If you have never heard the term particle filter, you are not alone.
And, if you are not from a scientific or research background, you would not know it is one of the most significant algorithms of the past 50 years which enables estimation in the midst of uncertainty.
Or, as Dr Neil Gordon, a senior scientist with the Department of Defence in South Australia, puts it, “it’s all to do with estimation in dynamic systems”.
“You’ve got to capture the uncertainty in the right way,” Dr Gordon said.
“It’s impossible to do that with an analytic function which is why we’ve ended up with effectively a computer-driven approach, particle filters.
“They are a way of driving random samples and herding them around in the right way to end up with the probability distribution in the right place.”
He uses the mystery of flight MH370, the Malaysia Airlines flight that went missing en route from Kuala Lumpur to Beijing in 2014, as an example.
“In the case of the MH370, we had an aircraft, presumed lost at sea,” Dr Gordon said.
“We had the last self-reported position early in the flight, we had data from radars, we had information about the wind and the weather and we had approximately once-an-hour metadata associated with Inmarsat satellite communication system handshakes with the aircraft,” Dr Gordon said.
“Using the particle filter we were able to factor together all those uncertainties, and determine where best to focus the search effort.”
The work required great technical innovation because neither traditional sensor data nor self-reported location data from the aircraft was available for the majority of the flight.
The resulting search area was vast (larger than the size of England) and extremely challenging for a search operation, given its remoteness, the depths of the ocean and the ocean weather.
“In those situations, even if you’re searching in the most likely area, there is still no guarantee of locating the wreckage,” Dr Gordon explains.
MH370 has never been found.
Dr Gordon said in the current climate, it’s likely that particle filters are being used in the COVID-19 fight.
“A particle filter, or a variation of it, can be used to model the possible infection scenarios, factoring in the different rates of transmission, levels of social distancing, whether people are wearing masks,” Dr Gordon said.
“These models enable health experts to provide advice to governments on how best to prepare for and manage those possible outcomes.”
In Defence, the main application of the particle filter is in surveillance, processing data from radars and other sensors in order to extract meaningful intelligence.
“When it comes to surveillance, it’s critical we extract as much information from our sensors as possible to maximise situational awareness,” Dr Gordon said.
“Using the particle filter, we take the received energy from a radar system, be it JORN or Wedgetail, and estimate how many targets there are, where they are, where they are going, what’s their intent.
“Methods such as the particle filter allow us to do this in a more accurate way than using standard Kalman filter type approaches.”
As Dr Gordon points out, having accurate surveillance not only enables us to put in place appropriate defensive measures but it also acts as a very powerful deterrent.
“If the chances of detection are very high, that is more likely to deter any potential adversary,” he says.
The particle filter is an incredibly cost-effective way of improving detection capability. A few lines of maths can achieve improvements in detection sensitivity that would otherwise cost many millions of dollars in physical upgrades.
Defence is a proud participant in National Science Week, celebrating and acknowledging the contribution our STEM workforce makes to the capabilities of the Australian Defence Force and the security of the country.
To hear more from Dr Gordon, click on the video link below.