Abstract
In this paper, we consider the joint angle-range estimation in monostatic FDA-MIMO radar. The transmit subarrays are first utilized to expand the range ambiguity, and the maximum likelihood estimation (MLE) algorithm is first proposed to improve the estimation performance. The range ambiguity is a serious problem in monostatic FDA-MIMO radar, which can reduce the detection range of targets. To extend the unambiguous range, we propose to divide the transmitting array into subarrays. Then, within the unambiguous range, the maximum likelihood (ML) algorithm is proposed to estimate the angle and range with high accuracy and high resolution. In the ML algorithm, the joint angle-range estimation problem becomes a high-dimensional search problem; thus, it is computationally expensive. To reduce the computation load, the alternating projection ML (AP-ML) algorithm is proposed by transforming the high-dimensional search into a series of one-dimensional search iteratively. With the proposed AP-ML algorithm, the angle and range are automatically paired. Simulation results show that transmitting subarray can extend the range ambiguity of monostatic FDA-MIMO radar and obtain a lower cramer-rao low bound (CRLB) for range estimation. Moreover, the proposed AP-ML algorithm is superior over the traditional estimation algorithms in terms of the estimation accuracy and resolution.
Highlights
The angle and range parameters are decoupled in the monostatic FDA-MIMO radar, there exists another serious problem of range ambiguity. e problem of unambiguous range in the monostatic FDA-MIMO radar was pointed in [12], where the maximum unambiguous range was derived
In [15], the angle and range estimation cramer-rao low bound (CRLB), MSE, and resolution of monostatic FDAMIMO radar were analysed and compared with traditional MIMO radar, which illustrated the advantages of monostatic FDA-MIMO radar. e multiple signal classification (MUSIC) algorithm was utilized in monostatic FDA-MIMO radar [16], where the resolution threshold for angle and range estimation was analysed
Reconstruction algorithm based on compressed sensing always subject to the grid-mismatch effect and deteriorating the estimation performance [19]. e off-grid angle and range estimation approach for monostatic FDAMIMO radar with single snapshot based on decoupled atomic norm minimization (DANM) is proposed in [20]
Summary
The angle and range parameters are decoupled in the monostatic FDA-MIMO radar, there exists another serious problem of range ambiguity. e problem of unambiguous range in the monostatic FDA-MIMO radar was pointed in [12], where the maximum unambiguous range was derived. In traditional monostatic FDA-MIMO radar, reducing the frequency increment can extend the unambiguous range. E multiple signal classification (MUSIC) algorithm was utilized in monostatic FDA-MIMO radar [16], where the resolution threshold for angle and range estimation was analysed.
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