Infineon Technologies Ag

Prachi Vaishnav

Avik Santra

Saverio Trotta

Ashutosh Baheti

Reinhard-Wolfgang Jungmaier

Raghavendran Vagarappan Ulaganathan

Lorenz Ferdinand Wilhelm Weiland

In an embodiment, a method of tracking includes predicting a predicted state, in a global coordinate space, of an object based on a state of the object; determining in local coordinates the predicted state; determining a plurality of measurements of the object, in the local coordinates, with first and/or second radar sensors; determining a matching of the predicted state and the plurality of measurements, in the local coordinates, for a matching result; and updating the state of the object based on the matching result. The first and second sensors are arranged along perpendicular lines which intersect at the origin of the global coordinate space.

Position tracking with multiple sensors

In an embodiment, a method for tracking a target includes: receiving raw data from a millimeter-wave radar, the raw data including a plurality of macro-Doppler frames, each macro-Doppler frame having N chirps, where each macro-Doppler frame stretches over a time interval having a first duration; generating micro-Doppler frames from the plurality of macro-Doppler frames, each micro-Doppler frame including L chirps from M macro-Doppler frames, where each micro-Doppler frame stretches over a time interval having a second duration that is longer than the first duration; detecting one or more moving targets based on the macro-Doppler frames; detecting one or more static targets based on the micro-Doppler frames; and tracking a first target as the target transitions from being detected based on the macro-Doppler frames to being detected based on the micro-Doppler frames.

Radar-based target tracker

In an embodiment, a method includes: obtaining depth data indicative of a time-resolved measurement of a 3-D position of a moveable target in a field-of-view of a depth sensor; and processing the depth data using an interacting multi-model (IMM) tracking algorithm, the IMM tracking algorithm providing, for each one of multiple iterations, tracking data comprising a respective estimate of the 3-D position of the moveable target, wherein the IMM tracking algorithm comprises a first model providing a first output, a second model providing a second output, and a fusion module fusing the first output and the second output to provide the tracking data, wherein the first model models a movement state of the moveable target, and wherein the second model models a rest state of the moveable target.

Interacting multi-model tracking algorithm using rest state model

In an embodiment, a method for tracking targets includes: receiving data from a radar sensor of a radar; processing the received data to detect targets; identifying a first geometric feature of a first detected target at a first time step, the first detected target being associated to a first track; identifying a second geometric feature of a second detected target at a second time step; determining an error value based on the first and second geometric features; and associating the second detected target to the first track based on the error value.

Radar based tracker using empirical mode decomposition (EMD) and invariant feature transform (IFT)

In an embodiment, a method for tracking a target using a millimeter-wave radar includes: receiving radar signals using the millimeter-wave radar; generating a range-Doppler map based on the received radar signals; detecting a target based on the range-Doppler map; tracking the target using a track; generating a predicted activity label based on the track, where the predicted activity label is indicative of an actual activity of the target; generating a Doppler spectrogram based on the track; generating a temporary activity label based on the Doppler spectrogram; assigning an uncertainty value to the temporary activity label, where the uncertainty value is indicative of a confidence level that the temporary activity label is an actual activity of the target; and generating a final activity label based on the uncertainty value.

Integration of tracking with classifier in mmwave radar

In an embodiment, a method for tracking human targets includes: receiving radar signals using a radar sensor; generating a range-Doppler map based on the received radar signals; detecting a human target based on the range-Doppler map, where detecting the human target includes determining a range and a bounding box dimension of a bounding box bounding the detected human target, the bounding box at least partially surrounding the detected human target; and when the determined range is inside an expected region associated to a track, adding to the track a new detection point including the determined range and bounding box dimension, where the expected region is determined based on bounding box dimensions of detection points of the track.

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Munich, Germany

Prachi Vaishnav