reverberation rejection algorithm using auto regressive coefficients

Hi all

I am working on colored noise (reverberation) rejection algorithms. One approach present in literature is through prewhitening filter. In this approach as per my understanding, first we estimate reverberation spectrum through the data set in which no target is present i.e. we estimate ar coefficients (through auto regression) from this data set. In the next step, we use these ar coefficients in inverse filter to remove the reverberation from the next incoming data which can contain both reverberation as well as target.

I have done some MATLAB coding for this like:

a_=armcov(single_ping_data(1:10*pulse_samples),p);

here single_ping_data is the data set containing both reverberation and target. In the first ten" pulse_samples" chunks, there is only reverberation present so I estimate ar coefficients from this data set as a_.

In the next step, i formulate a combine signal as:

combine_signal=(single_ping_data(10*pulse_samples+1:10*pulse_samples+2*pulse_samples));

and finally i applied inverse filter on combine signal as:

y = filter(1,a_,combine_signal);

I repeated this process in a loop up to complete length of " single_ping_data".

The problem is that when i applied match filter on this filtered data, i could not get better results as compared to ones i got without applying this filter.

Can any one kindly help me in this regard?

Thanks