A data processing apparatus is provided, which includes processing circuitry. The processing circuitry is configured to acquire a data set from target detected by a detection apparatus, perform rendering of the data set, and generate a plurality of views arranged on a screen. Each view of the plurality of views includes a plurality of pixels. Each pixel included in the plurality of views is associated with a plurality of pieces of information including a first information displayed on the screen and a second information that indicates a view among the plurality of views to which the pixel belongs.

An evaluation system for processing measured data which include physical measured data detected with the aid of one or multiple sensors, and/or realistic synthetic measured data of the sensor(s), into one or multiple evaluation results. The system includes at least two input stages independent from each other, which are designed to receive measured data and process these measured data into precursors. At least one processing stage, receives the precursors from all input stages as inputs and is designed to process one or multiple input precursor(s) into a shared intermediate product. At least one output stage, which is designed to process the intermediate product into one or multiple evaluation result(s) of the evaluation system. A method for training the evaluation system. A method for operating the evaluation system is also provided.

A method and electronic device for determining relevant signals in radar signal processing. The electronic device includes a radar transceiver, a memory, and a processor. The processor is configured to cause the electronic device to obtain, via the radar transceiver of the electronic device, radar measurements for one or more modes in a set of modes; process the radar measurements to obtain a set of radar images; identify relevant signals in the set of radar images based on signal determination criteria for an application; and perform the application using only the relevant signals.

The system comprises a plurality of sensor systems, a counter drone, and a processor. A sensor system of the plurality of sensor systems comprises one or more sensors that are connected to a network. The counter drone is connected to the network. The processor is configured to receive an indication of a potential target from the plurality of sensor systems; generate a fused data set for the potential target, determine whether the potential target comprises the threat drone based at least in part on the fused data set; and in response to determining that the potential target comprises the threat drone, provide counter drone instructions to the counter drone.

A method for determining the elevation angle and/or azimuth angle of a signal received by an ultrasound sensor includes: providing an ultrasound sensor with a frequency-dependent radiation pattern; transmitting a first ultrasound wave at a first frequency; transmitting a second ultrasound wave at a second frequency different from the first frequency; receiving reflections of the first and second waves, the reflections being caused by an object; and determining the elevation angle of the first and second reflected waves based on amplitudes of the reflections of the first and second waves. Determining the elevation angle (and/or azimuth angle includes calculating a ratio between the amplitudes of received reflections of the first and second waves and mapping a calculated ratio to an elevation angle and/or azimuth angle. The mapping is based on a predetermined ratio curve or ratio dataset which associates a certain amplitude ratio to an elevation angle and/or azimuth angle.

A method of automatically managing a center pivot irrigation machine comprising steps of: (a) providing at least one center pivot irrigation machine and positioning said center pivot irrigation machine such that said center pivot irrigation machine is movable within an irrigated plot around a center thereof; (b) providing a ground penetration radar; (c) mounting said ground penetration radar on said center pivot irrigation machine; (d) moving said center pivot irrigation machine about said center of said irrigated plot; (e) scanning said irrigated by said ground penetration radar at frequencies ranging between 200-1200 MHz; (f) calculating a distribution of soil moisture over a depth from a soil surface; and (g) creating an irrigation plan according to said distribution.

Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.

The invention relates to a method for establishing the presence of a misalignment of at least one sensor within a sensor group with two or more sensors which detects objects in the surroundings of a motor vehicle, wherein at least two of the sensors differ from each other in their measuring principle and the measurement signals from the sensors are compared with each other.

A light detection and ranging system is provided. The system includes a Galvanometer mirror; a multiple-facet light steering device; and a controller device comprising one or more processors, memory, and processor-executable instructions stored in memory. The processor-executable instructions comprise instructions for receiving a first movement profile of the Galvanometer mirror of the LiDAR scanning system; receiving calibration data of the multiple-facet light steering device of the LiDAR scanning system; generating a second movement profile of the Galvanometer mirror based on the calibration data and the first movement profile; and providing one or more control signals to adjust movement of the Galvanometer mirror based on the second movement profile.

Systems and methods for asynchronous late-fusion of measurements. State information and intermediate values may be calculated as measurements arrive and are stored. When late sensor measurements arrive out of the temporal order in which the measurements were generated, the late measurements are stored in temporal order rather than the order in which measurements arrive. State information is then recalculated to account for the late-arriving sensor measurement, with state outputs propagated forward in temporal order using the previously computed intermediate values to speed up computation. In this manner, more accurate revised state information is efficiently generated, accounting for any late-arriving measurements. This modular processing framework also enables sensors to be added or changed, which may cause measurements to arrive asynchronously, without having to reprogram the processing framework.