ACS6124 Multisensor and Decision Systems Part I – Multisensor Systems Assessment
ACS6124 Multisensor and Decision Systems
Part I – Multisensor Systems
Assessment
Lecturer: Yuanbo Nie
email: y.nie@sheffield.ac.uk
2024/2025
This is the first of two assignments for the module ACS6124, with this one assessing the learning
outcomes of the Multisensor Systems component of the module.
Assignment Code: ACS6124-001
Assignment weighting: 50% of the module assessment marks.
Assignment released: 10 Feb 2025 (Monday, Week 1, Semester 2)
Assignment due: 12:00pm (noon), 31 Mar 2024 (Monday, Week 8, Semester 2)
Late submission penalty will be applied
Assignment format: A report of 6 pages maximum (excluding the Appendix)
A development experience exported from mySkills uploaded
Accompanied Matlab code to be submitted and evaluated
Submission mode: Report, code and mySkills development experience must be
submitted electronically via Blackboard
1 Assignment Processes
Report format details
The report should be in a pdf form using a margin (top, bottom, left and right) of no less than
20mm, and text should be size 12 point (minimum 11pt) with 1.5 line spacing.
Submission mode details
This report and the code are to be submitted via Turnitin using the Assessment tab on Blackboard
by 12:00 (noon), Monday 31 Mar 2024 (Week 8 of Semester 2). Please note that you will be allowed
only a single submission and so the first submission made will be the one assessed.
Penalties for Late Submission
Work that is submitted after this deadline (without medical or other similar evidence
unless agreed) will incur a penalty for late submission. The usual late submission penalty of 5% documented
reduction in the mark for every working day (or part thereof) that the assignment is late and a mark
of 0 for submission more than 5 days late. For more information http://www.shef.ac.uk/ssid/
assessment/grades-results/submission-marking
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Unfair Means
This is an individual assignment. You should not discuss the assignment with other students or
work together with other students in its completion. The assignment must be wholly your own
work. References must be provided to any other work that is used as part of the assignment. Any
suspicion of the use of unfair means will be investigated and may lead to penalties. For more
information see: http://www.shef.ac.uk/ssid/unfair-means
Extenuating Circumstances
If you have any medical or special circumstances that you believe may affect your performance on
the assignment then you should raise these with the Module Leader at the earliest opportunity.
You will also need to submit an extenuating circumstances form. More information at:
http://www.shef.ac.uk/ssid/forms/circs
Support
Please email me if you have any questions about the assignment.
Feedback
Written feedback will be provided on Blackboard within 15 working days, in line with Department
guidelines.
Assignment Technical Requirements
Write a report fulfilling the requirements of the Task 1 to 3 of the Assignment. The report should
follow the specified guidelines and meet the objectives provided for each task. Task 4 will be graded
through the evaluation of the submitted code.
Links to Labs
The workload of this assignment has been designed based on the assumption that the lab exercises
have all been successfully completed.
• Tasks requested in this assignment are extensions of lab activities to a real-world engineering
problem. Feel free to reuse any of the scripts you have developed in the labs, specifically you
may need
– From Lab A & B, scripts for simulation and linearisation of nonlinear dynamical systems,
and conversion from 2 2 Assignment: Integrated Navigation for Aircraft This
project concerns the accurate estimation of aircraft position, airspeed
body components and attitude with an integrated system consisting of inertial measurement units (IMUs), the global positioning system (GPS), and air-data systems. A schematic drawing of the system architecture is shown in Figure 1. State Estimation and Fault Detection Algorithm Inertial Measurement Unit Multi-Antenna GPS Air-data System Acceleration Rotational Rates Position & Altitude Ground Speed Components Attitude True Airspeed Angle of Attack Side-slip Angle Position Altitude Airspeed Body Components Roll Angle Pitch Angle Yaw Angle Windspeed Components Sensor Biases
& Faults
Figure 1: Overview of the Sensor Fusion Architecture.
The IMU uses rate gyros for aircraft attitude and heading angle estimation. However, these sensors
have a major drawback of long-term instability caused by uncertainties (biases, drifts, scale factors,
misalignments, nonlinearities and noises). Attitude and heading estimation with only rate gyros
yields time accumulated errors.
