Digital Signal Processing with Matlab Examples, Volume 3: by Jose Maria Giron-Sierra

By Jose Maria Giron-Sierra

This is the 3rd quantity in a trilogy on smooth sign Processing. the 3 books supply a concise exposition of sign processing themes, and a advisor to aid person sensible exploration according to MATLAB programs.

This ebook contains MATLAB codes to demonstrate all the major steps of the idea, supplying a self-contained consultant appropriate for self sustaining research. The code is embedded within the textual content, aiding readers to place into perform the tips and techniques discussed.

The e-book basically makes a speciality of filter out banks, wavelets, and photographs. whereas the Fourier rework is sufficient for periodic indications, wavelets are enhanced for different instances, akin to short-duration signs: bursts, spikes, tweets, lung sounds, and so on. either Fourier and wavelet transforms decompose signs into elements. additional, either also are invertible, so the unique signs may be recovered from their parts. Compressed sensing has emerged as a promising inspiration. one of many meant purposes is networked units or sensors, that are now turning into a fact; for that reason, this subject can be addressed. a variety of experiments that reveal photo denoising functions also are integrated. within the curiosity of reader-friendliness, the longer courses were grouped in an appendix; extra, a moment appendix on optimization has been additional to complement the content material of the final chapter.

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Extra info for Digital Signal Processing with Matlab Examples, Volume 3: Model-Based Actions and Sparse Representation

Example text

1 Evolution of the State Error The state error er = x − xˆ could also be considered the estimation error. From OPKF: xˆ (n + 1) = A xˆ (n) + B u(n) + K (n) [y(n) − C xˆ (n)] 21 22 1 Kalman Filter, Particle Filter and Other Bayesian Filters Fig. 77) (let us denote Ψ = [A − K (n) C] ) Assuming that at the beginning the mean of the error is zero, then (depending on Ψ ), the mean would stay at μe (n) = 0. 8 shows the evolution of the error for the two-tank system example. 3 that has been included in Appendix B.

It can be noticed that the original and the propagated PDFs are almost equal. 5, which uses a simple code vectorization for speed. Fig. 4 Nonlinear Conditions Fig. 5 Fig. 18 shows a practical perspective of the PDF propagation through the nonlinear measurement function. 5. This kind of representation will be used again in next sections. 4,'r^'); title('nonlinear measurement'); subplot('position',[pl2 pb2 pw2 ph2]) %bottom plot plot(hax,ha); hold on; plot([0 0],[0 3500],'r--'); axis([-10 10 0 3500]); title('before'); It is clear that the arctan(x) function is almost linear for small values of x.

77) (let us denote Ψ = [A − K (n) C] ) Assuming that at the beginning the mean of the error is zero, then (depending on Ψ ), the mean would stay at μe (n) = 0. 8 shows the evolution of the error for the two-tank system example. 3 that has been included in Appendix B. 2 0 0 0 10 20 30 40 0 K22 0 10 20 30 40 sampling periods Fig. 81) In certain cases, it would be convenient to use from the beginning this constant K , which could be pre-computed. 9 shows the evolution of the Kalman filter gains in the two-tank system example.

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