This document describes techniques, apparatuses, and systems for radar interference mitigation using signal pattern matching. Radar signals (e.g., chirps) received by a radar system may include interference from other nearby radar systems. The interference can result in reduced sensitivity of the radar system. The techniques, apparatuses, and systems described herein mitigate the interference by selecting an uncorrupted segment of the radar signal that neighbors a corrupted segment, analyzing the radar signal to identify a match segment that has similar signal characteristics to the neighbor segment, and replacing the corrupted segment with a segment that is adjacent to the match segment. In this manner, a noise floor of the radar system may be lowered, leading to increased sensitivity.

Aspects and implementations of the present disclosure address challenges of the existing technology by enabling lidar-assisted identification and characterization of visibility-reducing media (VRM) such as fog, rain, snow, dust in autonomous vehicle applications, using lidar sensing. VRM can be identified and characterized using a variety of techniques, including analyzing a spatial distribution of low-intensity lidar returns, detecting pulse elongation of VRM-returns associated with reflection from VRM, determining intensity of VRM-returns, determining reduction of intensity of returns from various reference objects, and other techniques.

Disclosed are a pulse radar apparatus that detects a position and a motion of a target, and an operating method thereof. The pulse radar apparatus includes a clock signal generator that outputs a transmission clock signal and a reception clock signal, a transmitter that generates a first signal, a receiver that receives an echo signal and the reception clock signal, and generates a second signal, and a signal processor that converts the second signal into a digital signal and analyzes the digital signal. The clock signal generator controls a transmission-to-reception clock delay, and generates a synchronization signal. The signal processor converts the digital signal into a representative value and analyzes the second signal using the representative value. The representative value is one of an accumulated sum of the digital signal in a time duration between synchronization signals and an average value of the digital signal in the time duration between synchronization signals.

The present invention relates to a method of effectively removing various vibration noises using microwave Doppler radar, and an apparatus therefor. The method comprises the steps of: (a) generating and transmitting an oscillation frequency to a dynamic target, and receiving a signal reflected from the dynamic target and various signals generated around the dynamic target; (b) generating a Doppler IF signal from each of n received signals; (c) converting each Doppler IF signal into digital data; (d) configuring digital signals into a data set, and converting the data set into a frequency component symbol set; (e) calculating a value by adding index symbols and dividing by n reception antennas; and (f) classifying deviation between spectrum components of a commonly-generated periodic signal and an uncommon aperiodic signal, and obtaining only a periodic signal through filtering. The present invention can improve accuracy of sensing a biometric signal.

Systems, methods, tangible non-transitory computer-readable media, and devices associated with radar validation and calibration are provided. For example, target positions for targets can be determined based on imaging devices. The targets can be located at respective predetermined positions relative to the imaging devices. Radar detections of the targets can be generated based on radar devices. The radar devices can be located at a predetermined position relative to the imaging devices. Filtered radar detections can be generated based on performance of filtering operations on the radar detections. A detection error can be determined for the radar devices based on calibration operations performed using the filtered radar detections and the target positions determined based on the one or more imaging devices. Furthermore, the radar devices can be calibrated based on the detection error.

Systems, methods, tangible non-transitory computer-readable media, and devices associated with radar validation and calibration are provided. For example, target positions for targets can be determined based on imaging devices. The targets can be located at respective predetermined positions relative to the imaging devices. Radar detections of the targets can be generated based on radar devices. The radar devices can be located at a predetermined position relative to the imaging devices. Filtered radar detections can be generated based on performance of filtering operations on the radar detections. A detection error can be determined for the radar devices based on calibration operations performed using the filtered radar detections and the target positions determined based on the one or more imaging devices. Furthermore, the radar devices can be calibrated based on the detection error.

Upon each new detection, called pivot detection, by a radar system, the method includes the steps consisting of: grouping together, with the pivot detection, grouped detections, defined as detections that belong to a sweep preceding the sweep of the pivot detection and that have a non-nil probability according to a grouping criterion; filtering the grouped detections so as to keep only detections that are kinematically strictly coherent with the pivot detection, by: initializing a histogram, each dimension of which is a temporal variation of a coordinate measured by the radar system; computing a potential value interval for each coordinate of the pivot detection and each grouped detection; computing a minimum temporal variation and a maximum temporal variation for the or each coordinate from potential value intervals of the pivot detection and each grouped detection; incrementing the set of classes of the histogram whose index along each dimension is located between the computed minimum and maximum temporal variations; and detecting a target once at least one class of the histogram reaches a predefined value.

A method for close-range detection, includes transmitting, via a radar transceiver, radar signals to detect an object. The method also includes determining whether the object includes a pattern code based on reflections of the radar signals received by the radar transceiver. In response to determining that the object includes the pattern code, the method includes identifying range information about a range between the electronic device and the pattern code. The method further includes selecting, based on the range information, one or more signals from the reflections of the radar signals that are reflected off of the pattern code. Additionally, the method includes identifying, based on the one or more signals, information about the pattern code.

A maritime radar system is provided, comprising a transmitter, a receiver, and one or more processors arranged to provide range and azimuth discrimination of a detection area by performing a delay/Doppler analysis of the echo of a single beam transmitted by the transmitter and received by the receiver.

A target acquisition system includes a transmitter configured to emit a plurality of pulses at a plurality of transmit times toward a target, a receiver configured to detect a plurality of photon arrival events at a plurality of receive times, and a processor configured to determine a range of the target and a range-rate of the target by identifying a subset of the receive times and a subset of the transmit times, generating scaled transmit times based on the subset of the transmit times and a plurality of trial target velocities relative to the receiver, cross-correlating the scaled transmit times and the subset of the received times to generate a plurality of cross-correlation power values, and calculating the range and the range-rate of the target based on the plurality of cross-correlation power values.