A system and method for detecting a rotary wing aircraft. A return electromagnetic signal, reflected by a rotary wing aircraft, is received through an electromagnetic signal detection apparatus. The aircraft includes a plurality of propeller blades attached to at least one motor. At least one propeller blade has at least one portion with a reflectivity different from other portions. A first time series data of the return electromagnetic signal is received. A second time series data is determined based on the first time series data and a predefined threshold. A characteristic of the second time series data is used to determine whether it corresponds to the known aircraft.

In an embodiment, a method includes: obtaining a time sequence of measurement frames of a radar measurement of a scene, each measurement frame of the time sequence of measurement frames comprising data samples along at least a fast-time dimension and a slow-time dimension, a slow time of the slow-time dimension being incremented with respect to adjacent radar chirps of the radar measurement, a fast time of the fast-time dimension being incremented with respect to adjacent data samples; determining covariances of the data samples for multiple fast times along the fast-time dimension and using respective distributions of the data samples along the slow-time dimension; determining a range map of the scene based on the covariances using a spectrum analysis; and detecting one or more objects of the scene based on the range map.

Exemplary aspects are directed to circuitry that assesses and differentiates a set of targeted data and updates a high-level bin with a numerical value indicating the number of data elements that compared successfully with a predefined value range defined for each bin. A cumulative sum of the high-level bins may then be calculated. Following, a target threshold may be compared to the cumulative sum at each bin and then providing an indication upon discovering a cumulative sum exceeding the threshold. The targeted data may be further refined by changing (through circuitry or other intervention) the predefined range values and then reprocessing the targeted data.

The use of complex analysis enables enable various new methods and strategies for the identification of a RF transmission source in a radar detector. The incoming signal (13) is processed using complex signal representations (I, Q), allowing for the recognition of signal patterns in the radio spectrum which may be hidden by conventional methods using only demodulation of signals which are in-phase with a local oscillator. The additional signal information can aid in overcoming signal and noise discrimination challenges faced by radar detectors using traditional band-limited, scalar sampling strategies, including the challenge of discriminating between police radar signals and other radar sources that utilize signals in the same frequency ranges (e.g., radar door openers, traffic sensors, vehicle-based collision avoidance, cruise control, blind spot monitor emitters).

A discrete-time, digital filter for notch filtering a complex digital signal, the filter having a transfer function allowing selective filtering of complex signal components. A system for receiving a modulated signal, the system including a processor for adaptively generating filter coefficients of a discrete-time, digital filter, for filtering a complex discrete-time signal, the processor configured to (i.) identify a number of signal samples in a discrete-time signal, and use the signal samples to calculate autocorrelation values sufficient to calculate the filter coefficients, (ii.) solve a system of equations, the system of equations defined by a Toeplitz matrix and a vector to determine the coefficient values, the Toeplitz matrix defined using the autocorrelation values, and the vector defined as the autocorrelation values of a white noise signal.

An object detection method includes: obtaining a first offset value and a second offset value, setting a first detection threshold value by adding the first offset value to a first reference threshold value, setting a second detection threshold value by adding the second offset value to a second reference threshold value, obtaining a detection input, and performing target detection upon the detection input according to at least the first detection threshold value and the second detection threshold value. The first offset value is different from the second offset value. The first reference threshold value is determined for detecting if at least one object with a first value of an object characteristic exists. The second reference threshold value is determined for detecting if at least one object with a second value of the object characteristic exists. The second value is different from the first value.

In some aspects, a radar device may receive a received signal comprising a reflected frequency modulated continuous wave (FMCW) radar signal and interference. The radar device may identify the reflected FMCW radar signal based at least in part on performing a phase based search procedure to facilitate removing the interference from the received signal. The radar device may perform an action based at least in part on a characteristic of the identified reflected FMCW radar signal. Numerous other aspects are described.

A method for a radar sensor, in particular a radar sensor for motor vehicles. The method includes the steps: determining, for particular evaluation channels that correspond to different central antenna positions of relevant transmitting antennas and receiving antennas in one direction, and for particular individual radar targets, a respective individual radial velocity of the particular radar target associated with the particular evaluation channel, based on signals obtained in respective evaluation channels; estimating a particular velocity of the particular radar target based on the determined individual radial velocities of the radar target, the velocity including information concerning a velocity in the forward direction in relation to the radar sensor, and a tangential velocity; and associating radar targets as belonging to an extended radar object as a function of the estimated velocities of the radar targets. A radar sensor is also described.

Camera data and radar echoes are received from the surroundings. At least one radar echo is assigned to a delimiting frame of an object detected on the basis of a camera, the delimiting frame being generated using the camera data by comparing corresponding azimuth angles and specified distances of the radar echo and the object detected on the basis of a camera. In the event of a successful assignment, a distance which is assumed on the basis of a camera is corrected according to the distance of the respective detected object in the surroundings, said distance being determined in a radar-based manner. The respective delimiting frame together with the corrected distance is then output as an object data set which indicates a successful object detection.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing angular estimation using machine learning. In particular, a radar system 102 includes an angle-estimation module 504 that employs machine learning to estimate an angular position of one or more objects (e.g., users). By analyzing an irregular shape of the radar system 102's spatial response across a wide field of view, the angle-estimation module 504 can resolve angular ambiguities that may be present based on the angle to the object or based on a design of the radar system 102 to correctly identify the angular position of the object. Using machine-learning techniques, the radar system 102 can achieve a high probability of detection and a low false-alarm rate for a variety of different antenna element spacings and frequencies.