insEKF

Inertial Navigation Using Extended Kalman Filter

Since R2022a

Description

The insEKF object creates a continuous-discrete extended Kalman Filter (EKF), in which the state prediction uses a continuous-time model and the state correction uses a discrete-time model. The filter uses data from inertial sensors to estimate platform states such as position, velocity, and orientation. The toolbox provides a few sensor models, such as insAccelerometer, insGyroscope, insGPS, and insMagnetometer, that you can use to enable the corresponding measurements in the EKF. You can also customize your own sensor models by inheriting from the positioning.INSSensorModel (Sensor Fusion and Tracking Toolbox) interface class. The toolbox also provides motion models, such as insMotionOrientation and insMotionPose, that you can use to enable the corresponding state propagation in the EKF. You can also customize your own motion models by inheriting from the positioning.INSMotionModel (Sensor Fusion and Tracking Toolbox) interface class.

Creation

Syntax

filter = insEKF

filter = insEKF(sensor1,sensor2,...,sensorN)

filter = insEKF(___,motionModel)

filter = insEKF(___,options)

Description

filter = insEKF creates an insEKF filter object with default property values. With the default settings, the filter can estimate orientation by fusing accelerometer and gyroscope data.

filter = insEKF(sensor1,sensor2,...,sensorN) configures the filter to accept and fuse data from one or more sensors. The filter saves these sensors in its Sensors property.

filter = insEKF(___,motionModel) configures the filter to use the motion model to predict and estimate state, in addition to any combination of input arguments from previous syntaxes. The filter saves the specified motion model in the MotionModel property.

filter = insEKF(___,options) configures the filter using the insOptions object options.

Properties

State — State vector of extended Kalman filter

N-element real-valued vector

State vector of the extended Kalman filter, specified as an N-element real-valued vector. N is the dimension of the filter state, determined by the specific sensors and motion model used to construct the filter.

Note

In the State property, if a state variable named Orientation has a length of four, the object assumes it is a quaternion. In that case, the filter renormalizes the quaternion and ensures that the real part of the quaternion is always positive.

Data Types: single | double

StateCovariance — State error covariance of extended Kalman filter

N-by-N real-valued positive-definite matrix

State error covariance for the extended Kalman filter, specified as an N-by-N real-valued positive-definite matrix. N is the dimension of the state, specified in the State property of the filter.

Data Types: single | double

AddtiveProcessNoise — Additive process noise for extended Kalman filter

N-by-N real-valued positive definite matrix

Additive process noise for the extended Kalman filter, specified as an N-by-N real-valued positive definite matrix. N is the dimension of the state, specified in the State of the filter.

Data Types: single | double

MotionModel — Motion model used in extended Kalman filter

insMotionOrientation object | insMotionPose object | object inheriting from positioning.INSMotionModel class

This property is read-only.

Motion model used in the extended Kalman filter, specified as an insMotionOrientation object , an insMotionPose object, or an object inheriting from the positioning.INSMotionModel interface class. Specify a motion model using the motionModel input argument.

Data Types: object

Sensors — Sensors fused in extended Kalman filter

{insAccelerometer,insGyroscope} (default) | cell array of inertial sensor objects

This property is read-only.

Sensors fused in the extended Kalman filter, specified as a cell array of inertial sensor objects. An inertial sensor object is one of these objects:

• An insAccelerometer object

• An insMagnetometer object

• An insGPS object

• An insGyroscope object

• An object inheriting from the positioning.INSSensorModel interface class

Data Types: cell

SensorNames — Names of sensors

cell array of character vectors

This property is read-only.

Names of the sensors, specified as a cell array of character vectors. By default, the filter names the sensors using the format 'sensorname_n', where sensorname is the name of the sensor, such as Accelerometer, and n is the index for additional sensors of the same type.

To customize the sensor names, specify the options input when constructing the filter.

Example: {'Accelerometer' 'Accelerometer_1' 'Accelerometer_2' 'Gyroscope'}

Data Types: cell

ReferenceFrame — Reference frame of extended Kalman filter

"NED" (default) | "ENU"

This property is read-only.

Reference frame of the extended Kalman filter, specified as "NED" for the north-east-down frame or "ENU" for the east-north-up frame.

To specify the reference frame as "ENU", specify the options input when constructing the filter.

Data Types: char | string

Object Functions

predict	Predict state estimates forward in time for insEKF
fuse	Fuse sensor data for state estimation in insEKF
residual	Residual and residual covariance from state measurement for insEKF
correct	Correct state estimates in insEKF using direct state measurements
stateparts	Get and set part of state vector in insEKF
statecovparts	Get and set part of state covariance matrix in insEKF
stateinfo	State vector information for insEKF
estimateStates	Batch fusion and smoothing of sensor data
tune	Tune insEKF parameters to reduce estimation error
createTunerCostTemplate	Create template of tuner cost function
tunerCostFcnParam	First parameter example for tuning cost function
copy	Create copy of insEKF
reset	Reset states for insEKF

Examples

Create insEKF with Different Configurations

Create a default insEKF object. By default, the filter fuses the measurement data from an accelerometer and a gyroscope assuming orientation-only motion.

filter1 = insEKF

filter1 = 
  insEKF with properties:

                   State: [13x1 double]
         StateCovariance: [13x13 double]
    AdditiveProcessNoise: [13x13 double]
             MotionModel: [1x1 insMotionOrientation]
                 Sensors: {[1x1 insAccelerometer]  [1x1 insGyroscope]}
             SensorNames: {'Accelerometer'  'Gyroscope'}
          ReferenceFrame: 'NED'

