A method of producing blood flow velocity vector imagery, comprising: emitting N unfocused plane wave acoustic signals at each of M specific angles Θ m to a transducer array into a medium over substantially an entire field of measurement, wherein M and N are at least 2 and m ∈{1,..., M}; receiving scattered and reflected ultrasonic signals on the transducer array in response to the emission; processing the received ultrasonic signals to extract for each image point of an image of the medium information in the form of ensembles of observations r m = [ r m,..., r m ( N -1)] T,where r m is an image point signal for the angle Θ m and N is the number of unfocused plane wave acoustic signals emitted per specific angle Θ m, to construct a blood flow vector velocity signal, by: wall filtering the extracted information; using the wall-filtered information to form autocorrelation values and Doppler frequency estimates; partitioning of a bistatic range-rate model, f = Av + b + e,where f is a length- M vector of the Doppler frequency estimates, the flow velocity vector at the image point is v = [υ x,υ z] T, b represents bias due to aliasing of Doppler frequencies, e is stochastic error, and the model matrix A is dimensioned [ M × 2] and has rows a m ( θ m), where: a m = 1 λ ⁢ sin θ m, 1 + cos θ m, into linear and nonlinear parts; and solving the partitioned model by the weighted least squares scheme for the blood flow velocity vector v corresponding to at least one point in the medium; generating blood flow vector velocity signals; detecting the presence of blood flow corresponding to a display device pixel by qualifying blood flow vector velocity signals through a series of tests on values of quality metrics produced as byproducts of the blood flow vector velocity estimation procedure; and generating on a display device blood flow vector velocity imagery from the blood flow vector velocity signals. emitting N unfocused plane wave acoustic signals at each of M specific angles Θ m to a transducer array into a medium over substantially an entire field of measurement, wherein M and N are at least 2 and m ∈{1,..., M}; receiving scattered and reflected ultrasonic signals on the transducer array in response to the emission; processing the received ultrasonic signals to extract for each image point of an image of the medium information in the form of ensembles of observations r m = [ r m,..., r m ( N -1)] T,where r m is an image point signal for the angle Θ m and N is the number of unfocused plane wave acoustic signals emitted per specific angle Θ m, to construct a blood flow vector velocity signal, by: wall filtering the extracted information; using the wall-filtered information to form autocorrelation values and Doppler frequency estimates; partitioning of a bistatic range-rate model, f = Av + b + e,where f is a length- M vector of the Doppler frequency estimates, the flow velocity vector at the image point is v = [υ x,υ z] T, b represents bias due to aliasing of Doppler frequencies, e is stochastic error, and the model matrix A is dimensioned [ M × 2] and has rows a m ( θ m), where: a m = 1 λ ⁢ sin θ m, 1 + cos θ m, into linear and nonlinear parts; and solving the partitioned model by the weighted least squares scheme for the blood flow velocity vector v corresponding to at least one point in the medium; generating blood flow vector velocity signals; detecting the presence of blood flow corresponding to a display device pixel by qualifying blood flow vector velocity signals through a series of tests on values of quality metrics produced as byproducts of the blood flow vector velocity estimation procedure; and generating on a display device blood flow vector velocity imagery from the blood flow vector velocity signals. wall filtering the extracted information; using the wall-filtered information to form autocorrelation values and Doppler frequency estimates; partitioning of a bistatic range-rate model, f = Av + b + e,where f is a length- M vector of the Doppler frequency estimates, the flow velocity vector at the image point is v = [υ x,υ z] T, b represents bias due to aliasing of Doppler frequencies, e is stochastic error, and the model matrix A is dimensioned [ M × 2] and has rows a m ( θ m), where: a m = 1 λ ⁢ sin θ m, 1 + cos θ m, into linear and nonlinear parts; and solving the partitioned model by the weighted least squares scheme for the blood flow velocity vector v corresponding to at least one point in the medium; generating blood flow vector velocity signals; detecting the presence of blood flow corresponding to a display device pixel by qualifying blood flow vector velocity signals through a series of tests on values of quality metrics produced as byproducts of the blood flow vector velocity estimation procedure; and generating on a display device blood flow vector velocity imagery from the blood flow vector velocity signals.