Shooter detection part 3: Acoustic gunshot detection explained

Shooter detection - acoustic gunshot detection

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Rich Onofrio, Chief Technology Officer at Shooter Detection Systems provides an explanation of how gunshot detection is able to cut through the noise.

Gunshot detection

While it’s true that there are many different approaches to indoor gunshot detection, one thing is consistent with all of them: the use of acoustics, or sound to detect gunshots.

At first glance, this seems logical. Anyone who has been in the presence of a firearm when it is discharged knows that it makes a very distinct – and loud – noise.

Additionally, given the relatively low cost of microphones and processors, creating a sensor that “listens” for a specific sound is a solid first step. Unfortunately though, sound can sometimes be deceptive.

If you want to build a sensor that people can rely on, especially one used to keep people safe or send critical location data to first responders, it is therefore imperative that the sensor’s detection and false alert rate be as low as possible.

What’s in a gunshot?

When a bullet leaves the muzzle of a gun, one of the first things that happens is that the projectile starts to rapidly push out onto the air molecules around it.

This movement is what our ears – or our skin if we are close enough – detects. Just like our ears, gunshot detection sensors use a specific type of microphone to detect this movement.

Yet while this movement can be described by humans using different words: shockwave, energy, pressure, acoustic wave, etc., it all refers to the same physical property.

This is where those professionals exploring gunshot detection need to pay close attention.

Some manufacturers will explain that they have a special, patented sensor that measures a unique property and use words like concussive force, energy or percussion.

Understand that all this means is that their sensor has a microphone tuned to capture the rapid movement of air molecules. Nothing more. How a microphone is calibrated does impact how signals are collected.

Detecting shockwaves is a good example of this. Like a loud noise where air molecules expand and compress rapidly, a shockwave is an extreme compression of molecules to the point where the wave of moving air molecules exceeds the speed of sound.

A secondary characteristic of a shockwave is that it will dissipate very rapidly over distance.

Measuring for shockwave still requires a microphone or “pressure sensor” but given the magnitude and focusing of energy required to move air at this speed, shockwave detection is not a preferred method for gunshot detection.

The first reason is that a shockwave is directional. Any detection device would need to be placed in line with or in front of the shockwave.

A gunshot that occurs facing away from a sensor calibrated only for shockwave may not be recorded.

Second, several firearm calibers do not create a shockwave when discharged. A high-powered rifle will, but handguns like a .45 or some types of 9mm may not or at a minimum will only travel at supersonic speeds for a short distance.

Additionally, a shockwave, also known an N wave, in a confined indoor space can become easily distorted making it difficult or impossible to detect due to reflections from walls, ceiling and floor.

The bottom line? The science behind acoustic gunshot detection is far more complicated than most think.

A gunshot or balloon pop?

In the early days of indoor gunshot detection, simply detecting the movement of air molecules displaced during a gunshot and comparing it electronically to a known digital profile or library of profiles was thought to be good enough.

However, testing against objects that produce air displacement like a bullet quickly revealed the flaw in this thinking.

There are many objects, from balloons to doors slamming to nail guns, that create the same properties as those emitted by a gunshot.

Given the complexities of operating indoors, where these waves of air molecules can be deflected by objects or diminish as they bounce off corners, the signal received by a sensor may be significantly altered.

Oddly enough, under these conditions a mechanical sensor must attempt to solve the same problem that a human would: was that an actual gunshot or something just like a gunshot?

Simply adding more microphones that are calibrated differently does not solve this problem.

While advanced algorithms and machine learning can help, it becomes clear that more information needs to be added if we want a sensor to accurately detect an actual gunshot.

The high cost of false alarms

Alarm fatigue is a problem in security, but pales in comparison to law enforcement showing up at your building due to a false alert or a malicious “Swatting” incident.

When your accounting department can easily calculate the hourly cost to your organization for shutting down an assembly line or evacuating a hospital wing, it becomes clear why it is important for gunshot detection to have an additional verification mode that works in real time alongside acoustic detection.

Two signals are better than one

When a bullet leaves the muzzle of a gun, another physical event occurs at the speed of light: the muzzle “flash” or light/heat signal from the expulsion of hot gases moving the bullet.

Infrared (IR) light waves are released and can be captured by high quality, specially adapted IR sensors.

Capturing and processing this IR signal and combining the data with microphones that capture the compression of air molecules significantly increases the probability that what is detected is a gunshot.

Since IR light travels faster than the speed of sound, there is no time lost in this extra confirmation step.

The speed at which a gunshot sensor can process this data is instantaneous when compared to manual processes like a human receiving, reviewing and verifying a video image of a possible firearm.

Some manufacturers may claim the use of IR, but if they claim to “see” through walls, you can bet they don’t need that IR to detect a gunshot.

Just like air molecules, IR waves can bounce off walls or be deflected by objects.

In this instance, this is a positive trait as it means that IR does not limit a sensors detection capability to only within a field of view.

However, any manufacturer that claims that their sensor uses two factor verification yet can detect signals where IR waves can be blocked – such as through a wall – is stating that IR is not part of their sensor’s gunshot verification process.

With lives potentially on the line, indoor gunshot detection demands a higher standard.

False alerts waste resources and inhibit first responders’ ability to appropriately respond.

A gunshot detection system’s technology approach should prioritize both the minimization of false alerts and speed in alarm data delivery.

When evaluating systems, be wary of misleading claims and focus on precision, speed and reliability. The technology exists to detect gunshots accurately indoors, so don’t settle for less.

About the author

Rich is the Chief Technology Officer of Shooter Detection Systems, having performed roles from Vice President of Engineering and Managing Director for the company as an original employee.

Rich came to SDS from Raytheon BBN Technologies where he worked as an Electrical Engineer on a wide range of gunshot detection systems including Boomerang, as well as soldier-worn, helicopter based and other infrared/acoustic based gunshot detection systems.

He was also lead hardware designer of SDS’ original indoor gunshot detection system, the patented SDS Handheld Tester and has co-authored all of SDS’ many patents to date.

Make sure to keep an eye out for the next and final installment of the Shooter detection miniseries, coming 25 September. Find our previous installment here.

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