BaseObserverModel

classdef BaseObserverModel < handle
%BASEOBSERVERMODEL Abstract base class for observer models
%   BaseObserverModel is a base class that encapsulates noisy process
%   observation.  It is abstract, and cannot be directly instantiated.
%
%   BaseObserverModel methods:
%      simulate     - simulate a noisy measurement.
%      observerData - given a Gaussian prediction for the state and an actual 
%                     measurement, returns covariance data based on
%                     observation model.
%
    
    properties
        h  % function handle for h(x,t) or h(x,t,v)
        R  % covariance of observation noise
        isLinearNoise = true;  % true if the noise is linear
        
        J_x  % function handle for Jacobian of h with respect to x
        J_w  % function handle for Jacobian of h with respect to w
             %   used only if isLinear == false        
    end
    
    properties (Access = protected)
        cholR;
    end
        

    methods
        function obsModel = BaseObserverModel(h, R, varargin)
            if nargin == 0
                return;
            end
            
            if isa(h, 'BaseObserverModel')
                assert(nargin == 1, 'Too many arguments');
                
                obsModel.h = h.h;
                obsModel.R = h.R;
                obsModel.isLinearNoise = h.isLinearNoise;
                obsModel.J_x = h.J_x;
                obsModel.J_w = h.J_w;
                
                obsModel.cholR = h.cholR;
            else
                obsModel.h = h;
                obsModel.R = R;
                obsModel.cholR = sdchol(obsModel.R);

                if ~isempty(varargin)
                    % now parse option/value pairs
                    for k = 1:2:length(varargin)
                        obsModel.(varargin{k}) = varargin{k+1};
                    end                    
                end
            end
        end
        
        function observation = simulate(this, state, t)
        %SIMULATE Simulate a measurement of the true state
        %  
        
            if nargin == 2
                t = 0;
            end
            
            v = (randn(1, size(this.cholR, 2)) * this.cholR)';
            if this.isLinear
                observation(k) = this.h(state, t) + v;
            else
                observation(k) = this.h(state, v, t);
            end
        end
    end
    
    methods (Abstract = true)
        [C_zz C_xz innovation] = observerData(this, predMean, predCov, ...
                                              meas, curTime);
        %OBSERVERDATA 
        %
        %
    end

end