An intelligent control method and system using a radar security camera are disclosed, wherein a target is detected by 360° radar sensing regardless of the rotation radius of a camera by using the security camera having a built-in radar, and the camera is enabled to track the target according to the moving direction and specific signs of the target after the target is identified as a person and a vehicle sequentially according to a decision priority order.

In various examples, methods and systems are provided for sampling and transmitting the most useful information from a radar signal representing a scene while staying within the computational and storage confines of a standard automotive radar sensor and the bandwidth constraints of a standard communication link between a radar sensor and processing unit. Disclosed approaches may select a patch of frequency bins that correspond to radar signals based at least on proximities of the frequency bins to one or more frequency bins corresponding to at least one peak and/or detection point in the radar signals. Data representing samples corresponding to the patch of frequency bins may be transmitted to the processing unit and applied to one or more machine learning models in order to accurately classify, identify, and/or track objects.

An operation method performed by an apparatus for detecting multiple targets may comprise transmitting first signals using M.sub.t transmit antennas included in the apparatus; receiving the first signals reflected by the multiple targets through M.sub.r receive antennas included in the apparatus; generating a first function for estimating a velocity and an azimuth of each of the multiple targets using the first signals and the reflected first signals; estimating a velocity and an azimuth that maximize a result of the first function as a velocity and an azimuth of a first target closest to the apparatus among the multiple targets; generating a second function by cancelling interference caused by the first target from the first function; and estimating a velocity and an azimuth that maximize a result of the second function as a velocity and an azimuth of a second target among the multiple targets.

The present disclosure provides an error detector for determining an error vector between a radio trajectory and an image trajectory. The error detector includes: an input for monitoring a radio trajectory of an object from a radio signal and an image trajectory of an object from an image over an observation area; a correlation module arranged to correlate the radio trajectory with the image trajectory; an error module arranged to determine an error vector between the radio trajectory and the image trajectory; and an output arranged to transmit the error vector for use in determining an estimated trajectory of a target based on a target trajectory from a radio signal.

A multiple input multiple output (MIMO) radar system for detecting a moving object is based on an explicit signal model. The explicit signal model accounts for waveform separation residuals by relating measurements of the virtual array to an auto-term including a Kronecker product of object-receiver signatures and transmitter-object signatures; and a cross-term including a Kronecker product of object-receiver signatures and transmitter-object residual signatures. The radar system uses a spatial MIMO object detector that is based on the explicit signal model to detect the moving object.

In an embodiment, a method includes: receiving reflected radar signals with a millimeter-wave radar; performing a range discrete Fourier Transform (DFT) based on the reflected radar signals to generate in-phase (I) and quadrature (Q) signals for each range bin of a plurality of range bins; for each range bin of the plurality of range bins, determining a respective strength value based on changes of respective I and Q signals over time; performing a peak search across the plurality of range bins based on the respective strength values of each of the plurality of range bins to identify a peak range bin; and associating a target to the identified peak range bin.

A system and method for classifying a type of interaction between a human user and a mobile communication device within a defined volume, based on multiple sensors. The method may include: determining a position of the mobile communication device relative to a frame of reference of the defined volume, based on: angle of arrival, time of flight, or received intensity of radio frequency (RF) signals transmitted by the mobile communication device and received by a phone location unit located within the defined volume configured to wirelessly communicate with the mobile communication device; obtaining at least one sensor measurement related to the mobile communication device from various non-RF sensors; repeating the obtaining, to yield a time series of sensor readings; and using a computer processor to classify the type of interaction into one of many predefined types of interactions, based on the position and the time series of sensor readings.

Trapped or confined individuals may be located and rescued by detecting their movement using reflected, radio frequency signals over a range of multiple antenna polarities.

A method for real-time THz sensing using true time delay (TTD) is implemented by a base station and includes transmitting, by a transceiver that includes TDD elements and phase shifters configured in the transceiver, simultaneous frequency dependent (SFD) beams to scan an environment at a first granularity to detect a spatial cluster target. Each of the SFD beams corresponds to a different phase angle and different frequency. The method includes determining, among the SFD beams, a subset of beams that detected the spatial cluster target. The method includes beam switching, by the transceiver, using time division multiplexing (TDM) and a TDM bandwidth to scan a portion of the environment at phase angles corresponding to the subset of beams and at a second granularity finer than the first granularity. The method includes combining data received from the SFD beams, by multiple threads that concurrently process data received from the SFD beams.

An estimated initial direction of movement of a newly-detected object is to be determined. The actual previous directions of movement and positions of previously-detected objects are determined and stored. When a newly-detected object is newly detected at a certain position, then the actual previous direction of movement of one of the previously-detected objects at that position is used as a basis to determine the estimated initial direction of movement of the newly-detected object at that position.