The integration of rate gyros with other long-term stable sensors has the capacity of calibrating
gyros in flight. The integrated navigation system has the following advantages:
• long-term stable measurements,
• sensor system uncertainties can be estimated and removed,
• high data rate for flight control and display, • sensor fault detection and re -
configuration capability,
• flexible integration structures . aircraft position (xE , yE , zE in m), ground speed earth components (uE , vE , wE in m/s) and attitude (roll angle ϕ, pitch angle θ, yaw angle ψ in rad), and the air-data sensors will offer processed true airspeed (VTAS in m/s), angle of attack (α in rad), and side slip angle (β in rad). 3 2.1 Dynamics Model For the Kalman filter design, we would like to express the system dynamics in the following general format x˙(t) = f(x(t), u(t), t) + g(x(t), u(t), ω(t)), (1) y(t) = h(x(t), t), (2) ym(tk) = y(tk) + ν(tk), k = 1, 2, . . . (3) with kinematic equation (1), observation equation (2) and measurement model (3). x is the state variable, u is the input variable, y is the output variable with ym the measured outputs, ω is the system noise and ν is the output measurement noise. Note that the notation used here is different from the lecture slides to avoid confusions with problem-specific variable names. 2.1.1 Kinematic Equations For aircraft trajectory in a short range, the simplified kinematic equations using flat non-rotating earth assumption are x˙E =(Vu cos θ + (Vv sinϕ+ Vw cosϕ) sin θ) cosψ − (v cosϕ− w sinϕ) sinψ + VwxE , y˙E =(Vu cos θ + (Vv sinϕ+ Vw cosϕ) sin θ) sinψ + (v cosϕ− w sinϕ) cosψ + VwyE , z˙E =− Vu sin θ + (Vv sinϕ+ Vw cosϕ) cos θ + VwzE , V˙u =Ax − ggrav sin θ + rVv − qVw, V˙v =Ay + ggrav cos θ sinϕ+ pVw − rVu, V˙w =Az + ggrav cos θ cosϕ+ qVu − pVv, ϕ˙ =p+ q sinϕ tan θ + r cosϕ tan θ,
θ˙ =q cosϕ− r sinϕ,
ψ˙ =q
sinϕ
cos θ
+ r
cosϕ
cos θ
,
with VwxE , VwyE , VwzE wind components in the earth reference frame. They are assumed to be
constant, ie V˙wxE = 0, V˙wyE = 0 and V˙wzE = 0 but they are also unknowns hence need to be
estimated by the algorithm. u, v and w are airspeed components in the aircraft's body reference
frame which also cannot be directly measured. ggrav = 9.81m/s
2 is the gravitational acceleration.
In classical aircraft dynamic models, the acceleration and angular rates are additionally expressed
as functions of flight control inputs, aerodynamic coefficients and other state and output variables.
To avoid working with such a model with complicated aerodynamic coefficients, for this assignment,
we will use a multi-sensor integration structure where the IMU measurements directly serve as
the system input and the GPS and air-data measurements serve as the outputs. Note that this
is sufficient to estimate all the state variables. This sensor fusion scheme is often used in flight
tests where the aerodynamic coefficients are not yet known in good precision, as well as in fault-
tolerant flight control designs where structure failures in flight could lead to changes in aerodynamic
characteristics. Therefore, we have state vector
x = [xE , yE , zE , Vu, Vv, Vw, ϕ, θ, ψ, VwxE , VwyE , VwzE ]
⊤,
input vector u = [Ax, Ay, Az, p, q, r]
⊤, and output vector
y = [xGPS , yGPS , zGPS , uGPS , vGPS , wGPS , ϕGPS , θGPS , ψGPS , VTAS , α, β]
⊤.
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2.1.2 Observation Equations
A GPS receiver with multiple antennas offers 9 observables. They are the position coordinates,
ground speed components, and attitude angles:
xGPS =xE ,
yGPS =yE ,
zGPS =zE ,
uGPS =uE = (Vu cos θ + (Vv sinϕ+ Vw cosϕ) sin θ) cosψ − (Vv cosϕ− Vw sinϕ) sinψ + VwxE ,
vGPS =
ve
Airdata sensors give three variables: the true airspeed vTAS, angle of attack α and side slip angle
β:
VTAS =
√
V 2u + V
2
v + V
2
w ,
α =tan−1
Vw
Vu
,
β =tan−1
Vv√
V 2u + V
2
w
,
Together they form the observation model (2).
