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.

A method and apparatus for processing global navigation satellite signals, or radar signals, specifies an arrival time of a signal having a shape similar to a known pseudo-random noise sequence (PRN) of rectangular pulses. Two quadrature signals are generated and six correlations are calculated and multiplied by a correlation coefficient. The results of one of quadrature signals are summed and a timing error is estimated. An improved signal arrival time is generated by adding the estimated timing error to the predicted signal arrival time is generated.

Embodiments described herein can address these and other issues by using radar machine learning to address the radio frequency (RF) to perform object identification, including facial recognition. In particular, embodiments may obtain IQ samples by transmitting and receiving a plurality of data packets with a respective plurality of transmitter antenna elements and receiver antenna elements, where each data packet of the plurality of data packets comprises one or more complementary pairs of Golay sequences. I/Q samples indicative of a channel impulse responses of an identification region obtained from the transmission and reception of the plurality of data packets may then be used to identify, with a random forest model, a physical object in the identification region.

Embodiments described herein can address these and other issues by using radar machine learning to address the radio frequency (RF) to perform object identification, including facial recognition. In particular, embodiments may obtain IQ samples by transmitting and receiving a plurality of data packets with a respective plurality of transmitter antenna elements and receiver antenna elements. I/Q samples indicative of a channel impulse responses of an identification region obtained from the transmission and reception of the plurality of data packets may then be used to identify, with an autoencoder, a physical object in the identification region.

A distance measuring apparatus includes: a DTC generator unit that generates DTC signals having edges delayed to define time segments; a template generator unit that generates template signals consecutively in a pre-designated number within the time segments in response to the DTC signals; a coarse time determiner unit that determines the time segment in which a delayed signal is received by calculating correlations with the consecutively generated template signals; a fine time measurer unit that determines the time at which the delayed signal is received within the time segment determined at the coarse time determiner unit from the results of calculating correlations between multiple template signals within the determined time segment and the delayed signal; and a distance calculator unit that calculates the total delay duration of the delayed signal and calculates the distance to the measurement target object from the calculated delay duration.

Apparatus and methods for signal generation, reception, and calibration involving quadrature modulation and frequency conversion. Embodiments of the present invention provide extremely wide bandwidth, high spectral purity, versatility and adaptability in configuration, and ease of calibration, and are particularly well-adapted for use in integrated circuitry.

A method for the coherent integration of Fill Pulses in Pulse Doppler Radar sensors is disclosed. The present invention uses a pre- and a post-coherent waveform transmission and reception period to collect transient signals from reflections of Fill Pulses throughout the range extent. It then reassembles these signals to produce additional coherently integrable pulses of interval returns that are input to the filter and coherently integrated along with normally coherently integrated pulses. The result is an improved Signal To Noise Ratio (SNR) and Signal To Clutter Ratio (SCR) which is related to the total number of pulses emitted, including the Fill Pulses. This improvement can be obtained almost solely by signal processing in a digitally controlled Radar, and requires few if any hardware modifications.

A radar signal transmitter transmits first and second radar signals at different first and second frequencies. A radar receiver receives reflected radar signals and generates receive signals indicative of the reflected radar signals. A first receive signal is indicative of a first reflected radar signal generated by reflection of the first transmitted radar signal, and a second receive signal is indicative of a second reflected radar signal generated by reflection of the second transmitted radar signal. A processor receives the first and second receive signals and computes a difference between the first and second receive signals to generate a difference signal. The processor processes the difference signal to provide radar information for the region, the processor adjusting at least one of amplitude and phase of at least one of the first and second receive signals such that the difference is optimized at a preselected range from the receiver.

Example software defined radar architectures are disclosed. Example chipsets disclosed herein to implement a software defined radar architecture include a digital processor chip including a first serial port and a second serial port. Disclosed example chipsets also include a transmitter chip to generate a plurality of transmit signals based on baseband radar waveform data to be obtained from the digital processor chip, the transmitter chip including a third serial port to communicate with the first serial port of the digital processor chip to obtain the baseband radar waveform data. Disclosed example chipsets further include a receiver chip to determine baseband received radar data from a plurality of radar signals, the receiver chip including a fourth serial port to communicate with the second serial port of the digital processor chip to provide the baseband received radar data to the digital processor chip.

A method for transforming and reconstructing a signal includes receiving a plurality of samples of a waveform of the signal at different points in time. The waveform of the signal is transformed, for each sample, into an in-phase (I) component and a quadrature (Q) component. A derotational circuit applies a delayed complex conjugate multiple (DCM) to the signal to determine a constant product having an I component (I.sub.c) and a Q component (Q.sub.c). A magnitude component is determined based on I.sub.c and Q.sub.c. A delta phase component is determined based on I.sub.c and Q.sub.c. The magnitude component is processed to create a processed magnitude component. The delta phase component is processed to create a processed delta phase component. An IQ waveform is created by reconstructing the waveform of the signal based on the processed magnitude component and the processed phase component.