A moving object detection device using an ultrasonic sensor, a method thereof, and a warning system using the same is provided. The moving object detection device includes: an ultrasonic sensor configured to receive a plurality of reflected signals per period; and a detector configured to divide a sensing distance of the ultrasonic sensor into a plurality of regions, detect a region of the plurality of regions as a region of interest (ROI) when an intensity of a reflected signal of the plurality of reflected signals exceeds a predetermined threshold in a region of the plurality of regions, and detect a moving object based on changes in the ROI.

One illustrative integrated electromagnetic-acoustic sensor includes: a ground plane; a patch antenna above the ground plane to send or receive an electromagnetic (EM) signal having an EM signal frequency; and an array of capacitive micromachined acoustic transducers formed by cavities between the patch antenna and a base electrode to send or receive an acoustic signal having an acoustic signal frequency. One illustrative sensing method includes: driving or sensing a EM signal between a ground plane and a patch antenna; and driving or sensing an acoustic signal between the patch antenna and a base electrode, the base electrode and the patch antenna having an array of capacitive micromachined acoustic transducer cavities therebetween.

A method for estimating a range of a moving target includes emitting, from a target, a first ultrasound signal T, wherein the first ultrasound signal T is generated based on a first differential Zadoff-Chu sequence x; receiving, at a receiver, a second ultrasound signal R, which corresponds to the first ultrasound signal T, wherein the second ultrasound signal R includes a second differential Zadoff-Chu sequence y; applying a maximum likelihood estimator to the first ultrasound signal T and the second ultrasound signal R to calculate an initial time of flight estimate tau.sub.corr; and calculating an initial range estimate d.sub.corr of the target by multiplying the initial time of flight estimate tau.sub.corr with a speed of sound c. A differential Zadoff-Chu sequence is different from a Zadoff-Chu sequence.

A method for estimating a range of a moving target includes emitting, from a target, a first ultrasound signal T, wherein the first ultrasound signal T is generated based on a first differential Zadoff-Chu sequence x; receiving, at a receiver, a second ultrasound signal R, which corresponds to the first ultrasound signal T, wherein the second ultrasound signal R includes a second differential Zadoff-Chu sequence y; applying a maximum likelihood estimator to the first ultrasound signal T and the second ultrasound signal R to calculate an initial time of flight estimate tau.sub.corr; and calculating an initial range estimate d.sub.corr of the target by multiplying the initial time of flight estimate tau.sub.corr with a speed of sound c. A differential Zadoff-Chu sequence is different from a Zadoff-Chu sequence.

Sound signals that are not audible are generated by one or more speakers disposed in the vehicle. Reflected sound signals from a driver or a passenger of the vehicle that are not audible are received by one or more microphones disposed in the vehicle. A behavior-induced acoustic pattern is detected based on the reflected ultrasound signals. The behavior-induced acoustic pattern is analyzed to recognize a behavior of the driver or the passenger of the vehicle. A response or an alert is generated according to the recognized behavior of the driver or the passenger in the vehicle.

This disclosure relates generally to methods and systems for unobtrusive and automated detection of frequencies of spatially located distinct parts of a machine. Location of vibration and detection of vibration frequency of each vibrating part in a machine is critical for routine monitoring and fault detection in the machine. Current solutions use either high frames per second (fps) industrial grade camera or stroboscopes tuned at one particular frequency. Manual stroboscopes require manual intervention for objects moving at different speeds with high convergence time. Point-lasers need prior knowledge of exact location of faults. Also Point-by-point scanning of a large machine body is time consuming. In the present disclosure, a movement detector such as RADAR enables detecting all vibration frequencies that also serve to reduce the search space of a stroboscope configured to start strobing at each detected vibration frequency to enable mapping of each vibration frequency to a corresponding vibrating part.

An in-vehicle object determining apparatus cooperates with an obstacle sensor unit, which detects an obstacle at a first time. An estimated detected state is calculated as a detected state of the obstacle estimated to be detected by the obstacle sensor unit at a second time after a lapse of a predetermined time period from the first time, on condition that the obstacle is assumed to be under stationary state, based on (i) a vehicle-relative position of the obstacle detected at the first time, (ii) a sensor position of the obstacle sensor unit, and (iii) a vehicle position change during a period from the first time to the second time. It is determined that the obstacle is a moving object based on a discrepancy between the estimated detected state of the obstacle and a real detected state of the obstacle actually detected by the obstacle sensor unit at the second time.

An in-vehicle object determining apparatus cooperates with an obstacle sensor unit, which detects an obstacle at a first time. An estimated detected state is calculated as a detected state of the obstacle estimated to be detected by the obstacle sensor unit at a second time after a lapse of a predetermined time period from the first time, on condition that the obstacle is assumed to be under stationary state, based on (i) a vehicle-relative position of the obstacle detected at the first time, (ii) a sensor position of the obstacle sensor unit, and (iii) a vehicle position change during a period from the first time to the second time. It is determined that the obstacle is a moving object based on a discrepancy between the estimated detected state of the obstacle and a real detected state of the obstacle actually detected by the obstacle sensor unit at the second time.

A method is described for spatial modelling of an interior of a vehicle. The method includes transmitting a wireless signal, from each of multiple transmitters, each transmitter having a known location in the vehicle, receiving, by multiple receivers, multiple reflection signals having been reflected in the interior of the vehicle, each receiver having a known location in the vehicle. The method also includes, for the received reflection signals, determining a source data set by determining multipath propagation components, Doppler shifts, phase shifts and time differences of the received reflection signals, and determining a spatial model of at least a portion of the vehicle interior by applying a computer vision algorithm on the source data set. A system is also described for performing the method.

An acoustic model determination approach for a real-time locating system is disclosed. The system includes one or more transmitting devices and one or more mobile devices. The acoustic model may be determined by deriving an acoustic representation of sub-structures within the building, and then forming the acoustic model based on the acoustic representation and the location and orientation of the static acoustic transmitting device. In another embodiment, an acoustic signal is transmitted from a static acoustic transmitting device, with the reflected signals received by the same static acoustic transmitting device in a receiving mode. Based on these received acoustic signals, the acoustic model is formed based on the reflected signals and the location and orientation of the static acoustic transmitting device.