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mmWave presence radar detects people by sending radio waves into a space and analyzing how reflected waves change over time. Breathing, fidgeting, typing, or a small head movement can alter the return enough for the sensor’s signal-processing algorithm to classify someone as present—even when they are not walking around. That classification is an inference from radar measurements, not a camera image or a guarantee of a person’s identity.
What the radar measures
A radar transmits radio-frequency energy and listens for reflections from objects in its field of view. Those echoes change with the scene: a target’s position and movement affect the returning signal. Texas Instruments’ technical brief describes radar returns as carrying information about range (distance), velocity (speed and directionality), and angle (location). These measurements give the processing system clues about what is in the space and how it is moving. Texas Instruments’ mmWave sensing brief explains these signal characteristics.
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Unlike a camera, mmWave sensing does not rely on visible light or form a visual picture of a person. It detects changes in reflected radio waves. A person walking usually causes a relatively large change; breathing or fidgeting produces smaller, repeated changes that can still be useful to an algorithm.
Why tiny movements can register
The key is change over time. Even when someone is sitting still, breathing moves the chest slightly. Typing, shifting posture, or moving the head also changes the reflected signal. TI lists breathing, fidgeting, small head movements, and typing as examples of micro-motions detectable by its IWRL6432AOP. The sensor is not recognizing these actions as a human would; its software evaluates radar measurements against rules designed to distinguish motion patterns from a static scene.
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In practical terms, walking is a strong signal and a breath is a weaker one. The system must be configured and processed to treat a small, persistent change as evidence of presence rather than noise. Different sensors and products can use different filters, thresholds, and decision logic, so the ability to detect a particular movement is implementation-dependent.
How FMCW radar helps measure motion
One common radar method is frequency-modulated continuous wave (FMCW). In an FMCW system, the transmitted signal changes frequency in a controlled pattern. Comparing the transmitted signal with its echo lets the radar derive information such as distance; examining how the return changes across measurements provides evidence about motion.
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- [Precise AI-Powered Presence Sensor] Powered by advanced AI, PIR, and 60GHz mmWave radar, the FP300 accurately detects human presence even when you’re still. Its adaptive AI learning continuously optimizes sensitivity to reduce false triggers, ensuring lights and devices stay on even when you’re lounging on the couch, and off when they need to be.
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Infineon’s documented example uses FMCW with its 60 GHz XENSIV BGT60TR13C sensor. Its application note for the KIT_CSK_BGT60TR13C describes configurable maximum range and micro- and macro-motion thresholds. FMCW is one approach, not a universal recipe: the sensor, signal processing, and state logic vary across products. Infineon’s AN124642 application note documents this particular implementation.
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The radar’s measurements do not automatically equal a presence decision. Embedded software analyzes the signal and assigns a state according to the product’s detection rules. In Infineon’s example, the algorithm distinguishes among micro-presence, macro-presence, and absence. Large movement can support a macro-presence classification; after that movement stops, smaller motion such as breathing may still support micro-presence. If the observed signal no longer meets the configured criteria, the system can move to absence.
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This illustrates why “presence” is an algorithmic classification, not a standardized promise that every radar will identify every occupant. The thresholds and processing chain determine what evidence counts. TI’s xWRL6432 motion-and-presence demo, for example, describes an onboard algorithm that includes micro-Doppler classification and discrimination. TI’s demo documentation shows the role of software alongside the radar hardware.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What affects detection distance and reliability
There is no single detection distance that applies to all mmWave presence sensors. Range depends on the sensor and algorithm, the configured maximum range, the target’s position and movement, and the installation. A small breathing motion is harder to detect at a distance than a larger movement, and thresholds can affect whether the system classifies a weak return as presence.
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For its BGT60TR13C solution, Infineon’s application brief reports micro-motion such as breathing or typing up to 4 m. The same brief distinguishes 5 m as a tested and qualified range for macro-motion, while describing performance up to 10 m as not tested and not qualified. These figures apply to that vendor’s solution and should not be generalized to other radar products. Infineon’s presence-detection application brief provides its qualifications; its presence-sensing overview describes the company’s solution.
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Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →- Movement size: Larger motion generally creates a more obvious change in the return than subtle breathing or small fidgets.
- Placement and field of view: The target’s position relative to the radar affects the signal the sensor receives.
- Configuration: Maximum range and motion thresholds can be adjustable, as in Infineon’s documented example.
- Processing: Filters, classification methods, and state logic affect how a product interprets small changes.
These sources do not establish a universal ability to detect people through every wall, enclosure, or material. Whether a particular installation works depends on its hardware, settings, and physical setup; claims about penetration or range need to be checked for the specific product.
How mmWave compares with other presence sensing
The useful comparison is not that radar is always better, but what the application requires. Infineon contrasts its radar solution with passive infrared (PIR) sensing on micro-motion detection and enclosure integration, while TI describes mmWave’s ability to provide range, velocity, and angle information. Those are vendor-specific descriptions, not a controlled comparison across the market.
- Very small motion: Check whether the specific product documents detection of movements such as breathing, rather than relying on a generic “presence” label.
- Location or distance: Determine whether the system exposes range or angle data, or only a presence state.
- Verified range: Look for the motion type, test conditions, and whether the quoted distance is tested and qualified.
- Installation: Confirm field of view and enclosure requirements for the intended placement.
- Integration effort: Establish whether the hardware includes a complete embedded presence algorithm or requires application-level development.
A concrete 60 GHz example for builders
Infineon’s KIT_CSK_BGT60TR13C is a development platform built around the 60 GHz XENSIV BGT60TR13C. The accompanying application note describes an FMCW presence-detection implementation with configurable behavior and micro-presence, macro-presence, and absence states. It is a way to evaluate or prototype the sensing method; a development kit should not be assumed to be a turnkey consumer occupancy sensor.
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