Using Millimeter Waves to Map Your Surroundings for Better Location

This patent describes a way for a mobile device to use millimeter wave signals to understand its environment and pinpoint its location by analyzing how those signals bounce off surfaces.

What it covers

This invention uses millimeter wave (mmWave) radio frequency signals to create a map and determine a device's location. A transmitting device sends out these signals, which then bounce off surfaces like walls or furniture, creating 'multipath components.' The device receives these bounced signals and analyzes information from them, such as the angle they arrived (AoA) or when they arrived (time of arrival). Using this data, along with a stored map, a ray tracing process calculates the device's precise location. It can then generate images or video of the surroundings, potentially showing other objects in the environment. For example, a phone could use this to show a user exactly where they are in a large building or to highlight obstacles.

What it doesn't cover

• —Methods that do not use millimeter wave (mmWave) radio frequency signals.
• —Systems that do not analyze multipath components of the RF signal.
• —Methods that do not determine at least two pieces of information like AoA, AoD, time of arrival, RTA, or phase.
• —Techniques that do not use a stored 2D or 3D map of the environment.
• —Processes that do not employ a ray tracing calculation to determine location.

The clever bit

The innovation lies in combining mmWave signal analysis with existing map data and ray tracing. This allows for precise localization by understanding how the signals interact with the physical environment, rather than relying solely on satellite signals which fail indoors.

Why it matters

Accurate indoor positioning is a long-standing challenge. This technology offers a potential solution by leveraging readily available millimeter wave frequencies, which are key to modern high-speed wireless communication like 5G. It could enable new location-aware applications and improve existing ones that struggle with GPS indoors.

Real-world examples

• 1.Advanced indoor navigation systems
• 2.Augmented reality applications requiring precise spatial awareness
• 3.Robotics and autonomous systems needing environmental mapping
• 4.Smartphones with enhanced location services