2.1.3 Measurement Equations
In the kinematic equation (1) and measurement model (3), we see the appearance of system noise
ω and output measurement noise ν. For this assignment, we will model the process noises related
to input measurements, ie for the input measurements from the IMU we have
Axm = Ax + ωAx , Aym = Ay + ωAy , Azm = Az + ωAz ,
pm = p+ ωp, qm = q + ωq, rm = r + ωr,
with ω = [ωAx , ωAy , ωAz , ωp, ωq, ωr]
⊤ and
E{ω(tk)} = 0, E{ω(ti)ω⊤(tj)} = Qδij , Q = diag(σ2Ax , σ2Ay , σ2Az , σ2p, σ2q , σ2r).
For outputs from the GPS and air-data sensors, consider
xGPSm = xGPS + νx, yGPSm = yGPS + νy, zGPSm = zGPS + νz,
uGPSm = uGPS + νu, vGPSm = vGPS + νv, wGPSm = wGPS + νw,
ϕGPSm = ϕGPS + νϕ, θGPSm = θGPS + νθ, ψGPSm = ψGPS + νψ,
VTASm = VTAS + νVTAS , αm = α+ να, βm = β + νβ ,
with measurement noises ν = [νx, νy, νz, νu, νv, νw, νϕ, νθ, νψ, νVTAS , να, νβ ]
⊤ and
E{ν(tk)} = 0, E{ν(ti)ν⊤(tj)} = Rδij ,
R = diag(σ2xE , σ
2
yE , σ
2
zE , σ
2
u, σ
2
v , σ
2
w, σ
2
ϕ, σ
2
θ , σ
2
ψ, σ
2
VTAS , σ
2
α, σ
2
β).
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Therefore, we have an input measurement vector cm = [Axm , Aym , Azm , pm, qm, rm]
⊤ and output
measurement vector
dm = [xGPSm , yGPSm , zGPSm , uGPSm , vGPSm , wGPSm , ϕGPSm , θGPSm , ψGPSm , VTASm , αm, βm]
⊤.
The standard deviations (σ) of the input and output noises are assumed to be constant and known
in nominal flight: 0.01 m/s−2 for accelerations (ie for σAx , σAy , σAz ), 0.01 deg/s for angular rates
(ie for σp, σq, σr), 5 meters for GPS position measurements (ie for σxE , σyE ), 10 meters for GPS
altitude measurements (ie for σzE ), 0.1 m/s for velocity measurements (ie for σu, σv, σw, σVTAS ),
and 0.1 degrees for angle measurements (ie for σϕ, σθ, σψ, σα, σβ). When using these numbers,
note that for dynamics equations, all angles should be in radians and all angular rates should be in
rad/s. Also, the input and output noises are assumed to be uncorrelated, ie E{ω(ti)ν⊤(tj)} = 0.
2.2 Assignment Tasks
Task 1: Kalman Filter Design
You will need to design an integrated GPS/IMU/Air-data navigation system by constructing a navigation
model with 12 states (position, velocity, attitude and wind components), 6 inputs and 12
measurements, and adapt your scalar extended Kalman filter (EKF) or iterated extended Kalman
filter (IEKF) algorithm from the lab. The choice between EKF and IEKF is at your own discretion.
If you are familiar with other estimation schemes, eg unscented Kalman filter, feel free to use them
as well.
For this task you will work with dataTask1.mat which contains input measurements ck, the outputs
dk and the time variables t and dt in seconds. ck and dk are matrices with rows the recordings
of different time instances and the columns the variables are in the same ordering of cm and dm
presented earlier.
You will realize that in comparison to your lab exercise, the expected value for the initial condition
xˆ0|0 = E{x(t0)} and the error covariance P0|0 are not explicitly given. You could use engineering
judgment to configure this.
• For state variables that can be directly measured (xE , yE , zE , ϕ, θ, ψ), simply assume the
initial value to be the same as the noisy measurements at the first time instance.
• The airspeed body components (Vu,Vv,Vw) we do not directly measure but we know the air-
speed VTAS is measured and we know the airspeed will be mostly in the forward traveling
direction. So it is reasonable to configure the initial guess of (Vu,Vv,Vw) to be (VTAS ,0,0). As
for the wind speeds, we can simply guess them to be zero. The key point of making these
engineering guesses is that you need to tell the scheme through P0|0 that these guesses are far
from being accurate, by making the variance/standard deviation suitably large.
