We present a simulation study of an algorithm for the range and angle of arrival(AOA) estimation with an interferometric synthetic aperture radar(InSAR) altimeter using a real digital elevation model(DEM). We also illustrate a step-by-step procedure of generating raw InSAR data, as well as their range and azimuth compressed data, which is to be used for the subsequent altitude and angle estimation. The AOA is estimated using a deterministic maximum likelihood estimator(DMLE) applied to the first arrived point for each pulse in the compressed data obtained with three antennas. The range bin size and the pulse repetition interval(PRI) are much smaller than the cell size of the DEM used in this study. To make the DEM compatible to the radar parameters, we first generate a higher resolution DEM by linearly interpolating the given DEM. After a brief description of the principle of the InSAR altimeter, the algorithms for altitude and angle estimation are presented, and their performance is assessed through simulation.
We present an implementation result of a computer GUI-based simulator using MATLAB to verify the performance of interferometric radar altimeter(IRA) which is possible to measure the slant range altitude and the cross-track angle to the nearest point for terrain aided navigation(TAN). After a brief description of the principle of TAN and IRA, we present that the grids are divided for the modeling of the reflected signal in digital elevation map(DEM) and so the radar cross section(RCS) of each grid is calculated and the signal-noise ratio(SNR) of the reflected signal in the radar beam width. And the signal processing procedures of the IRA and the structure of the IRA simulator are shown.
Many of modern radar systems employ antenna arrays with thousands of anetnnas. These antennas are usually grouped into a few subarrays or beams through the process of beamspace transformation, to estimate the angle of a target. In this letter, we consider a simple but pragmatic beamspace transformation that only allows beam steering, and present a technique of finding optimal steering angles that minimize the Cramér-Rao bound (CRB) for angle estimation. The CRB and mean squared error (MSE) of the angle estimate using the resulting optimal steering angles are compared with those using the widely used DFT-based beam steering.
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