return signals are processed utilizing a digital nonrecursive matched filter whose coefficients are tapered using a Tukey window. High-speed convolution employing the fast Fourier transform is examined for implementation of the digital matched filtering operation. Computer simulation of the signal processing system indicates that, even in the presence of considerable background and fluctuation noises, the processor provides an output signal having a well-defined peak. The error in time of arrival is found to be less than 3 ms, corresponding to an error in depth of less than 0.1 percent, for an average signal-to-noise ratio of 15 dB and a vertical ocean depth of 12 000 ft (3.7 km). These performance figures apply to the most dimcult case of mapping at angles of _+ 45' off vertical. 'I Montreal, Canada, in 1980. Currently, he is completing the Ph.D. degree in the Department of Electrical Engineering at Pennsylvania State University, University Park, PA. He is presently working on feature extraction for a speaker-independent isolated-word recognition system. His current research interests are in the areas of digital signal processing, speech recognition, pattern recognition, and computer applications of related areas, system modeling, and simulation.
Toward a group delay reduction in digital filtering
Digital Signal Processing, 2009
Finite impulse response (FIR) filters are often used in digital signal processing applications because their linear phase properties do not introduce group delay distortion. While this property is desirable, we may also desire that the filter exhibit zero group delay to suppress the causal time offset between the input and output of the filter. We can minimize the causal delay by the use of recursive minimum phase filters but these introduce an objectionable group delay distortion. We desire both zero group delay and a nonrecursive impulse response (IR). This filter can be applied to input signals indirectly through a modified overlap and save FFT based circular convolution. The desired result is obtained in our proposed new filtering technique: the modified overlap and save method (MOSM). We accomplish this by redefining the time origin of the prototype filter's impulse response by circularly shifting the response in the FFT filter time vector till the symmetry point coincides with the vector's zero index.