Task 1.1: For the use of the Kalman filter, you will need to derive the Jacobian matrices for
our nonlinear system. Obtain the expressions for your derived Jacobian matrices F = ∂f∂x ,
G = ∂g∂ω and H =
∂h
∂x with f and g from (1)
and
h
from
( 2 )
. row, 4th column of the H matrix. 6 Hints: substitute IMU measurement equations into the kinematic equations, then group dif- ferent terms appropriately into f(x(t), u(t), t) and g(x(t), ω(t)). Equations for the kinematic model and observation model can be found in usefulEquations.m, and you can use Matlab's Symbolic Math Toolbox to help you compute the required matrices (code developed in the
labs may be helpful). [6%]
Task 1.2: Complete the Kalman Filter design for the integrated navigation system (code
developed in Lab D may be helpful). In clear figures, plot the trajectories of the estimated 12
states (ie not outputs) and comment on your findings. [12%]
Task 2: Estimating and Removing Sensor Biases
All sensors will have errors even in nominal operations, typically in the form of scale factors, biases
and noises. In Task 1 we have observed the handling of noise measurements with the Kalman filter.
In this part of the assignment, we will extend the algorithm to deal with sensor biases.
In particular, the IMU is known to develop gyroscopic bias over time. dataTask2.mat contains the
sensor measurement for the very same flight trajectory, with the addition of IMU sensor biases.
Task 2.1: Re-run Task 1 using dataTask2.mat and compare the trajectories of the estimated
states with the ones obtained from the previous task. Show the comparison in a clear figure
(ie trajectories from both tasks shown together in the same figure) for the variables with the
most pronounced differences (around 2-3 variables would be a good selection). Explain the
reasons behind the degradation in the estimation performance. [6%]
In Kalman filter design, a common practice to handle unknown biases is to simply include them as
variables to be estimated. Let's consider the case of constant bias where we have
Axm = Ax + bAx + ωAx , Aym = Ay + bAy + ωAy , Azm = Az + bAz + ωAz ,
pm = p+ bp + ωp, qm = q + bq + ωq, rm = r + br + ωr,
b˙Ax = 0, b˙Ay = 0, b˙Az = 0,
b˙p = 0, b˙q = 0, b˙r = 0.
Task 2.2: Implement the integrated navigation scheme using the updated navigation model
with 18 states (x = [xE , yE , zE , Vu, Vv, Vw, ϕ, θ, ψ, bAx , bAy , bAz , bp, bq, br, VwxE , VwyE , VwzE ]
⊤),
6 inputs and 12 measurements (as before). Verify that the influences of unknown biases
have been successfully mitigated. In a clear figure, present your estimation of the bias terms
(bAx , bAy , bAz , bp, bq, br) through the Kalman filter scheme.
Note: Please remember to scale the figures appropriately otherwise you may lose marks for
this task. The convergence of the Kalman filter can cause initial spikes, hence the scaling of
the figures should be adjusted so that these spikes do not cause the value of the actual bias
terms to become negligibly small to be identified from the figures. If the numerical value of the
bias terms can not be clearly read from the figures, we have no means to determine whether
they are correct. [12%]
Task 3: Estimating Additive Faults
The integrated navigation framework with Kalman filter can also be used for sensor fault detection
and diagnosis (FDD). We will consider additive faults which can be viewed as a generalization of
the bias case, but with bias terms no longer constant.
Task 3.1: Run the state estimation with the updated navigation model using data from
dataTask3.mat and identify the first instance when the sensor fault occurs (there may be more
than 1 instance as you will see later). In a clear figure, present the history of the innovation
of the EKF/IEKF and indicate the first instance of fault occurrence with a vertical dashed
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line. Comment on your observation and provide some rationales behind the use of Kalman
filter innovation/residual for fault detection. Note that in this task you just need to observe
the influence of the faults. [7%]
Kalman filter-based FDD often model the faults as a random walk process. For our problem, we
will have
bAx(tk+1) = bAx(tk) + ωbAx (tk), bAy (tk+1) = bAy (tk) + ωbAy (tk), bAz (tk+1) = bAz (tk) + ωbAz (tk),
bp(tk+1) = bp(tk) + ωbp(tk), bq(tk+1) = bq(tk) + ωbq (tk), br(tk+1) = br(tk) + ωbr (tk).
Note these are dynamical equations in discrete-time. With this implementation the b terms are now
fault signals (containing noises, faults and biases).
