1. Prototype

Smoke test (SW V0.21).

The prototype is built around a Raspberry Pi Pico W, I am using a Raspberry Pi 3 for program development, obviously (I am developing solely with C language), and for checking raw data on the serial port (debugging). All components are connected thru the I2C bus.

Two different branches of development:

• SW V0.2x -> non IoT / OLED display / acoustic burglar detection
• SW V0.4x -> IoT

2. I2C bus components

* MLX90642 IR array (the actual sensor)

* OLED display w/ SSD1306 controller (merely for experimenting / presentation)

* TSL25911 ambient light sensor (reference component, or for night activation of the IR array if applicable)

The Raspberry Pi Pico W is the I2C bus master. The Raspberry Pi is not part of this network.

3. InfraRed (IR) array

The MLX90642 IR array consists of 768 IR sensors, each individual IR sensor delivering 2 Byte of thermal data to be acquired via I2C commands i. e. registers. Simple math formulas have to be applied on the data to get object temperatures. 

Temperatures in the range -40°C to 85°C can be measured (see data sheet), values can be acquired continuously or step-wise. I2C clock speed has to be high enough to be able to deal with the array refresh rate to avoid glitches. 

Compared with commercial and ready-to-use systems, a resolution of 32x24 is not very high of course.

4. IoT

Several options for realizing an IoT device, but as mentioned I am using a Raspberry Pi Pico W and the home WiFi. 

Display and rendering of thermal data on a phone or a computer makes more sense than on any attached display. 

Example implementation where 60 sensors are read and displayed (15 pixels in 4 rows from left to right):

Implementation when all 768 pixels are read and data is visualized (moving object in 5m distance in an outdoors area), see also project log:

Note: for a simple burglar detection however, all of this may not be necessary and setting off an alarm may be sufficient.

5. Challenges

There are of course many challenges along the way until I have a somewhat useable system that does not boast with too many false positives. To mention a few:

* adapting to different environmental conditions (outdoors with bright sunlight is a particular challenge)

* recognizing (intelligently) the movement of objects

* general signal conditioning

* interpolation, extrapolation and presentation of values

* implementation of the SSL server

* combining more sensors to get a higher resolution image

In order to tackle some of the challenges, different approaches will be employed (such as ML).