A modular, radio frequency (RF) system includes one or more directional antennas and is configured with both hardware and software components to enable the RF system to monitor (e.g., detect or track signals or objects) and/or interact with (e.g., track signals or objects, or transmit signals) objects in particular directions. The RF system includes one or more machine learning models to determine, based on received signals, one or more signals to transmit.

A modular, radio frequency (RF) system includes one or more directional antennas and is configured with both hardware and software components to enable the RF system to monitor (e.g., detect or track signals or objects) and/or interact with (e.g., track signals or objects, or transmit signals) objects in particular directions. The RF system includes one or more machine learning models to determine, based on received signals, one or more signals to transmit.

A secondary echo and a primary echo subjected to topographic echo processing are compared with each other. When there is a topographic echo in the primary echo or the secondary echo determined as a strong echo, an echo resulting from removal of the topographic echo is defined as a strong-topographic-echo-removed reception signal. Electric power of the topographic echo in the secondary echo or the primary echo determined as a weak echo and the strong-topographic-echo-removed reception signal are defined as weak echo parameters. Electric power of the weak echo estimated from a reception signal in a weak echo region resulting from phase correction of a reception signal resulting from removal of a frequency component of the strong echo from the strong-topographic-echo-removed reception signal representing the weak echo parameter, a spectral width of the weak echo representing the weak echo parameter, and a Doppler velocity of the weak echo are provided as spectral parameters.

A secondary echo and a primary echo subjected to topographic echo processing are compared with each other. When there is a topographic echo in the primary echo or the secondary echo determined as a strong echo, an echo resulting from removal of the topographic echo is defined as a strong-topographic-echo-removed reception signal. Electric power of the topographic echo in the secondary echo or the primary echo determined as a weak echo and the strong-topographic-echo-removed reception signal are defined as weak echo parameters. Electric power of the weak echo estimated from a reception signal in a weak echo region resulting from phase correction of a reception signal resulting from removal of a frequency component of the strong echo from the strong-topographic-echo-removed reception signal representing the weak echo parameter, a spectral width of the weak echo representing the weak echo parameter, and a Doppler velocity of the weak echo are provided as spectral parameters.

A system includes a shift register to store data samples, where the shift register includes a cell under test (CUT), a left guard cell, a right guard cell, a left window, and a right window. The system includes two sets of comparators to compare incoming data samples with data samples in the left window and the right window to compute ranks of the incoming data samples. The system includes a sorted index array to store a rank of the data samples in the shift register. The system includes a selector to select a Kth smallest index from the sorted index array and its corresponding data sample from the shift register. The system includes a target comparator, where the first comparator input receives a data sample from the CUT and the second comparator input receives a Kth smallest data sample, and the comparator output indicates a CFAR target detection.

A method for determining direction information for at least one target object in a radar system for a vehicle. The first detection information is provided by at least two receive antennas of the radar system, wherein the first detection information is specific for a first radar signal transmitted by a first transmit antenna of the radar system. The second detection information is provided by the at least two receive antennas of the radar system, wherein the second detection information is specific for a second radar signal transmitted by a second transmit antenna of the radar system. A first angle determination and a second angle determination are performed. At least one comparison of the first angle information with the second angle information is performed in order to detect an ambiguity in the first angle determination for the determination of the direction information.

An object is to enable measurement of position and velocity of a measurement object. A velocity measurement device includes a transmitting means, a receiving means, and a signal processing means. The transmitting means transmits a transmission signal by a transmitting antenna toward a measurement object. The receiving means receives a reflected wave from the measurement object with multiple receiving antennas and generates a reception signal for each of the receiving antennas. The signal processing means obtains a phase plane of the reflected wave with respect to an antenna plane of the multiple receiving antennas from a phase difference between the reception signals to specify an arrival direction of the reflected wave, obtains a distance to the measurement object from a propagation delay time of the reflected wave, and calculates a phase fluctuation of the reflected wave to calculate a velocity of the measurement object from the phase fluctuation.

An anti-interference microwave detection method and microwave detection device, wherein the anti-interference microwave detection method can accurately eliminate the interference signal in the environment that have any frequency relationship with the local oscillator signal of the microwave detection device, including same frequency, adjacent frequency, and harmonic frequency, so that it is conducive to improve the feedback accuracy of the Doppler intermediate frequency signal for the detection of the motion of objects in the corresponding detection space, so that it is beneficial for achieving the combined detection of motion characteristics including human movement, micro-movement, breath and hear beat, the detection function of the microwave detection device is diversified and suitable for intelligent detection applications of multifunctional requirements.

An electronic device includes memory circuitry, interface circuitry, and processor circuitry. The processor circuitry is configured to transmit, to a plurality of electronic reference devices, a first signal, the first signal having a pulse width below a threshold. The processor circuitry is configured to determine, based on the received second signals and at least one predetermined time period, a time of flight of each of the second signals. The processor circuitry is configured to obtain, from the memory circuitry, reference positions of the plurality of electronic reference devices. The processor circuitry is configured to determine, based on the associations, one or more candidate positions of the electronic device. The processor circuitry is configured to determine, based on the distances, the one or more candidate positions, and the obtained reference positions, a position of the electronic device.

Embodiments provided herein allow for identification of one or more regions of interest in a radar return signal that would be suitable for selected application of super-resolution processing. One or more super-resolution processing techniques can be applied to the identified regions of interest. The selective application of super-resolution processing techniques can reduce processing requirements and overall system delay. The output data of the super-resolution processing can be provided to a mobile computer system. The output data of the super-resolution processing can also be used to reconfigure the radar radio frequency front end to beam form the radar signal in region of the detected objects. The mobile computer system can use the output data for implementation of deep learning techniques. The deep learning techniques enable the vehicle to identify and classify detected objects for use in automated driving processes. The super-resolution processing techniques can be implemented in analog and/or digital circuitry.