Customize a sensor model used with the insEKF object. The sensor measures the velocity state, including a bias affected by random noise.
Customize the sensor model by inheriting from the positioning.INSSensorModel interface class and implementing its methods. Note that only the measurement method is required for implementation in the positioning.INSSensorModel interface class. These sections provide an overview of how the BiasSensor class implements the positioning.INSSensorModel methods, but for details on their implementation, see the details of the implementation are in the attached BiasSensor.m file.
Implement sensorStates method
To model bias, the sensorStates method needs to return a state, Bias, as a structure. When you add a BiasSensor object to an insEKF filter object, the filter adds the bias component to the state vector of the filter.
Implement measurement method
The measurement is the velocity component of the filter state, including the bias. Therefore, return the summation of the velocity component from the filter and the bias.
Implement measurementJacobian method
The measurementJacobian method returns the partial derivative of the measurement method with respect to the state vector of the filter as a structure. All the partial derivatives are 0, except the partial derivatives of the measurement with respect to the velocity and bias state components.
Implement stateTransition method
The stateTransiton method returns the derivative of the sensor state defined in the sensorStates method. Assume the derivative of the bias is affected by a white noise with a standard deviation of 0.01. Return the derivative as a structure. Note that this only showcases how to set up the method, and does not correspond to any practical application.
Implement stateTransitionJacobian method
Since the stateTransiton function does not depend on the state of the filter, the Jacobian matrix is 0.
Create and add inherited object
Create a BiasSensor object.
biSensor =
BiasSensor with no properties.
Create an insEKF object with the biSensor object.
filter =
insEKF with properties:
State: [17x1 double]
StateCovariance: [17x17 double]
AdditiveProcessNoise: [17x17 double]
MotionModel: [1x1 insMotionPose]
Sensors: {[1x1 BiasSensor]}
SensorNames: {'BiasSensor'}
ReferenceFrame: 'NED'
The filter state contains the bias component.
ans = struct with fields:
Orientation: [1 2 3 4]
AngularVelocity: [5 6 7]
Position: [8 9 10]
Velocity: [11 12 13]
Acceleration: [14 15 16]
BiasSensor_Bias: 17
Show customized BiasSensor class
classdef BiasSensor < positioning.INSSensorModel
%BIASSENSOR Sensor measuring velocity with bias
% Copyright 2021 The MathWorks, Inc.
methods
function s = sensorstates(~,~)
% Assume the sensor has a bias. Define a Bias state to enable
% the filter to estimate the bias.
s = struct('Bias',0);
end
function z = measurement(sensor,filter)
% Measurement is the summation of the velocity measurement and
% the bias.
velocity = stateparts(filter,'Velocity');
bias = stateparts(filter,sensor,'Bias');
z = velocity + bias;
end
function dzdx = measurementJacobian(sensor,filter)
% Compute the Jacobian, which is the partial derivative of the
% measurement (velocity plus bias) with respect to the filter
% state vector.
% Obtain the dimension of the filter state.
N = numel(filter.State);
% The partial derviative of the Bias with respect to all the
% states is zero, except the Bias state itself.
dzdx = zeros(1,N);
% Obtain the index for the Bias state component in the filter.
bidx = stateinfo(filter,sensor,'Bias');
dzdx(:,bidx) = 1;
% The partial derivative of the Velocity with respect to all the
% states is zero, except the Velocity state itself.
vidx = stateinfo(filter,'Velocity');
dzdx(:,vidx) = 1;
end
function dBias = stateTransition(~,~,dt,~)
% Assume the derivative of the bias is affected by a zero-mean
% white noise with a standard deviation of 0.01.
noise = 0.01*randn*dt;
dBias = struct('Bias',noise);
end
function dBiasdx = stateTransitonJacobian(~,filter,~,~)
% Since the stateTransiton function does not depend on the
% state of the filter, the Jacobian is all zero.
N = numel(filter.State);
dBiasdx = zeros(1,N);
end
end
end
