Embodiments are directed to a method for determining velocity of an object. The method includes in response to two interleaved chirp sequences being sent towards the object, processing responsive chirps of each of the two interleaved chirp sequences independently from one another to produce respective Doppler-spectrum data sets, and calculating the velocity of the object based on the respective Doppler-spectrum data sets. Each of the interleaved chirp sequences being characterized by a common time spacing between respective chirps of the respective chirp sequence, and each chirp of one of the chirp sequences being offset by an amount of time that is different than the common time spacing.

The present invention relates to a security surveillance microwave sensor having a reduced false report rate by means of biological signal detection, which monitors and determines a malfunction state or a false alarm generated by environmental factors by detecting humans, animals or objects approaching within a predetermined distance using a microwave signal. The present invention may extend the monitoring distance of security surveillance, set an IF frequency band disturbed by a human body, amplify the IF frequency or use a change in the voltage level to extend the monitoring distance, manage a monitoring state by double-checking transmission and reception of security signals, and reduce the false report rate by distinguishing the false alarms or the malfunction state of the sensor.

Doppler signal processing device for detecting an object according to a received wireless signal. The Doppler signal processing device includes a frequency analysis unit for generating a frequency domain signal vector according to at least one digital signal, an interference suppression unit for performing a suppression operation according to the frequency domain signal vector and a frequency domain interference estimation signal vector to generate an interference suppressed frequency domain signal vector, an interference estimation unit for generating the frequency domain interference estimation signal vector according to the frequency domain signal vector, a detection unit for generating a result signal according to the interference suppressed frequency domain signal vector, an error detection unit for optionally providing an error detection control signal to the interference estimation unit to adjust a rate of updating the frequency domain interference estimation signal vector.

A system and method for detecting a rotary wing aircraft. A return electromagnetic signal, reflected by a rotary wing aircraft, is received through an electromagnetic signal detection apparatus. The aircraft includes a plurality of propeller blades attached to at least one motor. At least one propeller blade has at least one portion with a reflectivity different from other portions. A first time series data of the return electromagnetic signal is received. A second time series data is determined based on the first time series data and a predefined threshold. A characteristic of the second time series data is used to determine whether it corresponds to the known aircraft.

A system for interpreting gestures may include one or more processors, at least three Doppler radar devices, and a memory device. The memory device may have a receiving module, a cube generating module, and a classifying module. The receiving module may include instructions that cause the one or more processors to receive Doppler information from the at least three Doppler radar devices. The cube generating module may include instructions that cause the one or more processors to generate a micro-Doppler cube by projecting Doppler information in X, Y, and Z-directions over a period of time into the micro-Doppler cube. The classifying module may include instructions that cause the one or more processors to classify one or more gestures performed by an extremity when located in the volume into a category of a plurality of categories based on the micro-Doppler cube.

A method of increasing a resolution of radar data is provided. The method of training a radar resolution increase model comprises generating a high-resolution training ground truth and a low-resolution training input from original raw radar data based on information corresponding to at least one of dimensions defining the original raw radar data, and training the resolution increase model based on the high-resolution training ground truth and the low-resolution training input. A radar data processing device generates high-resolution output data from low-resolution input data based on a trained resolution increase model.

Methods and systems include, in at least one aspect: obtaining from a camera 2D image data of an object, obtaining from a radar device radar data of the object, combining the radar data and the 2D image data to produce 3D location information of the object, and modeling a 2D trace of the object using the 2D image data by finding an initial version of the 2D trace, receiving an initial portion of the 3D location information, extending the initial portion of the 3D location information in accordance with physical-world conditions to find at least one 3D location beyond the initial portion of the 3D location information, projecting the at least one 3D location into a 2D image plane of the camera to locate 2D region, and processing the 2D region in the 2D image data to extend the 2D trace of the object in flight.

The present invention provides a radar target detection method based on estimation before detection (EBD), which comprises: obtaining pre-detect targets (PDTs) based on conventional pulse-Doppler processing and pre-detection; estimating ranges and speeds of PDTs, i.e., performing parameter EBD; establishing a dimension-reduction observation model of a received signal based on PDTs and parameter thereof; reconstructing a target vector in the dimension-reduction observation model based on a sparse recovery algorithm; and designing a generalized likelihood ratio detector based on the reconstruction result for target detection. The method of the present invention can significantly reduce the radar signal processing loss, and the target detector used in the method has the constant false alarm rate (CFAR) property, so that the weak target detection performance can be greatly improved.

Provided is a radar apparatus that detects a target object with high accuracy. The radar apparatus includes: transmission circuitry, which, in operation, alternately outputs a first transmission signal with a first central frequency and a second transmission signal with a second central frequency higher than the first central frequency for each transmission period; and one or a plurality of transmission antennas, which, in operation, transmit the fast transmission signal and the second transmission signal. The second central frequency is higher than a frequency (1+1/Nc) times the first central frequency, where Nc is an integer indicating a number of times of transmission of each of the first transmission signal and the second transmission signal for the each transmission period within a predetermined duration.

System and method of deploying a trained machine learning neural network (MLNN) in generating a fall injury condition of a subject. The method comprises receiving, at input layers of the trained MLNN, millimeter wave (mmWave) radar point cloud data representing fall attributes from monitoring the subject via mmWave radar sensing device, the input layers associated with the fall attributes, receiving, at a second set of input layers, personal attributes of the subject associated with ones of the second set of input layers, the first and second sets of input layers interconnected with an output layer of the trained MLNN via intermediate layers, the trained MLNN produced by establishing a correlation between an injury condition of prior subjects and mmWave point cloud data and personal attributes associated with the prior subjects, and generating, at the output layer, the fall injury condition attributable to the subject.