In one example, a radar circuit uses computer processing circuitry for processing data corresponding to reflection signals via a sparse array. Output data indicative of signal magnitude associated with the reflection signals is generated, and then angle-of-arrival information is discerned therefrom by (e.g., iteratively): correlating the output data with at least one spatial frequency support vector indicative of a correlation peak for the output data; generating upper-side and lower-side support vectors which are neighbors along the spatial frequency spectrum for said at least one spatial frequency support vector, and providing, via a correlation of the upper-side and lower-side support vectors and said at least one spatial frequency support vector, at least one new vector that is more refined along the spatial frequency spectrum for said at least one spatial frequency support vector.

The method for locating a certain object is a method for locating the object by means of a detected locating signal. With this method, locating of precisely this object is checked in such a way that an object with at least a temporally variable reflective property is used, and an influence of this reflective property on the detected locating signal is checked. The locating system has a locating sensor for locating an object by means of a locating signal detected by a locating sensor, as well as an evaluating device which is configured to check an influence of a temporally variable reflective property of the object on the locating signal detected by the locating sensor.

Methods, systems, computer-readable media, and apparatuses for determining one or more attributes of at least one target based on eigenspace analysis of radar signals are presented. In some embodiments, a subset of eigenvectors to use for forming a signal or noise subspace is identified based on principal component analysis. In some embodiments, the subset of eigenvectors is identified based on estimating the total number of targets using a discrete Fourier transform (DFT) or other spectral analysis technique. In some embodiments, a DFT is used to identify areas of interest in which to perform eigenspace analysis. In some embodiments, a DFT is used to estimate one attribute of a target, and eigenspace analysis is performed to estimate a different attribute of the target, with the results being combined to generate a multi-dimensional representation of a field of view.

A system for estimating a parameter of an object includes a receiver configured to detect a return signal of a radar signal, and a processing device configured to sample the return signal to generate a series of signal samples, partition a time frame into a plurality of successive segments k, and for each segment k, apply a Doppler Fourier transform and calculate a complex value y.sub.k as a function of Doppler frequencies f.sub.D. The processing device is also configured to calculate an index based on an acceleration hypothesis and a velocity hypothesis of a set of hypotheses, and for each segment, select one or more Doppler frequency bins based on the index and extract components of the complex value y.sub.k (f.sub.D) associated with each selected Doppler frequency bin. The processing device is further configured to calculate a velocity and acceleration spectrum, and estimate an object parameter based on the spectrum.

A method for calculating an altitude of a target through an apparatus for calculating an altitude of the target, which comprises a plurality of MIMO radar virtual antennas, may comprise: receiving electromagnetic waves reflected from the target through a pair of virtual antennas classified into an upper antenna and a lower antenna and alternately arranged in two columns linearly; obtaining range information and phase information of the target from the pair of virtual antennas by analyzing the electromagnetic waves; and calculating altitude information of the target from position information of the pair of virtual antennas, and the range information and the phase information.

The present disclosure generally pertains to systems and methods for processing radar signals from a sparse MIMO array. In some embodiments, the signals from a MIMO radar array are processed to generate a sparse virtual array. Then, by using a two-dimensional (2D) variant of missing-data iterative adaptive approach (missing-data IAA or MIAA) to process the virtual array, the system can estimate information from the missing antennas of the sparse virtual array. Then, by using the now full virtually array, the system can process the virtual array using a variant of multi-dimensional folding (MDF) to discover the existence and location (e.g., distance, elevation, and azimuth) of objects (also called scatterers) within the MIMO radar array's field of view.

Methods, systems, computer-readable media, and apparatuses for determining one or more attributes of at least one target based on eigenspace analysis of radar signals are presented. In some embodiments, a subset of eigenvectors to use for forming a signal or noise subspace is identified based on principal component analysis. In some embodiments, the subset of eigenvectors is identified based on estimating the total number of targets using a discrete Fourier transform (DFT) or other spectral analysis technique. In some embodiments, a DFT is used to identify areas of interest in which to perform eigenspace analysis. In some embodiments, a DFT is used to estimate one attribute of a target, and eigenspace analysis is performed to estimate a different attribute of the target, with the results being combined to generate a multi-dimensional representation of a field of view.

The present disclosure generally pertains to systems and methods for processing radar signals from a sparse MIMO array. In some embodiments, the signals from a MIMO radar array are processed to generate a sparse virtual array. Then, by using a two-dimensional (2D) variant of missing-data iterative adaptive approach (missing-data IAA or MIAA) to process the virtual array, the system can estimate information from the missing antennas of the sparse virtual array. Then, by using the now full virtually array, the system can process the virtual array using a variant of multi-dimensional folding (MDF) to discover the existence and location (e.g., distance, elevation, and azimuth) of objects (also called scatterers) within the MIMO radar array's field of view.

In an embodiment, a method for tracking targets includes: receiving data from a radar sensor of a radar; processing the received data to detect targets; identifying a first geometric feature of a first detected target at a first time step, the first detected target being associated to a first track; identifying a second geometric feature of a second detected target at a second time step; determining an error value based on the first and second geometric features; and associating the second detected target to the first track based on the error value.

In an embodiment, a method for tracking targets includes: receiving data from a radar sensor of a radar; processing the received data to detect targets; identifying a first geometric feature of a first detected target at a first time step, the first detected target being associated to a first track; identifying a second geometric feature of a second detected target at a second time step; determining an error value based on the first and second geometric features; and associating the second detected target to the first track based on the error value.