Silicon Laboratories Inc.

Joel Kauppo

Sauli Johannes Lehtimaki

Antonio Torrini

Jere M Knaappila

Mika Tapio Länsirinne

Terry Lee Dickey

Attila Zólomy

Ádám Süle

Sebastian Ahmed

Jeffrey A Tindle

A system and method for determine the spatial position of a network device is disclosed. A locator device having an antenna array is used to perform an AoX calculation to find the direction to the network device. The AoX calculation utilizes a plurality of the antennas in the antenna array. The locator device also performs a high accuracy distance measurement (HADM). In certain embodiments, only one antenna in the antenna array is used for the HADM calculation. Using the direction and the distance to the network device, the locator is able to determine the spatial position of the network device. In some embodiments, the antenna array may a rotational symmetry array having a plurality of outer antenna elements and one central antenna element.

Hybrid AoX and HADM system for low cost and simple location finding

A system and method for determining a position or a movable device is disclosed. The present system utilizes a movable device equipped with a locator device that has an antenna array such that it may determine the angle of arrival of a plurality of incoming beacon signals. In certain embodiments, the movable device is also able to measure its distance travelled. By knowing its distance moved and the angle of arrival from each beacon, the locator device is able to calculate its position as well as the position of each beacon. This procedure may be executed at regular intervals so that the movable device accurately determines its position.

Positioning and self-calibration mechanism using moving AoA locator

An antenna array that utilizes ground guard rings and metamaterial structures is disclosed. In certain embodiments, the antenna array is constructed from a plurality of antenna unit cells, wherein each antenna unit cell is identical. The antenna unit cell comprises a top surface, that contains a patch antenna and a ground guard ring. A reactive impedance surface (RIS) layer is disposed beneath the top surface and contains the metamaterial structures. The metamaterial structures are configured to present an inductance to the patch antennas, thereby allowing the patch antennas to be smaller than would otherwise be possible. In some embodiments, the metamaterial structures comprise hollow square frames. An antenna array constructed using this antenna unit cell has less coupling than conventional antenna arrays, which results in better performance. Furthermore, this new antenna array also requires less space than conventional antenna arrays.

Rotational symmetric AoX antenna array with metamaterial antennas

A wireless locator that facilitates determining a location of a nearby wireless asset including an antenna array with multiple antennas, at least one wireless transceiver that receives a location signal from the nearby wireless asset and that takes multiple samples from the location signal including a set of samples for each antenna, and a processor that compresses the samples to generate location information associated with the nearby wireless asset. The wireless locator may be part of a wireless location system including multiple wireless locators distributed in the area and a central processing system. Various compression methods are disclosed, including averaging of the samples, bit reduction of the samples, converting the samples to corresponding phase values, and combining corresponding samples of multiple sample supplemental sets. Combinations of the various compression methods are also disclosed.

Compression of location information during backhaul communications of a wireless location system

A system and method of determining the angle of arrival or departure using a neural network is disclosed. The system collects a plurality of I and Q samples as a packet containing a constant tone extension is being received. The I and Q samples are used to form I and Q arrays, which are used as the input to the neural network. The neural network produces a first output representative of the azimuth angle and a second output representative of the elevation angle. In certain embodiments, the neural network is capable of detecting a plurality of angles, where, for each angle, there are three outputs, a first output representative of the azimuth angle, a second output representative of the elevation angle and a third output representative of the relative amplitude. In some embodiments, the neural network is configured to determine the carrier frequency offset of an incoming signal as well.

Angle of arrival using machine learning

A system and method for detecting and compensating for carrier frequency offset is disclosed. This system compensates for CFO and calculates a corrected phase. This corrected phase may be used by, for example, an AoX algorithm, such as MUSIC, to more accurately determine the angle of arrival or angle of departure of a signal. In certain embodiments, the system oversamples the incoming signal to create a plurality of samples. The system then determines the phase of each of the plurality of samples and calculates the carrier frequency based on the time derivative of the phase. In certain embodiments, a particular portion of an incoming packet is used to determine the carrier frequency offset. In other embodiments, the system calculates the carrier frequency offset throughout an entirety of the incoming packet. Once the carrier frequency offset is determined, it can be used to adjust the received signals. These adjusted signals are then used to determine the angle of arrival or angle of departure.

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Espoo, Finland

Joel Kauppo