SWRA774 may   2023 IWRL6432

 

  1.   1
  2.   Trademarks
  3. 1Introduction
  4. 2Machine Learning in mmWave Sensing
  5. 3Development Process Flow
  6. 4Case-Study-1: Motion Classification
  7. 5Case-Study-2: Gesture Recognition
  8. 6References

Introduction

The past few years have seen a significant increase in sensing applications in the 60 GHz band (an unlicensed band open for a wide variety of applications). This is primarily due to advances in mmWave technology that have reduced the size, cost and power consumption of these sensing devices. Typical applications include:

  1. Building automation (indoor and outdoor surveillance, elderly care)
  2. Personal electronics (gesture recognition, presence detection)
  3. Automotive body and chassis (in-cabin sensing, kick-2-open sensor).

The smaller wavelength of approximately 5 mm for 60 GHz signals directly translates to higher velocity resolution and small form factor. Many sensing applications that earlier operated at lower frequency bands (3-10 GHz) are now moving to 60 GHz to leverage these advantages.

While mmWave radar has been traditionally employed in target detection and tracking, there has been a trend toward using radar signals for target classification [3]. Many of these classification algorithms use Machine Learning (ML) to leverage the radar’s high sensitivity to motion. The examples include: motion classification for reducing false alarms in building automation, fall detection for elderly care, and gesture recognition.

TI’s integrated radar-on-chip devices, such as IWRL6432 [1], are designed for the above applications. IWRL6432 has an integrated RF front end and a Hardware Accelerator (HWA@80 MHz) optimized for radar signal processing. The on-chip MCU, Arm® Cortex®-M4F (@160 MHz), provides sufficient computational capability for post-processing algorithms such as tracking and classification. The device is designed with various low-power modes to support applications where minimizing power consumption is critical.