A tracking method and system includes receiving a primary radar report after establishment of a real track of the aircraft, determining a false track slant range associated with the aircraft based on an effective altitude of the aircraft above a ground or water surface and an aircraft slant range defined between the radar arranged on the ground surface and the aircraft, determining a capture area based on the false track slant range and an azimuth of the aircraft, and determining whether the primary radar report is a false report by comparing a position of the aircraft determined from the primary radar report to the capture area.

Systems and methods for Wi-Fi sensing using UL-OFDMA are provided. Wi-Fi sensing systems include sensing devices and sensing transmitters configured to communicate through radio-frequency signals. Initially, first channel resources are allocated to first expected transmissions from the sensing transmitters and first sensing trigger message to trigger first series of sensing transmissions from the sensing transmitters is transmitted. Further, a first series of sensing transmissions is received, and the first series of sensing measurements are generated. Thereafter, identification of feature of interest is obtained and a selection of sensing transmitters is determined. Second channel resources are allocated to second expected transmissions from the selection of sensing transmitters. A second sensing trigger message to trigger a second series of sensing transmissions from the selection of the sensing transmitters is provided. A series of sensing transmissions is received, and a second series of sensing measurements is generated based on the second series of sensing transmissions.

The present disclosure is directed to processing data associated with a non-radar type sensing device to identify data points associated with a radar type sensing device that are likely secondary radar reflections such that a processor of a sensing apparatus can direct processing resources to processing radar data that are associated with primary radar reflections. The receipt of secondary radar reflections may cause a processor of a sensing apparatus to identify that an object is located at a location when there is no object in that location. Because of this, methods and apparatus of the present disclosure identify and avoid processing radar data that are likely to be associated with a object that does not exist. Eliminating false radar data, therefore, can prevent a processor of a sensing apparats from performing unnecessary processing tasks and can help prevent that processor from making false determinations.

Systems and methods for determining a presence of cargo within a container are described. The determination may comprise transmitting a first electromagnetic signal within the container and receiving a first reflected electromagnetic signal corresponding to the first electromagnetic signal. The first reflected electromagnetic signal may be converted into a set of first magnitude values corresponding to a set of bin values representing a respective distance from the radar device. The first magnitude values may be integrated, using a moving window, over successive subranges of bin values to produce a set of integrated magnitude values. A set of integrated base magnitude values may be subtracted from the set of integrated magnitude values to produce a set of normalized integrated magnitude values. A presence of cargo within the container may then be determined by comparing each of the normalized integrated magnitude values with corresponding threshold values.

A method for radar signal processing separates an electric signal generated by Doppler processors of a radar into alternating current (AC) magnitudes with Doppler frequencies corresponding to absolute values between 0.1 and 4 Hz and direct current (DC) magnitudes with no Doppler frequency. An AC line and a DC line are generated, based on the AC magnitudes and the DC magnitudes, respectively, by adding respective energies across Doppler cells for each range cell and subsequently applying CFAR detector algorithm respectively. An energy level difference between an energy spike in the AC line and another energy spike in the DC line is calculated and compared with a threshold set for detecting a breathing target that is stationary. If the energy level difference is greater than the threshold, the breathing target that is stationary is detected and reported to the radar operator.

In some examples, a system includes a weather radar device configured to transmit radar signals, receive first reflected radar signals at a first time, and receive second reflected radar signals at a second time. In some examples, the system also includes processing circuitry configured to determine a first magnitude of reflectivity based on the first reflected radar signals and determine a second magnitude of reflectivity based on the second reflected radar signals. In some examples, the processing circuitry is also configured to determine a temporal variance in reflectivity magnitudes based on determining a difference in reflectivity between the first magnitude and the second magnitude. In some examples, the processing circuitry is further configured to determine a presence of ice crystals based on the first magnitude of reflectivity, the second magnitude of reflectivity, and the temporal variance in reflectivity magnitudes.

An axis deviation angle estimation device estimates a vertical axis deviation angle of a radar device based on roadside object information including information on a plurality of reflection points on a roadside object and road surface information including information on a plurality of reflection points on a road surface. The vertical axis deviation angle is an angle of deviation of an actual mounting direction from a reference mounting direction in a vertical direction. The actual mounting direction is an actual direction of the radar device, and the reference mounting direction is a direction of the radar device when the radar device is mounted in a reference state.

A system can include a computer including a processor and a memory, the memory storing instructions executable by the processor to receive point cloud data. The instructions further include instructions to generate a plurality of feature maps based on the point cloud data, each feature map of the plurality of feature maps corresponding to a parameter of the point cloud data. The instructions further include instructions to aggregate the plurality of feature maps into an aggregated feature map. The instructions further include instructions to generate, via a feedforward neural network, at least one of a segmentation output or a classification output based on the aggregated feature map.

A millimeter wave radar apparatus determining a fall posture is applied to a human body. The millimeter wave radar apparatus includes a microprocessor and a millimeter wave radar. The millimeter wave radar is electrically connected to the microprocessor. The millimeter wave radar is configured to transmit a radar wave to the human body. The millimeter wave radar is configured to receive a reflected radar wave reflected from the human body based on the radar wave. The microprocessor is configured to obtain a point cloud information based on the reflected radar wave. The microprocessor is configured to utilize the point cloud information to determine whether the human body is in the fall posture.

A handling device (103) for loads (105) includes a radar transmitter (107), a radar sensor (111) and a data-processing device. The radar transmitter (107) is configured to irradiate at least part of the handling device (103) and/or the load (105). The radar sensor (111) is configured to detect at least part of the irradiated handling device (103) and/or the irradiated load (105). The data-processing device is configured to determine the position of at least part of the handling device (103) and/or the load 105) with reference to a signal from the radar sensor (111). At least a part (405) of the handling device (103) is radar-absorbing.