Task 3.2: Update the navigation model using 6 additional noise terms with the faults (bias)
modeled as above. Now you should have
ω = [ωAx , ωAy , ωAz , ωp, ωq, ωr, ωbAx, ωbAy, ωbAz , ωbp , ωbq , ωbr ]
⊤.
Hints: In Task 2 you have, for example, b˙Ax = 0 for the b terms as continuous-time dynamics
equations. The equivalent discrete-time expression will be bAx(tk+1) = bAx(tk). In this task,
you need to update the expression to bAx(tk) = bAx(tk) +ωbAx (tk) in discrete-time. There are
two equivalent ways of implementing them in the setup:
– You can directly modify the Γ matrix after c2d, by appending it with a sub-block of zeros
and a sub-block of an identity matrix.
– Alternatively, you can transform bAx(tk) = bAx(tk) + ωbAx (tk) back to continuous-time
and obtain b˙Ax =
1
TsωbAx , with Ts the sampling time. You can use this expression instead
of b˙Ax = 0 in your linearization script and compute the G matrix accordingly, then use
c2d to obtain Γ as before.
The standard deviation of the additional noise terms needs to be set to relatively high values
to capture the fault signals (Hint: which matrix contain the standard deviation/variance of
the process noises?). Iteratively improve your design and verify that the influences of faults
have been successfully mitigated. Check the history of the innovation of the EKF/IEKF again
and compare it with what you have observed in Task 3.1. Briefly explain the differences. [10%]
Task 3.3: Finally, present your estimation of the fault signals through the Kalman filter scheme
in a clear figure. When generating the figure, scale the axes to clearly show the features of the
fault, rather than the Kalman filter convergence process.
[12%]
Task 4: Estimation Performance Competition
In this part of the assignment, you will submit your design of the state estimation and its per-
formance will be measured against the work of your fellow peers. Use the Blackboard template
SubmissionTemplate.m and follow the instructions inside to prepare the script. As a reminder:
• Rename the function in the format of SID + your student ID as on Blackboard (eg if your
ID is 21010000, name the function as SID21010000 and submit it as SID21010000.m)
• The function has 4 input variables
– uk: input measurements, a N × 6 matrix with rows the recordings of different time
instances and the columns being [Axm , Aym , Azm , pm, qm, rm],
– yk: output measurements, a N ×12 matrix with rows the recordings of different time in-
stances and the columns being [xGPSm, yGPSm, zGPSm, uGPSm, vGPSm, wGPSm, ϕGPSm,
θGPSm, ψGPSm, VTASm, αm, βm],
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– t: time vector of dimension N × 1,
– dt: uniform time step size,
• From the 4 input variables, the function must return the following 2 output variables
– x est: estimated state trajectory: a N × 12 matrix with rows the recordings of different
time instances and the columns being [xE , yE , zE , u, v, w, ϕ, θ, ψ, VwxE , VwyE , VwzE ],
– b est: estimated state trajectory for the bias and fault terms, a N × 6 matrix with rows
the recordings of different time instances and the columns being [bAx , bAy , bAz , bp, bq,
br],
• Do not use 'clear all' type of commands inside your function as they will erase your function
input variables as well.
• Do not plot figure
• Do not load any external data
• You should ONLY submit a single, standalone .m file (should be able to run without the need
of other files), otherwise it can not be automatically graded leading to a mark of 0 for Task 4.
You should include all sub-routines as sub-functions or nested functions.
CAUTION: There are currently two flight tapes provided that you could use to test your Task 4
design with, namely: dataTask3.mat and dataTask4.mat. dataTask4.mat is provided in order for
YOU to additionally test the script that you have made.
WARNING: It is your responsibility to ensure the proper functioning of your script, as run-time
errors can lead to a mark of 0% for this task. You can use testYourScript.m to test your script on
different flight tapes, to help you identify syntax errors or output variable dimension mismatches.
However, note that testYourScript.m is not a comprehensive test, successfully run in one of the
flight tapes doesn't reflect YOUR script is completely robust towards the system and it is YOUR
responsibility to test your script with different flight tapes to ensure the ROBUSTNESS of your
script in handling different scenarios. testYourScript.m functions as a mimic to what you would
do in a real life situation when you are hired in the industry. This function will take the input
provided, process the data with necessary computations, and return the output accordingly, in this
case the total run time and the run status of your program (error or successful).
