Outline

Overview

Syllabus Summary: This course starts by providing a background to the signals and transforms required to understand modern sensors. It goes on to provide an overview of the workings of typical active sensors (Radar, Lidar and Sonar). It provides insight into basic sensing methods as well as aspects of interfacing and signal processing. It includes both background material and a number of case studies. The course covers the following topics: a) SIGNALS: Convolution, The Fourier Transform, Modulation (FM, AM, FSK, PSK etc), Frequency shifting (mixing) b) PASSIVE SENSORS: Infrared Radiometers, Imaging Infrared, Passive Microwave Imaging, Visible Imaging and Image Intensifiers c) ACTIVE SENSORS THE BASICS: Operational Principles, Time of flight (TOF) Measurement and Imaging of Radar, Lidar and Sonar, Radio Tags and Transponders, Range Tacking, Doppler Measurement, Phase Measurement d) SENSORS AND THE ENVIRONMENT: Atmospheric Effects, Target Characteristics, Clutter Characteristics, Multipath e) ACTIVE SENSORS: ADVANCED TECHNIQUES: Probability of Detection, Angle Measurement and Tracking, Combined Range/Doppler and Angle Tracking, Frequency Modulation and the Fast Fourier Transform, High Range Resolution, Wide Aperture Methods, Synthetic Aperture Methods (SAR) Objectives: The course aims to provide students with a good practical knowledge of a broad range of sensor technologies, operational principles and relevant signal processing techniques. Expected Outcomes: A good understanding of active sensors, their outputs and applicable signal processing techniques. An appreciation of the basic sensors that are available to engineers and when they should be used.

Unit details and rules

Academic unit	Aerospace, Mechanical and Mechatronic
Credit points	6
Prerequisites ?	MTRX3700 or MTRX3760 or equivalent study at another institution
Corequisites ?	None
Prohibitions ?	MECH4720 or MECH8720
Assumed knowledge ?	Strong MATLAB skills and some Electromagnetics
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Graham Brooker, graham.brooker@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final exam Supervised exam	45%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Out-of-class quiz	Post-lecture Quizzes Individquiz to be completed after each lecture to assist with consolidation	10%	Multiple weeks	Typically 30 to 60 minutes	AI allowed
Outcomes assessed:
Practical skill	Laboratory Group submission to be completed after each laboratory session.	25%	Multiple weeks	3 hrs in Lab plus 1 hr post-Lab	AI allowed
Outcomes assessed:
Practical skill	MATLAB tutorial Group Matlab-based analysis	20%	Multiple weeks	2 hrs in tutorial plus additional time	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

MATLAB tutorial: A number of hands-on group tutorials will be undertaken during which students are expected to apply and investigate what they have learned by developing models and software.
Quizzes: A quiz will be held after each lecture to ensure that students have understood the work covered so far.
Lab activities: Weekly group activities during which students will be required to assemble sensing, processing, and actuation hardware that illustrates sensing and signal processing concepts.
Final exam: Open-book examination that will include a number of short-answer questions as well as a longer analysis question. Students are required to pass the exam to pass the unit.

Detailed information for each assessment task can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

5% per day (or part thereof)

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction	Lecture (2 hr)
Week 01	Registration	Practical (3 hr)
Week 02	Filtering and Modulation	Lecture (2 hr)
Week 02	MATLAB - Graphs, Filtering & FFT	Practical (3 hr)
Week 03	Active Ranging Sensors	Lecture (2 hr)
	Modulation	Computer laboratory (1 hr)
	MATLAB - Modulation	Practical (3 hr)
Week 04	Active Imaging Sensors	Lecture (2 hr)
	Modulation	Computer laboratory (1 hr)
	MATLAB - Pulsed Sonar	Practical (3 hr)
Week 05	Signal Propagation	Lecture (2 hr)
	3D Imaging	Computer laboratory (1 hr)
	MATLAB - Imaging	Practical (3 hr)
Week 06	Target Detection in Noise	Lecture (2 hr)
	3D Imaging	Computer laboratory (1 hr)
	MATLAB - Attenuation	Practical (3 hr)
Week 07	Target and Clutter Characteristics	Lecture (2 hr)
	Radar Range Equation	Computer laboratory (1 hr)
	MATLAB - Multipath	Practical (3 hr)
Week 08	Doppler Processing	Lecture (2 hr)
	Radar Range Equation	Computer laboratory (1 hr)
	MATLAB - RCS with Angle	Practical (3 hr)
Week 09	High Range Resolution Sensors	Lecture (2 hr)
Week 09	Matched Filter & Doppler	Computer laboratory (1 hr)
Week 10	High Angular Resolution Sensors	Lecture (2 hr)
	Matched Filter & Doppler	Computer laboratory (1 hr)
	MATLAB - Doppler	Practical (3 hr)
Week 11	Range and Angle Estimation	Lecture (2 hr)
	Phased Arrays	Computer laboratory (1 hr)
	MATLAB - FMCW - Linear Chirp	Practical (3 hr)
Week 12	Tracking Moving Targets	Lecture (2 hr)
	Phased Arrays	Computer laboratory (1 hr)
	MATLAB - Phased Array Sonar	Practical (3 hr)
Week 13	Radiometry	Lecture (2 hr)
Week 13	MATLAB - Antenna transfer Function and Tracking	Practical (3 hr)
Weekly	Students are expected to commit to at least 5 hours per week of independent study in addition to timetabled activities.	Independent study (65 hr)

Attendance and class requirements

Independent study: Depending on student competence and background, at least five hours of private study per week outside formal contact hours will be required to consolidate the work covered in class.
Laboratory: Student groups will assemble and measure the characteristics of various sensors. Some MATLAB code will be provided but students will be expected to develop additional code.
Tutorial: A number of MATLAB tutorials will be undertaken, during which groups of students are expected to develop code to model some sensing or signal processing application.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Graham Brooker, Sensors for Ranging and Imaging 2nd ed. IET, 2021. ISBN-13: 978-1-83953-199-6

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. assimilate information regarding the myriad of possibilities for the design of a sensor, and to convey this information to ones colleagues
LO2. develop skills for efficient project management in a team environment
LO3. integrate incomplete information and make value judgements to solve a sensing problem by using engineering "gut feel", rather than a rigorous analytical approach
LO4. apply specialised engineering skills (mechanical, electrical, and software) to analyse the performance of a sensor
LO5. understand active sensors, their outputs, and applicable signal processing techniques, and demonstrate an appreciation of the basic sensors that are available to engineers, and when they should be used
LO6. describe a number of sensors
LO7. make a distinction between sensor performance, based on simulation and measurement.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

The assignment has been removed as it is too AI sensitive Mark allocation changed with the exam going up to 45% The exam is the only form of assessment that provides a reasonably accurate reflection of the individual student competence in the course. It will remain a hurdle task

Additional information

Work, health and safety

Students will be required to perform an on-line lab induction.

Details available on Canvas

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

MECH5720: Sensors and Signals

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links