Reflected sonar signals arising from one or more possible unknown objects are distinguished according to a first criterion, and possible shapes each having a defined unique associated point are assigned each of the possible unknown objects. Then the three dimensional points are tracked by the sonar system.

Reflected sonar signals arising from one or more possible unknown objects are distinguished according to a first criterion, and possible shapes each having a defined unique associated point are assigned each of the possible unknown objects. Then the three dimensional points are tracked by the sonar system.

Systems and methods are disclosed for conducting an ultrasonic-based inspection. The systems and methods perform operations comprising: receiving a plurality of scan plan parameters associated with generating an image of at least one flaw within a specimen based on acoustic echo data obtained using full matrix capture (FMC); applying the plurality of scan plan parameters to an acoustic model, the acoustic model configured to determine a two-way pressure response of a plurality of inspection modes based on specular reflection and diffraction phenomena; generating, by the acoustic model based on the plurality of scan plan parameters, an acoustic region of influence (AROI) comprising an acoustic amplitude sensitivity map for a first inspection mode amongst the plurality of inspection modes; and generating, for display, a first image comprising the AROI associated with the first inspection mode for capturing or inspecting the image of the at least one flaw.

In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameterssuch as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

The disclosure relates to methods, systems, and apparatuses for virtual sensor data generation and more particularly relates to generation of virtual sensor data for training and testing models or algorithms to detect objects or obstacles. A method for generating virtual sensor data includes simulating, using one or more processors, a three-dimensional (3D) environment comprising one or more virtual objects. The method includes generating, using one or more processors, virtual sensor data for a plurality of positions of one or more sensors within the 3D environment. The method includes determining, using one or more processors, virtual ground truth corresponding to each of the plurality of positions, wherein the ground truth comprises a dimension or parameter of the one or more virtual objects. The method includes storing and associating the virtual sensor data and the virtual ground truth using one or more processors.

An environment sensing method includes the following steps, carried out by a data processor a) defining an occupancy grid comprising a plurality of cells; b) acquiring at least one measurement result from a distance sensor, representative of the distance of one or more nearest targets; and c) computing an occupation probability of the cells of the occupancy grid by applying to the measurement an inverse sensor model stored in a memory device in the form of a data structure representing a plurality of model grids associated to respective distance measurement results, at least some cells of a model grid corresponding to a plurality of contiguous cells of the occupancy grid belonging to a same of a plurality of angular sectors into which the field of view of the distance sensor is divided, and associating a same occupation probability to each one of the plurality of cells. An apparatus programmed or configured for carrying out the environment sensing method and a computer-implemented method of computing an inverse sensor model suitable for carrying out the environment sensing method are also provided.

Disclosed are a method and system for evaluating sonar self-noise at a ship design stage. The method includes: building a ship structure full-scale geometric simulation model; acquiring loss factors and sonar transducer space outfitting acoustic absorption coefficient material parameters; acquiring mechanical excitation, hydrodynamic excitation, and propeller excitation; inputting the loss factors and the sonar transducer space outfitting acoustic absorption coefficient material parameters into an established statistical energy evaluation model, and applying a mechanical excitation to a face plate of foundation of the built ship structure full-scale geometric simulation model, applying a hydrodynamic excitation to the surface of a ship hull, and applying a propeller excitation to a stern shaft to perform calculation of sonar self-noise of a ship to obtain total spectral density level of the sonar self-noise; and evaluating spectral density level calculation results by index requirements. The method is applicable in risk evaluation of sonar self-noise indexes.

Disclosed are a method and system for evaluating sonar self-noise at a ship design stage. The method includes: building a ship structure full-scale geometric simulation model; acquiring loss factors and sonar transducer space outfitting acoustic absorption coefficient material parameters; acquiring mechanical excitation, hydrodynamic excitation, and propeller excitation; inputting the loss factors and the sonar transducer space outfitting acoustic absorption coefficient material parameters into an established statistical energy evaluation model, and applying a mechanical excitation to a face plate of foundation of the built ship structure full-scale geometric simulation model, applying a hydrodynamic excitation to the surface of a ship hull, and applying a propeller excitation to a stern shaft to perform calculation of sonar self-noise of a ship to obtain total spectral density level of the sonar self-noise; and evaluating spectral density level calculation results by index requirements. The method is applicable in risk evaluation of sonar self-noise indexes.

An image processing method for reducing distortion of a depth image may include: obtaining a plurality of original images based on light beams which are emitted to and reflected from a subject; determining original depth values of original depth images obtained from the plurality of original images, based on phase delays of the light beams, the reflected light beams comprising multi-reflective light beams that distort the original depth values; determining imaginary intensities of the multi-reflective light beams with respective to each phase of the multi-reflective light beams, based on regions having intensities greater than a predetermined intensity in the original depth images; correcting the original depth values of the original depth images, based on the imaginary intensities of the multi-reflective light beams; and generating corrected depth images based on the corrected original depth values.

A method for use in acoustic imaging, comprising: transmitting, from a transmitter, a first sound wave pulse at a first frequency determined by a maximum sampling rate of a receiver; transmitting at least one second sound wave pulse at a frequency substantially equal to the first frequency, the first and at least one second sound wave pulses being transmitted substantially within a fraction of a sample interval of the receiver; receiving and sampling, at the receiver, a reflection of at least two of the first and at least one second pulses to generate a set of receiver samples; and expanding the set of receiver samples, based on the first frequency and a total number of the first and at least one second pulses transmitted, to generate an expanded sample set with a larger number of samples than the set of receiver samples.