Abstract
This paper presents two amplitude comparison monopulse algorithms and their covariance prediction equation. The proposed algorithms are based on the iterated least-squares estimation method and include the conventional monopulse algorithm as a special case. The proposed covariance equation is simple but predicts RMS errors very accurately. This equation quantitatively states estimation accuracy in terms of major parameters of amplitude comparison monopulse radar, and is also applicable to the conventional monopulse algorithm. The proposed algorithms and covariance prediction equations are validated by the numerical simulations with 100,000 Monte Carlo runs.
Highlights
Monopulse radar operates by comparing signal returns of four squinted beams steered around the expected target direction [1]
Two least-squares-based algorithms and their covariance prediction equations have been presented for amplitude comparison monopulse radar
The conventional monopulse problem is reinterpreted as a target location estimation problem in the track axis with four pseudorange equations in which geometrical distance is weighted by the antenna gain
Summary
Monopulse radar operates by comparing signal returns of four squinted beams steered around the expected target direction [1]. Some papers deal with formal noise analysis on the conventional algorithm, algorithm, it is extremely complex, and no useful information comes from it. The proposed algorithms have an iterative process that and a novel covariance prediction equation. The conventional monopulse algorithm is a special case of the proposed algorithms with the first iterative proposed covariance equation is simple but predicts very accurately the covariance of the estimated step. The proposed covariance equation is simple but predicts very accurately the covariance of the target location under the presence of measurement noise.
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