A radar apparatus includes a transmitting array antenna that transmits signals orthogonal to one another from a plurality of transmitting antennas, a receiving array antenna that receives the signals reflected from a target by a plurality of receiving antennas, and a signal processing unit that detects the target from reception signals received by the plurality of receiving antennas. The signal processing unit includes a correlation matrix calculation unit that determines a first correlation matrix corresponding to the transmitting array antenna and a second correlation matrix corresponding to the receiving array antenna, on the basis of the reception signals separated by a separation unit, and a detection unit that detects the target on the basis of an evaluation value calculated using eigenvectors of the first correlation matrix and the second correlation matrix.

A method for determining reception window timing using a measuring station receiving an antenna beam width, receiving an antenna tilt angle, receiving an altitude A, determining a far projection angle Δf, determining a near projection angle Δn, and determining a far projection range corresponding to the far projection angle Δf and based at least upon the values of Δf and A. The method further includes determining a near projection range corresponding to the near projection angle Δn and based at least upon the values of Δn and A, determining an end time of a reception window based at least upon the value of the far projection range the reception window being a window of time in which a response from the target station is expected to be received, and determining a start time of the reception window based at least upon the value of the near projection range

A method for processing coherent MIMO radar processing DDMA waveforms and includes NT.sub.X transmitters and NR.sub.X receivers, comprising the steps of a) generating waveforms on at most NT.sub.X transmitters, the waveforms, modulo the pulse repetition frequency Fr, being identical from one transmitter to the next, to within a phase ramp specific to each transmit path; b) generating, for at least one receiver, a Range-Doppler representation of the echoes of the transmitted waveforms, where, for each receiver, the echoes of a transmitter over a plurality of range cells occupy at least one frequency cell in the Doppler spectrum, called signal band, each signal band being specific to one of the transmitters, the placement of the signal bands in the Doppler spectrum being determined according to the phase ramp applied to each transmitter, the waveforms being generated so as to leave a portion of the Doppler spectrum between two signal bands unoccupied; c) identifying the transmitter corresponding to each signal band, on the basis of the Range-Doppler representation of the echoes of the transmitted waveforms. The method is particularly suitable for the millimetre band (W band), for automotive radar applications or applications of radar on board aeroplanes or drones, for the detection of fixed or moving target relative to the carrier.

A method for encoding and storing digital data, which include a plurality of real values, in a signal processing unit of a radar sensor in which at least one real value r in an exponential representation in the form r=m.Math.b.sup.k is stored, where m is a digital mantissa having a length p, b is a base, and k is a positive number that is encoded as a digital number having a length q. An exponential representation with b>2 is used for the compressed storage of the values r.

A method for encoding and storing digital data, which include a plurality of real values, in a signal processing unit of a radar sensor. In the method, at least one real value r in an exponential representation in the form r=m.Math.b.sup.k is stored, where m is a digital mantissa having a length p, b is a base, and k is a positive number that is encoded as a digital number having a length q. The values r for the compressed storage are transformed into an exponential representation in the form r=m*.Math.b.sup.f(k), where m* is the mantissa and f is a function of k that is selected from multiple functions, and the selection of function f takes place based on a value distribution of the values to be stored.

Methods are provided for identifying and quantifying information loss in a system due to uncertainty and analyzing the impact on the reliability of system performance. Models and methods join Fano's equality with the Data Processing Inequality in a Markovian channel construct in order to characterize information flow within a multi-component nonlinear system and allow the determination of risk and characterization of system performance upper bounds based on the information loss attributed to each component. The present disclosure additionally includes methods for estimating the sampling requirements and for relating sampling uncertainty to sensing uncertainty. The present disclosure further includes methods for determining the optimal design of components of a nonlinear system in order to minimize information loss, while maximizing information flow and mutual information.

An example method for performing a joint radon transform association includes detecting, by a processing device, a target object to track relative to a vehicle. The method further includes performing, by the processing device, the joint radon transform association on the target object to generate association candidates. The method further includes tracking, by the processing device, the target object relative to the vehicle using the association candidates. The method further includes controlling, by the processing device, the vehicle based at least in part on tracking the target object.

The present disclosure provides a radar apparatus including: an antenna including a first transmitting antenna, a second transmitting antenna, and a receiving antenna; a transmitter including a first modulator for generating a first transmission signal having an inverted phase of a source signal and transmitting the first transmission signal through the first transmitting antenna, and a second modulator for generating a second transmission signal having a shifted phase of the source signal and transmitting the second transmission signal through the second transmitting antenna; a receiver for receiving a reflection signal of the first transmission signal and the second transmission signal reflected from the object through the receiving antenna; and a controller for obtaining information for the object based on the reflection signal. According to the present disclosure, it is possible to efficiently detect the object using the antenna having a simple structure.

Devices and systems for implementing a walk-through gate are provided. The devices include a walk-through gate structure having boundaries that have curved inner surfaces on each side of a cavity. The curved inner surfaces are partially covered by a reflective material. The devices include radio frequency (RF) signal transmitters positioned tangent to the curved inner surfaces and RF signal receivers. The devices also include apertures that provide access to the cavity of the walk-through gate structure.

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.