After submission, the performance will be evaluated as follows based on another flight trajectory:
• Estimation Performance The estimation performance will be evaluated based on the weighted
mean squared error (WMSE) between the estimated states and the true states. The assigned
weights are shown as below for the nominal states:
States
10 10 5000 5000 5000 10 10 10
and for the bias/fault states:
States bAx bAy bAz bp bq br
Weights 1 1 1 50 50 50
[20%]
• Computational Performance You should make your code execute as efficiently as possible in
terms of computations. The code execution time of your submission will also be compared
with your peers. Please make sure your code finishes running within 5 minutes otherwise you
will not receive any mark for THE WHOLE Task 4. [5%]
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Task 5: Reflections
This assignment has been designed to bridge the gap between academic lectures and real-world
engineering practices. Please complete a Development Experience in mySkills, reflecting on the
following aspects:
• How did you break down a seemingly large engineering problem into smaller, more manageable
steps to reach the end goal?
• How does the time spent analyzing the problem compare to the time spent implementing the
solution? Would you approach this differently next time?
• Throughout the tasks, you do not have access to the true state trajectories to verify your
design. How did this impact your completion of the assignment? What engineering judgment
did you rely on to ensure your design was correct and would perform well with a new set of
data that has not been made available to you?
You can use Figure 2 as a template for logging the development experience. Once you've completed
your reflection, export the development experience as a PDF and upload it to Blackboard. Some
useful videos to help you navigate the platform:
• How to Get Started with mySkills
• Reflecting and recording personal development in mySkills
• Exporting your mySkills profile as a PDF
[10%]
3 Marking Criteria
This assignment is marked out of 100% and contributes to 50% of the overall module assessment.
Among this 100%, 25% is associated with the performance of the submitted Matlab script as part
of Task 4, 10% is associated with the development experience recorded in mySkills, and 65% is from
the marking of the report. The full range (0-65%) will be used when marking the report. Most part
of this assignment requires you to present the results in clear figures. Therefore, the marking will
have a strong emphasis on the clarity and well thoughtfulness of these figures (eg the selection of
relevant data to present, the scaling of the axis). Please aim to generate clear figures and write
concise discussions, keep in mind that the 6 pages allowance is a limit rather than a target. Contents
beyond a maximum of 6 pages (including all texts and figures) will be disregarded.
Additionally, penalties up to 5% for EACH violation of the following requirements WILL ALWAYS
be applied:
• minimum 11pt font size for the main text
• minimum 10pt font size texts in the figures
• page margin (top, bottom, left and right) no less than 20mm, strictly applied for both texts
and figures
• proper figure numbering and references (eg not appears to be ??). Note all figures should be
referred in the text.
• proper figure captions, axis labels, legends, etc.
WARNING: You may lose up to 25% of your Part I assignment grade due to formatting issues.
DOUBLE CHECK before you submit. Note that most LATEX templates do not allow arbitrary font
sizes (eg 11.5pt).
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Marking Criteria / Comments Marks
Task 1.1
• Correctly derive the expression for matrix F / 2
• Correctly derive the expression for matrix G / 2
• Correctly derive the expression for matrix H / 2
Task 1.2
• Clear figures with the trajectory of the estimated states / 6
• The estimation of state trajectories in the figures are correct / 6
Task 2.1
• A clear figure showing key differences / 4
• Explanations of the degradation in estimation performance / 2
Task 2.2
• A clear figure with the estimation of bias terms / 6
• The estimation of bias terms in the figures are corrects / 6
Task 3.1
• A figure showing the history of KF innovation and the first instance of fault
occurrence
/ 3
• The first instance of fault occurrence indicated is correct / 2
• Rationales behind the use of KF innovation for fault detection / 2
Task 3.2
• A figure showing the comparison of KF innovations / 4
• Briefly explain why you believe the faults are successfully mitigated / 6
Task 3.3
• A clear figure with the estimation of fault signals / 6
• The estimation of additive faults in the figures are corrects / 6
4 Submission checklist
□ Check your report against format requirements specified above.
□ Check your report against the marking criteria specified above.
□ Your report submitted via the Turnitin link on Blackboard.
□ Your exported development experience from mySkills uploaded to Blackboard as a PDF file.
□ Your Matlab function named with your student ID, eg SID21010000.m, submitted via the
code submission link on Blackboard. You should ONLY submit this single .m file as requested
in Task 4, otherwise it cannot be automatically graded leading to a mark of 0 for Task 4.
5 mySkills Development Experience Template