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I have a data vector x with uniform data points say [1 2 3 4 5]

and another data vector x_random with random data points say [.9 2.3 3.6 3.8 5.9]

The signal vector corresponding to random data is known and it consists of complex numbers like [-1 +i 2+2i 4-2i 1+3i -2+i]. The problem is that i want to interpolate nonuniform data points to get complex data corresponding to uniform point!!!

Can anybody help me? Do you know any matlab function for complex data interpolation?

Thanks

Anshu

can think of the following cases:

a) your metric is the magnitude of the complex numbers and you want to

interpolate the magnitude (answer not unique)

b) your metric is the phase of the complex numbers and you then you want to

interpolate the phase (answer not unique)

c) you want to interpolate the real and imaginary parts of the complex

numbers independently (answer is unique)

For each of these, you are really interpolating a real number and can be

done in the standard method.

Nandan

On 11/3/05, Anshu <anshu_27@ansh...> wrote:

>

> Hi

>

> I have a data vector x with uniform data points say [1 2 3 4 5]

> and another data vector x_random with random data points say [.9 2.3 3.6

> 3.8 5.9]

> The signal vector corresponding to random data is known and it consists of

> complex numbers like [-1 +i 2+2i 4-2i 1+3i -2+i]. The problem is that i want

> to interpolate nonuniform data points to get complex data corresponding to

> uniform point!!!

>

> Can anybody help me? Do you know any matlab function for complex data

> interpolation?

>

> Thanks

> Anshu

Copied directly from the interp1 help....

Interpolating Complex Data

For Real x and Complex Y. For interp1(x,Y,...) where x is real and Y is

complex, you can use any interp1 method except for 'pchip'. The

shape-preserving aspect of the 'pchip' algorithm involves the signs of the

slopes between the data points. Because there is no notion of sign with

complex data, it is impossible to talk about whether a function is

increasing or decreasing. Consequently, the 'pchip' algorithm does not

generalize to complex data.

The 'spline' method is often a good choice because piecewise cubic splines

are derived purely from smoothness conditions. The second derivative of the

interpolant must be continuous across the interpolating points. This does

not involve any notion of sign or shape and so generalizes to complex data.

For Complex x. For interp1(x,Y,...) where x is complex and Y is either real

or complex, use the two-dimensional interpolation routine interp2(REAL(x),

IMAG(x),Y,...) instead.

See Also

interpft, interp2, interp3, interpn, pchip, spline Regards,

Jeff

************************************

Jeff Winter

Snr Signal Processing Engineer

Aeroflex

www.aeroflex.com Check out our PXI RF digitizer:

www.aeroflex.com/pxi -----Original Message-----

From: matlab@matl... [mailto:matlab@matl...]On Behalf Of

Anshu

Sent: 03 November 2005 23:26

To: matlab@matl...

Subject: [matlab] Non Uniform data interpolation Hi

I have a data vector x with uniform data points say [1 2 3 4 5]

and another data vector x_random with random data points say [.9 2.3 3.6 3.8

5.9]

The signal vector corresponding to random data is known and it consists of

complex numbers like [-1 +i 2+2i 4-2i 1+3i -2+i]. The problem is that i

want to interpolate nonuniform data points to get complex data corresponding

to uniform point!!!

Can anybody help me? Do you know any matlab function for complex data

interpolation?

Thanks

Anshu

> Can anybody help me? Do you know any matlab function for complex

> data interpolation?

Complex numbers are an unordered bunch, so it's impossible to say

which complex numbers lie within any other two...

So, you need a metric on those complex data; it can be, real part,

imaginary part, its modulus, its argument (phase)... Depending on

what you need, you'll be able to figure out what to do.

--

Juan de Dios Santander Vela

Diplomado en CC. Físicas, Ingeniero en Electrónica

Doctorando en Tecnologías Multimedia

Becario Predoctoral del Instituto de Astrofísica de Andalucía

Sólo hay 10 clases de personas en el mundo: las que entienden la

notación binaria, y los que no.