Create a second insEKF object that fuses data from an accelerometer, a gyroscope, and a magnetometer, as well as models both rotational motion and translational motion.

filter2 = insEKF(insAccelerometer,insGyroscope,insMagnetometer,insMotionPose)

filter2 = 
  insEKF with properties:

                   State: [28x1 double]
         StateCovariance: [28x28 double]
    AdditiveProcessNoise: [28x28 double]
             MotionModel: [1x1 insMotionPose]
                 Sensors: {[1x1 insAccelerometer]  [1x1 insGyroscope]  [1x1 insMagnetometer]}
             SensorNames: {'Accelerometer'  'Gyroscope'  'Magnetometer'}
          ReferenceFrame: 'NED'

Create a third insEKF object that fuses data from a gyroscope and a GPS. Specify the reference frame of the filter as the east-north-up (ENU) frame. Note that the motion model that the filter uses is the insMotionPose object because a GPS measures platform positions.

option = insOptions(ReferenceFrame="ENU");
filter3 = insEKF(insGyroscope,insGPS,option)

filter3 = 
  insEKF with properties:

                   State: [19x1 double]
         StateCovariance: [19x19 double]
    AdditiveProcessNoise: [19x19 double]
             MotionModel: [1x1 insMotionPose]
                 Sensors: {[1x1 insGyroscope]  [1x1 insGPS]}
             SensorNames: {'Gyroscope'  'GPS'}
          ReferenceFrame: 'ENU'

Sequential Fusion of Accelerometer and Gyroscope Data Using insEKF

Load measurement data from an accelerometer and a gyroscope.

load("accelGyroINSEKFData.mat");

Create an insEKF filter object. Specify the orientation part of the state in the filter using the initial orientation from the measurement data. Specify the diagonal elements of the state estimate error covariance matrix corresponding to the orientation state as 0.01.

accel = insAccelerometer;
gyro = insGyroscope;
filt = insEKF(accel,gyro);
stateparts(filt,"Orientation",compact(initOrient));
statecovparts(filt,"Orientation",1e-2);

Specify the measurement noise and the additive process noise. You can obtain these values by using the tune object function of the filter object.

accNoise = 0.1739;
gyroNoise = 1.1129;
processNoise = diag([ ...
    2.8586 1.3718 0.8956 3.2148 4.3574 2.5411 3.2148 0.5465 0.2811 ...
    1.7149 0.1739 0.7752 0.1739]);
filt.AdditiveProcessNoise = processNoise;

Sequentially fuse the measurement data using the predict and fuse object functions of the filter object.

N = size(sensorData,1);
estOrient = quaternion.zeros(N,1);
dt = seconds(diff(sensorData.Properties.RowTimes));
for ii = 1:N
    if ii ~= 1
        % Step forward in time.
        predict(filt,dt(ii-1));
    end
    % Fuse accelerometer data.
    fuse(filt,accel,sensorData.Accelerometer(ii,:),accNoise);
    % Fuse gyroscope data.
    fuse(filt,gyro,sensorData.Gyroscope(ii,:),gyroNoise);
    % Extract the orientation state estimate using the stateparts object
    % function.
    estOrient(ii) = quaternion(stateparts(filt,"Orientation"));
end

Visualize the estimate error, in quaternion distance, using the dist object function of the quaternion object.

figure
plot(rad2deg(dist(estOrient,groundTruth.Orientation)))
xlabel("Samples")
ylabel("Distance (degrees)")
title("Orientation Estimate Error")

Batch Fusion and State Smoothing Using insEKF

Load measurement data from an accelerometer and a gyroscope.

load("accelGyroINSEKFData.mat");

Create an insEKF filter object. Specify the orientation part of the state in the filter using the initial orientation from the measurement data. Specify the diagonal elements of the state estimate error covariance matrix corresponding to the orientation state as 0.01.

filt = insEKF;
stateparts(filt,"Orientation",compact(initOrient));
statecovparts(filt,"Orientation",1e-2);

Specify the measurement noise and the additive process noise. You can obtain these values by using the tune object function of the filter object.

measureNoise = struct("AccelerometerNoise", 0.1739, ...
    "GyroscopeNoise", 1.1129);
processNoise = diag([ ...
    2.8586 1.3718 0.8956 3.2148 4.3574 2.5411 3.2148 0.5465 0.2811 ...
    1.7149 0.1739 0.7752 0.1739]);
filt.AdditiveProcessNoise = processNoise;

Batch-estimate the states using the estimateStates object function. Also, obtain the estimates after smoothing.

[estimates,smoothEstimates] = estimateStates(filt,sensorData,measureNoise);

Visualize the estimated orientation in Euler angles.

figure
t = estimates.Properties.RowTimes;
plot(t,eulerd(estimates.Orientation,"ZYX","frame"));
title("Estimated Orientation");
ylabel("Degrees")

Visualize the estimated orientation after smoothing in Euler angles.

figure
plot(t,eulerd(smoothEstimates.Orientation,"ZYX","frame"));
title("Smoothed Orientation");
ylabel("Degrees")

Visualize the estimate error, in quaternion distance, using the dist object function of the quaternion object.

trueOrient = groundTruth.Orientation;
plot(t,rad2deg(dist(estimates.Orientation, trueOrient)), ...
     t,rad2deg(dist(smoothEstimates.Orientation, trueOrient)));
title("Estimated and Smoother Error");
legend("Estimation Error","Smoothed Error")
xlabel("Time");
ylabel("Degrees")

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Version History

Introduced in R2022a