DETECTION OF SMALL AIRCRAFT WITH DOPPLER WEATHER RADAR

ABSTRACT We present a method that can be performed in parallel to reflectivity estimation in weather radar and that allows one to detect small aircraft. Though small aircraft and large birds might produce comparable reflectivity signals their spectral signatures are considerably different. A small aircraft with propellers can be recognized from its spectrum via modulations produced by Doppler shifts from rotating parts. Generally such a spectrum has an elevated spectral floor compared to the spectrum of a resolution volume without an airplane. The spectral floor level is used for detection. The method is demonstrated on a clear air case observed with Doppler weather radar on March 6, 2007 at elevation 0.5°. Index Terms -frequency domain analysis, noise measurement, adaptive signal processing

A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors

Direct computation of the discrete Fourier transform (DFT) and its FFT computational algorithms requires multiplication (and addition) of complex numbers. Complex number multiplication requires four real-valued multiplications and two real-valued additions, or three real-valued multiplications and five real-valued additions, as well as the requisite added memory for temporary storage. In this paper, we present a method for computing a DFT via a natively real-valued algorithm that is computationally equivalent to a N=2k-length DFT (where k is a positive integer), and is substantially more efficient for any other length, N. Our method uses the eigenstructure of the DFT, and the fact that sparse, real-valued, eigenvectors can be found and used to advantage. Computation using our method uses only vector dot products and vector-scalar products

Blind Channel Equalization with Colored Source Based on Constrained Optimization Methods

Tsatsanis and Xu have applied the constrained minimum output variance (CMOV) principle to directly blind equalize a linear channel—a technique that has proven effective with white inputs. It is generally assumed in the literature that their CMOV method can also effectively equalize a linear channel with a colored source. In this paper, we prove that colored inputs will cause the equalizer to incorrectly converge due to inadequate constraints. We also introduce a new blind channel equalizer algorithm that is based on the CMOV principle, but with a different constraint that will correctly handle colored sources. Our proposed algorithm works for channels with either white or colored inputs and performs equivalently to the trained minimum mean-square error (MMSE) equalizer under high SNR. Thus, our proposed algorithm may be regarded as an extension of the CMOV algorithm proposed by Tsatsanis and Xu. We also introduce several methods to improve the performance of our introduced algorithm in the low SNR condition. Simulation results show the superior performance of our proposed methods