Abstract
The monopulse angle measuring technique is widely adopted in radar systems due to its simplicity and speed in accurately acquiring a target’s angle. However, in a spatial adaptive array, beam distortion, due to adaptive beamforming, can result in serious deterioration of monopulse performance. In this paper, a novel constrained monopulse angle measuring algorithm is proposed for spatial adaptive arrays. This algorithm maintains the ability to suppress the unwanted signals without suffering from beam distortion. Compared with conventional adaptive monopulse methods, the proposed algorithm adopts a new form of constraint in forming the difference beam with the merit that it is more robust in most practical situations. At the same time, it also exhibits the simplicity of one-dimension monopulse, helping to make this algorithm even more appealing to use in adaptive planar arrays. The theoretical mean and variance of the proposed monopulse estimator is derived for theoretical analysis. Mathematical simulations are formulated to demonstrate the effectiveness and advantages of the proposed algorithm. Both theoretical analysis and simulation results show that the proposed algorithm can outperform the conventional adaptive monopulse methods in the presence of severe interference near the mainlobe.
Highlights
Monopulse is an established technique in radars for fast and precise estimation of direction of target [1,2]
Solution was proposed in [18] and it was found that this solution is equivalent to the maximum likelihood (ML) estimator when the sum beam weight is derived for optimal signal-to-noise ratio (SNR)
The monopulse ratio formed by the proposed algorithm is able to maintain the linearity around the look direction and minimize the effect from noise jamming
Summary
Xinyu Zhang 1,3 , Yang Li 1,4, *, Xiaopeng Yang 1 , Le Zheng 2 , Teng Long 1 and Christopher J. Beijing Key Laboratory of Embedded Real-time Information Processing Technology, Beijing Institute of Technology, Beijing 100081, China. Received: 27 September 2016; Accepted: 27 December 2016; Published: 8 January 2017
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