Techniques are discussed for determining reflected returns in radar sensor data. In some instances, pairs of radar returns may be compared to one another. For example, a velocity associated with a first radar return may be projected onto a radial direction associated with a second radar return to determine a projected velocity. In some examples, the second radar return may be a reflected return if the magnitude of the projected velocity corresponds to a magnitude of the second radar return. In some instances, a vehicle, such as an autonomous vehicle, may be controlled at the exclusion of information from reflected returns.

A detection system includes a ranging sensor and a controller circuit. The ranging sensor is configured to detect range rates of objects proximate a host vehicle. The controller circuit is in communication with the ranging sensor. The controller circuit is configured to determine a search area extending from the host vehicle. The controller circuit is further configured to determine a first histogram comprising counts of occurrences of the range rates detected within the search area. The controller circuit is further configured to determine a second histogram comprising the counts of occurrences of a portion of the range rates detected within the search area. The controller circuit is further configured to determine that a trailer is being towed by the host vehicle based on the first histogram and the second histogram.

A vehicle radar system (3) including a control unit arrangement (8) and at least one radar sensor arrangement (4) arranged to acquire a plurality of measured radar detections (z.sub.t, z.sub.t+1) at different times. The control unit arrangement (8) engages a tracking algorithm using the present measured radar detections (z.sub.t, z.sub.t+1) as input. For each track, for each one of a plurality of measured radar detections (z.sub.t, z.sub.t+1), the control unit arrangement (8) calculates a corresponding predicted detection (x.sub.t|t1|, x.sub.t+1|t|) and a corrected predicted detection (x.sub.t|t|, x.sub.t+1|t+1|), and calculates an innovation vector (19, 19) constituted by a first vector type (18a, ) and a second vector type (18b, r). The control unit arrangement (8) calculates a statistical distribution (24; .sub.inno,, .sub.inno,r) for at least one of the vector types (18a, ; 18b, r) and to determine how it is related to another statistical distribution (25; .sub.meas,, .sub.meas,r); and/or to determine its symmetrical characteristics. The tracking algorithm is maintained or re-initialize in dependence of result.

A vehicle radar sensor unit (2) arranged to acquire a plurality of radar detections, and including an antenna arrangement (3), a transmitter unit (4), a receiver unit (5) and a processing unit (6). The antenna arrangement (3) has at least two transmitter antennas (7, 8) and at least two receiver antennas (9, 10, 11, 12), where two transmitter antennas (7, 8) have a vertical spacing (h) between their respective phase centers (17, 18) that exceeds half the free-space wavelength of the transmitted signal. The processing unit (5) is arranged to determine a first radial velocity of each radar detection by tracking the change of radial distance (r) to each radar detection for a plurality of radar cycles; determine a second radial velocity that best matches the first radial velocity; track a plurality of measured heights (z) as a function of radial distance (r); and to choose a measured height (z.sub.GT) among the tracked measured heights (z) that has a minimal change from radar cycle to radar cycle.

A detection method, a detection device, a terminal, and a detection system are provided, for detecting a state of a target object in a detection area. The detection method includes: filtering a millimeter-wave radar signal received in the detection area; and extracting features suitable for indicating a motion mode of the target object in the detection area from each frame of the filtered millimeter-wave radar signal; monitoring an initial change point of the features through a flow window; caching a predetermined number of features starting from the initial change point; and identifying the cached features by a classifier to determine the state of the target object in the detection area.

In an apparatus for learning a scale factor used to calculate a true value of a speed of a vehicle from a detected speed that is a sensor reading from a vehicle speed sensor, a sensor reading acquirer acquires the detected speed of the vehicle from the vehicle speed sensor. A relative speed detector detects a relative speed of the vehicle to a stationary object based on a result of detection by an object detector that detects an object around the vehicle. A scale factor calculator calculates the scale factor based on the relative speed and the detected speed.

Systems and methods for measuring velocity and acceleration with a radar altimeter. In certain embodiments, a method for measuring velocity magnitude of a platform in relation to a surface includes transmitting a radar beam, wherein the radar beam is aimed toward a surface. The method also includes receiving a plurality of reflected signals, wherein the plurality of reflected signals correspond to portions of the transmitted radar beam that are reflected by a plurality of portions of the surface. Further, the method includes applying Doppler filtering to the plurality of signals to form at least one Doppler beam. Also, the method includes identifying range measurements within each Doppler beam in the at least one Doppler beam. The method further includes calculating one or more coefficients of the Taylor expansion of the velocity magnitude based on the range measurements of the at least one Doppler beam.

A method of determining the de-aliased range rate of a target in a horizontal plane by a host vehicle equipped with a radar system, said radar system including a radar sensor unit adapted to receive signals emitted from said host vehicle and reflected by said target, comprising: emitting a radar signal at a single time-point instance and determining from a plurality (m) of point radar detections measurements therefrom captured from said radar sensor unit, the values for each point detection of, azimuth and range rate; [.sub.i, {dot over (r)}.sub.i]; for each point detection determining a range rate compensated value ({dot over (r)}.sub.i,cmp); c) determining a plurality (j) of velocity profile hypotheses; for each (j-th) hypothesis determining modified compensated hypothesis range rates ({dot over (r)}.sub.i,j,cmp) in respect of each point detection on the target, based on the values of range rate compensated ({dot over (r)}.sub.i,cmp); for each j-th hypothesis, determining values of the longitudinal and lateral components of the range rate equation of the target {tilde over (c)}.sub.t,j and +{tilde over (s)}.sub.t,j; for each j-th hypothesis and for each point detection determining a velocity profile estimator range rate ({dot over ({circumflex over (r)})}.sub.i,j,cmp); for each hypothesis, for one or more point detections, determining a measure of the dispersion of, or variation between the velocity profile estimator range rates ({dot over ({circumflex over (r)})}.sub.i,j,cmp) for each velocity profile hypothesis and their respective modified range rates ({dot over ({circumflex over (r)})}.sub.i,j,cmp) from step d), or the dispersion of, or variation between, one or both of the velocity profile components {tilde over (c)}.sub.t,j and {tilde over (s)}.sub.t,j for each velocity profile hypothesis, and selecting the velocity profile where said measure of dispersion or variation is the lowest; setting the de-aliased range rate as the velocity of the velocity hypothesis selected.

Poor BSE estimation accuracy resulting from conventional Extended Kalman Filtering (EKF) approaches using RF OI sensors mounted on the ground as a remote bullet tracking sensor motivated the design and development of the present disclosure. The observer based BSE removes EKF process noise (state noise) and measurement noise (OI sensor noise) covariance matrices selection and tuning which have been long recognized by the estimation community as a time consuming process during the design stage; requires no consideration of interactions when having the control input signal as part of the state propagation equation; and provides a significant improvement in velocity estimation accuracy, in some cases to less than 1 m/s errors in all axes, thereby meeting the miss distance requirement with amble margin.

Radar systems are disclosed having phase measures limited to +/. An optical flow method considers the time derivative of the range with respect to phase (or velocity), and gives an indication of whether the phase is outside the measurable range by comparing the derivatives to forward and reverse wrap thresholds.