An object of the present invention is to provide an obstacle detection system and an obstacle detection method for a trajectory traveling vehicle, which are capable of detecting a front obstacle on a trajectory and around the trajectory with high accuracy. The system includes: a monitoring area setting processing unit that sets an obstacle monitoring area for detecting an obstacle; a front obstacle monitoring unit that monitors an obstacle in the obstacle monitoring area using a sensor that horizontally scans the front of the train; and an obstacle detection unit that detects an obstacle in the obstacle monitoring area based on a monitoring result by the front obstacle monitoring unit, in which the front obstacle monitoring unit complements a gap in a detection region of the sensor at a first position with a detection region of the sensor while the train moves from the first position to a second position.

Disclosed are devices, systems and methods for compensating radar measurements of a vehicle. One exemplary method includes generating a set of velocity hypotheses of a target object based on a first measurement data obtained from sensors mounted on the vehicle; generating cluster velocity estimates by applying a clustering algorithm to a second measurement data obtained from the sensors; and providing one or more selected velocity hypotheses from the set of velocity hypothesis as compensated radar measurements for the target object based on the cluster velocity estimates.

In a general aspect, a radar system includes a vapor cell sensor system and a radio frequency (RF) optic. The vapor cell sensor system includes a vapor cell sensor, and the RF optic is configured to direct an RF field onto the vapor cell sensor. The RF field includes one or more RF pulses that define a radar signal. The radar system also includes a signal processing system configured to perform operations that include generating a digital signal based on a signal from the vapor cell sensor system. The digital signal represents a measured response of the vapor to the RF field over a time period. The operations also include applying a matched filter to the digital signal to generate a filtered signal and processing the filtered signal to determine properties of the RF field sensed by the vapor cell sensor over the time period.

A clutter suppressing device for suppressing echo data of static clutter components indicating reflection waves caused by radar transmission signals reflecting on a static object is provided. The device includes a static clutter component suppressor configured to receive reception signals containing the static clutter components, and suppress the static clutter components, a reference data memory configured to store, as reference data, echo data of the reception signals obtained in fine weather and in which the static clutter components are suppressed by the static clutter component suppressor, and a rain component extracting module configured to extract echo data indicating rain components contained in the reception signals, by removing the reference data stored in the reference data memory from echo data of the reception signals obtained in rainy weather and in which the static clutter components are suppressed by the static clutter component suppressor.

A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The one or more controllers execute instructions to determine a signal to noise ratio (SNR) distance ratio for the input detection points generated by the plurality of radar sensors, where a value of the SNR distance ratio is indicative of an object being a ghost vehicle. The one or more controllers also determine an effective particle number indicating a degree of particle degradation for the importance sampling for each variable that is part of the state variable. In response to determining the effective particle number is equal to or less than a predetermined threshold, the one or more controllers estimate a ghost position for the ghost vehicle.

A radar anti-spoofing system for an autonomous vehicle includes a plurality of radar sensors that generate a plurality of input detection points representing radio frequency (RF) signals reflected from objects and a controller in electronic communication with the plurality of radar sensors. The controller executes instructions to determine time-matched clusters that represent objects located in an environment surrounding the autonomous vehicle based on the input detection points from the plurality of radar sensors. The controller determines an adjusted signal to noise (SNR) measure for a specific time-matched cluster by dividing an SNR of the specific time-matched cluster by a range measurement of the specific time-matched cluster. The controller determines a velocity-ratio measure of the time-matched cluster by dividing a motion-based velocity by a Doppler-frequency velocity, and identifies the time-matched cluster as either a ghost object or a real object.

The present invention includes systems and methods for a continuous-wave (CW) radar system for detecting, geolocating, identifying, discriminating between, and mapping ferrous and non-ferrous metals in brackish and saltwater environments. The radar system (e.g., the CW radar system) generates multiple extremely low frequency (ELF) electromagnetic waves simultaneously and uses said waves to detect, locate, and classify objects of interest. These objects include all types of ferrous and non-ferrous metals, as well as changing material boundary layers (e.g., soil to water, sand to mud, rock to organic materials, water to air, etc.). The radar system (e.g., the CW radar system) is operable to detect objects of interest in near real time.

A method of processing a radar hit from an object using, for each of a plurality of cells, a signal strength threshold, a hit rate threshold, a time of last detection; and receiving, for one of the plurality of cells corresponding to the object, a measured signal strength, a measured hit rate and a time of measurement. The object is identified as clutter if the measured hit rate is greater than the hit rate threshold, and the measured signal strength is less than signal strength threshold. The signal strength threshold is above a conventional CFAR signal threshold. Measured Doppler strength may also be used to identify clutter. Identification can be determined using Doppler-polarity-specific data values. The hit rate and the mean Doppler speed of the one of the plurality of cells can be updated using a running average.

A simulation method for predicting a false positive for a predefined region outside a desired field of view of a radar sensor. Calculated primary rays having a respective primary energy level represent the radar signal. Reflected rays are calculated based on the primary rays or other reflected rays and based on geometrical data for at least one item within the predefined region. An energy level is determined for each reflected ray based on an estimated reflectivity of the at least one item and based on the primary energy level of the respective primary ray, and a clustering level for the reflected rays is determined based on distances of the respective reflection points. A probability for an occurrence of a false positive is estimated based on the energy level and the clustering level.

A method for sensing sneezing based on a wireless signal includes obtaining a wireless signal, where the wireless signal propagates in space including a first object. Doppler estimation is performed on the wireless signal, to obtain Doppler information of the wireless signal. The Doppler information of the wireless signal may be used for indicating impact of the first object on a frequency of the wireless signal. Whether the first object is sneeze droplets is determined based on the Doppler information of the wireless signal.