mkclutterV(T , LambdaV , V);

function [clutter , mean_n_FA , n_FA] = mkclutterV(T , LambdaV , V);

% Make clutter into hyper-cube V with a mean poisson noise process LambdaV.
%
%
% [clutter , mean_n_FA , n_FA] = mkclutterV(T , LambdaV , V);
%
% 
% Inputs
% -----
%
% T            Number of scans
%
% LambdaV      mean number of false alarm per volume unit V = prod(diff(V));
%
% V            Hypercube limits (2 x n) dimensions (dfaut V = [0 ; 2Pi])
%
% 
% Outputs
% -----
%
% clutter      Matrix (N x n + 1), N = n_1 +...+ n_T where n_i ~ Poisson(LambdaV). 
%              Clutter(: , 1) is time index.
%
% mean_n_FA    Expected number of false alarms per volume unit
%
%
% Example
% -------
%
% T       = 10;
% LambdaV = 2;
% V       = [0 , -1000 ; 2*pi , 1000];
% clutter = mkclutterV(T , LambdaV , V);
%
%
%***************************************************************************%%
%  Auteur Sbastien PARIS ([e-mail]), Septembre 2002                   %
%***************************************************************************%%

if (nargin <3)
    
    V = [0 ; 2*pi];
    
end


[p , n]          = size(V);


if(p ~=2)
    
    error('V must be 2xn !!');
    
end

n_FA                 = poisrnd(LambdaV , T);                 % Tirage selon une loi de poisson du nombre de F.A par scan. E[mt] = Lambda*V

mean_n_FA            = sum(n_FA)/T;

max_n_FA             = max(n_FA);

O_max_n_FA           = ones(max_n_FA , 1);

ind_T                = (1 : T);

I                    = ind_T(O_max_n_FA , :);

R                    = [zeros(max_n_FA , 1) , triu( ones(max_n_FA ) )];

tp_ind               = find( R( : , n_FA + 1 ) );

le_tp_ind            = length(tp_ind);

Otp_ind              = ones(le_tp_ind , 1);

a                    = V(1 , :);

b                    = V(2 , :) - V(1 , :);


clutter              = [reshape(I( tp_ind ) , le_tp_ind , 1) , a(Otp_ind , :) + b(Otp_ind , :).*rand(le_tp_ind , n)];