Embodiments include methods, systems and computer readable storage medium for a method for determining a fine direction of arrival (DOA) for a target is disclosed. The method includes receiving, by a plurality of receivers of a radar system, radar signals reflected by a target. The method further includes mitigating, by the radar system, phase shifts in the radar signals caused by a motion of the target. The method further includes determining, by the radar system, the fine DOA in response to the mitigation of phase shifts and based on the radar signals. The method further includes estimating and storing, by the radar system, a Doppler frequency based on the fine DOA.

The disclosed systems, structures, and methods are directed to an object detection system, employing a receiver configured to receive a signal reflected from an object, an analog-to-digital converter (ADC) configured to convert the received signal into a digital signal, a pre-processor configured to improve a signal-to-noise (SNR) of the digital signal and to generate a pre-processed signal corresponding to the digital signal, a parameter extractor configured to calculate a number of reference cells M and a multiplication factor K.sub.0, and a Constant False Alarm Rate (CFAR) processor configured to analyze a cell-under-test (CUT) and M reference cells in accordance with the number of reference cells M and the multiplication factor K.sub.0 to detect the presence of the object.

A system and method to classify objects using radar data obtained by an automotive radar. The system includes a convolutional network having a plurality of hidden layers comprising convolution layers for extracting features from the radar data, and an output. The system also includes a deconvolutional network having a plurality of hidden layers comprising deconvolution layers for classifying the features extracted from the radar data, and a classification output. The system also includes a filter having an input coupled to the classification output of the deconvolutional network. The system further includes a fully connected network having a plurality of fully connected layers for determining a clutter threshold value from the output of the convolutional network. The filter is operable to use the clutter threshold value to filter noise and/or clutter from the classification output of the deconvolutional network and pass a filtered classification output to an output of the system.

The echoes being picked up in the distance-speed domain, the method being wherein it includes a step of producing a mask, in the distance-speed plane, overlying the zone of detection of the ground and/or sea clutter echoes picked up by the sidelobes, the zone being determinable by the antenna parameters of the radar, the waveform emitted by the radar and the environmental context of the radar, all the points of the distance-speed plane which are covered by the mask being assigned a characteristic which is specific to the mask; a step of filtering the received echoes, in which the echoes covered by the mask are rejected from the radar reception processing.

Disclosed are techniques for liveliness detection. In an aspect, a radar sensor of an electronic device transmits a radar frame comprising a plurality of bursts, each burst comprising a plurality of radar pulses, and receives a plurality of reflected radar pulses. The electronic device generates a radar image representing azimuth, elevation, range, and slow time measurements for the radar frame based on the plurality of reflected pulses, applies a Doppler FFT to the radar image to convert the radar image to represent azimuth, elevation, range, and velocity measurements for the radar frame, identifies at least one area of motion in the radar image based on velocity bins of the radar image, and detects a target dynamic object based on a CFAR detection applied over the range and azimuth measurements and a SNR threshold of the received plurality of reflected pulses associated with the at least one area of motion.

Method of slowly moving target detection with application for coastal surveillance radars. This method improves the well know other methods and efficiently detects targets with a high accuracy. The proposed method consists of three steps that are: step of generation and processing of signals with complex modulation; step of target clustering and step of detection of slowly moving targets in clutter environments.

A method for detecting a continuous wave signal of a detector according to an embodiment of the present disclosure includes: moving a local signal frequency, processing external signals based on the local signals, checking the local signal frequency in which a frequency of the local signal that causes the processing signals having a frequency of a detection band, fixing the frequency of the local signal with the frequency checked, discriminating the external signals in which the detected external signals are discriminated as any one of a continuous wave signal and a frequency modulation signal and notifying target signal detection.

Target detection units respectively performing detection processing of targets which are different in spatial extent from each other on the basis of a detection result of amplitude or power by a detection unit are provided, and at least one determination processing unit is configured to determine presence or absence of targets from a result of the detection processing of targets by the target detection units. As a result of this configuration, it is possible to detect a target even when it has a spatial extent.

An accelerator device for use in generating a list of potential targets in a radar system, such as an anti-collision radar for a motor vehicle, may process radar data signals arranged in cells stored in a system memory. A cell under test in is identified as a potential target if the cell under test is a local peak over boundary cells and is higher than a certain threshold calculated by sorting range and velocity radar data signals arranged in windows. The cells identified as a potential target are sorted in a sorted list of potential targets. The accelerator device may include a double-buffering local memory for storing cell under test and boundary cell data; and a first and a second sorting unit for performing concurrent sorting of the radar data signals arranged in windows and the cells identified as a potential target in pipeline with accesses to the system memory.

Detection of a radar target from a received radar signal includes computing a vector of filter weights dependent upon a steering vector and determining a threshold value dependent upon a designated probability of false alarm. The vector of filter weights is applied to samples of the received radar signal at a test cell, corresponding to a test range, to provide a filtered test signal and a test power of the filtered test signal is computed. The weights are also applied to samples of the received radar signal at a number of reference cells, to produce filtered reference signals. A reference power is computed from the filtered reference signals and the radar target is detected at the test range when a ratio of the test power to the reference power exceeds the threshold value.