（matlab程序）使用 System 对象模拟惯性测量单元测量仿真.rar

%% Introduction to Simulating IMU Measurements % This example shows how to simulate inertial measurement unit (IMU) % measurements using the |imuSensor| System object. An IMU can include a combination of % individual sensors, including a gyroscope, an accelerometer, and a % magnetometer. You can specify properties of the individual sensors using % |gyroparams|, |accelparams|, and |magparams|, respectively. % % In the following plots, unless otherwise noted, only the x-axis % measurements are shown. % Copyright 2018-2019 The MathWorks, Inc. %% Default Parameters % The default parameters for the gyroscope model simulate an ideal signal. % Given a sinusoidal input, the gyroscope output should match exactly. params = gyroparams % Generate N samples at a sampling rate of Fs with a sinusoidal frequency % of Fc. N = 1000; Fs = 100; Fc = 0.25; t = (0:(1/Fs):((N-1)/Fs)).'; acc = zeros(N, 3); angvel = zeros(N, 3); angvel(:,1) = sin(2*pi*Fc*t); imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Ideal Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)') %% Hardware Parameter Tuning % The following parameters model hardware limitations or defects. Some can % be corrected through calibration. %% % |MeasurementRange| determines the maximum absolute value reported by the % gyroscope. Larger absolute values are saturated. The effect is shown by % setting the measurement range to a value smaller than the amplitude of % the sinusoidal ground-truth angular velocity. imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.MeasurementRange = 0.5; % rad/s [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Saturated Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)') %% % |Resolution| affects the step size of the digital measurements. Use this % parameter to model the quantization effects from the analog-to-digital % converter (ADC). The effect is shown by increasing the parameter to a % much larger value than is typical. imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.Resolution = 0.5; % (rad/s)/LSB [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Quantized Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)') %% % |AxesMisalignment| is the amount of skew in the sensor axes. This skew % normally occurs when the sensor is mounted to the PCB and can be % corrected through calibration. The effect is shown by skewing the x-axis % slightly and plotting both the x-axis and y-axis. imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.AxesMisalignment = [2 0 0]; % percent [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1:2), '--', t, gyroData(:,1:2)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Misaligned Gyroscope Data') legend('x (ground truth)', 'y (ground truth)', ... 'x (gyroscope)', 'y (gyroscope)') %% % |ConstantBias| occurs in sensor measurements due to hardware defects. % Since this bias is not caused by environmental factors, such as % temperature, it can be corrected through calibration. imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.ConstantBias = [0.4 0 0]; % rad/s [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Biased Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)') %% Random Noise Parameter Tuning % The following parameters model random noise in sensor measurements. More % information on these parameters can be found in the % <docid:fusion_ug#mw_aa75f9c3-b6d6-45af-b219-65102598c2be % Inertial Sensor Noise Analysis Using Allan Variance> example. %% % |NoiseDensity| is the amount of white noise in the sensor measurement. It % is sometimes called angle random walk for gyroscopes or velocity random % walk for accelerometers. % rng('default') imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.NoiseDensity = 1.25e-2; % (rad/s)/sqrt(Hz) [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('White Noise Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)') %% % |BiasInstability| is the amount of pink or flicker noise in the sensor % measurement. % imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.BiasInstability = 2.0e-2; % rad/s [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Bias Instability Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)') %% % |RandomWalk| is the amount of Brownian noise in the sensor measurement. % It is sometimes called rate random walk for gyroscopes or acceleration % random walk for accelerometers. % imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.RandomWalk = 9.1e-2; % (rad/s)*sqrt(Hz) [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Random Walk Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)') %% Environmental Parameter Tuning % The following parameters model noise that arises from changes to the % environment of the sensor. %% % |TemperatureBias| is the bias added to sensor measurements due to % temperature difference from the default operating temperature. Most % sensor datasheets list the default operating temperature as 25 degrees % Celsius. This bias is shown by setting the parameter to a non-zero value % and setting the operating temperature to a value above 25 degrees % Celsius. imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.TemperatureBias = 0.06; % (rad/s)/(degrees C) imu.Temperature = 35; [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Temperature-Biased Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)') %% % |TemperatureScaleFactor| is the error in the sensor scale factor due to % changes in the operating temperature. This causes errors in the scaling % of the measurement; in other words smaller ideal values have less error % than larger values. This error is shown by linearly increasing the % temperature. imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.TemperatureScaleFactor = 3.2; % %/(degrees C) standardTemperature = 25; % degrees C temperatureSlope = 2; % (degrees C)/s temperature = temperatureSlope*t + standardTemperature; gyroData = zeros(N, 3); for i = 1:N imu.Temperature = temperature(i); [~, gyroData(i,:)] = imu(acc(i,:), angvel(i,:)); end figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Temperature-Scaled Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)') %% % |AccelerationBias| is the bias added to the gyroscope measurement due to % linear accelerations. This parameter is specific to the gyroscope. This % bias is shown by setting the parameter to a non-zero value and using a % non-zero input acceleration. imu = imuSensor('SampleRate', Fs, 'Gyroscope', params); imu.Gyroscope.AccelerationBias = 0.3; % (rad/s)/(m/s^2) acc(:,1) = 1; [~, gyroData] = imu(acc, angvel); figure plot(t, angvel(:,1), '--', t, gyroData(:,1)) xlabel('Time (s)') ylabel('Angular Velocity (rad/s)') title('Acceleration-Biased Gyroscope Data') legend('x (ground truth)', 'x (gyroscope)')