modelstates

States for motion model

Since R2022a

collapse all in page

Syntax

s = modelstates(filter,options)

Description

s = modelstates(filter,options) returns a structure that describes the motion model states tracked by the insEKF filter object.

Tip

After defining an insEKF object with a custom motion model, you can access the model states using the stateparts object function of insEKF.

example

Examples

collapse all

Open Live Script

Customize a 1-D constant velocity motion model used with an insEKF object. Customize the motion model by inheriting from the positioning.INSMotionModel interface class and implement the modelstates and stateTranistion methods. You can also optionally implement the stateTransitionJacobian method. These sections provide an overview of how the ConstantVelocityMotion class implements the positioning.INSMotionModel methods, but for more details on their implementation, see the attached ConstantVelocityMotion.m file.

Implement modelstates method

To model 1-D constant velocity motion, you need to return only the 1-D position and velocity state as a structure. When you add a ConstantVelocityMotion object to an insEKF filter object, the filter adds the Position and Velocity components to the state vector of the filter.

Implement stateTransition method

The stateTransition method returns the derivatives of the state defined by the motion model as a structure. The derivative of the Position is the Velocity, and the derivative of the Velocity is 0.

Implement stateTransitionJacobian method

The stateTransitionJacobian method returns the partial derivatives of stateTransition method, with respect to the state vector of the filter, as a structure. All the partial derivatives are 0, except the partial derivative of the derivative of the Position component, which is the Velocity, with respect to the Velocity state, is 1.

Create and add inherited object

Create a ConstantVelocityMotion object.

cvModel = ConstantVelocityMotion
cvModel = 
  ConstantVelocityMotion with no properties.

Create an insEKF object with the created cvModel object.

filter = insEKF(insAccelerometer,cvModel)
filter = 
  insEKF with properties:

                   State: [5×1 double]
         StateCovariance: [5×5 double]
    AdditiveProcessNoise: [5×5 double]
             MotionModel: [1×1 ConstantVelocityMotion]
                 Sensors: {[1×1 insAccelerometer]}
             SensorNames: {'Accelerometer'}
          ReferenceFrame: 'NED'

The filter state contains the Position and Velocity components.

stateinfo(filter)
ans = struct with fields:
              Position: 1
              Velocity: 2
    Accelerometer_Bias: [3 4 5]

Show customized ConstantVelocityMotion class

type ConstantVelocityMotion.m
classdef ConstantVelocityMotion < positioning.INSMotionModel
% CONSTANTVELOCITYMOTION Constant velocity motion in 1-D

%   Copyright 2021 The MathWorks, Inc.    

    methods 
        function m = modelstates(~,~)
            % Return the state of motion model (added to the state of the
            % filter) as a structure.
            % Since the motion is 1-D constant velocity motion,
            % retrun only 1-D position and velocity state.  
            m = struct('Position',0,'Velocity',0); 
        end
        function sdot = stateTransition(~,filter,~, varargin)
            % Return the derivative of each state with respect to time as a
            % structure.

            % Derivative of position = velocity.
            % Derivative of velocity = 0 because this model assumes constant
            % velocity.

            % Find the current estimated velocity
            currentVelocityEstimate = stateparts(filter,'Velocity');

            % Return the derivatives
            sdot = struct( ...
                'Position',currentVelocityEstimate, ...
                'Velocity',0); 
        end
        function dfdx = stateTransitionJacobian(~,filter,~,varargin)
            % Return the Jacobian of the stateTransition method with
            % respect to the state vector. The output is a structure with the
            % same fields as stateTransition but the value of each field is a
            % vector containing the derivative of that state relative to
            % all other states.

            % First, figure out the number of state components in the filter
            % and the corresponding indices
            N = numel(filter.State);  
            idx = stateinfo(filter);  


            % Compute the N partial derivatives of Position with respect to
            % the N states. The partial derivative of the derivative of the
            % Position stateTransition function with respect to Velocity is
            % just 1. All others are 0.
            dpdx = zeros(1,N);  
            dpdx(1,idx.Velocity) =  1;
            
            % Compute the N partial derivatives of Velocity with respect to
            % the N states. In this case all the partial derivatives are 0.
            dvdx = zeros(1,N);

            % Return the partial derivatives as a structure.
            dfdx = struct('Position',dpdx,'Velocity',dvdx);
        end
    end
end

Input Arguments

collapse all

INS filter, specified as an insEKF object.

Options for the INS filter, specified as an insOptions object.

Output Arguments

collapse all

State structure, returned as a structure. The field names of the structure are the names of the states that you want estimate. The insEKF filter object uses the value of each field as the default value of its corresponding state component, and uses the size of the value as the size of the corresponding state component.

Tip

You can use the stateparts object function of the insEKF object to access the states, saved in the filter.

Version History

Introduced in R2022a

See Also

|