Abstract
The compressive array method, where a compression matrix is designed to reduce the dimension of the received signal vector, is an effective solution to obtain high estimation performance with low system complexity. While sparse arrays are often used to obtain higher degrees of freedom (DOFs), in this paper, an orthogonal dipole sparse array structure exploiting compressive measurements is proposed to estimate the direction of arrival (DOA) and polarization signal parameters jointly. Based on the proposed structure, we also propose an estimation algorithm using the compressed sensing (CS) method, where the DOAs are accurately estimated by the CS algorithm and the polarization parameters are obtained via the least‐square method exploiting the previously estimated DOAs. Furthermore, the performance of the estimation of DOA and polarization parameters is explicitly discussed through the Cramér‐Rao bound (CRB). The CRB expression for elevation angle and auxiliary polarization angle is derived to reveal the limit of estimation performance mathematically. The difference between the results given in this paper and the CRB results of other polarized reception structures is mainly due to the use of the compression matrix. Simulation results verify that, compared with the uncompressed structure, the proposed structure can achieve higher estimated performance with a given number of channels.
Highlights
In a traditional scalar sensor array, the time delay of the phased array is used to estimate the direction of arrival (DOA)
The estimated elevation angle is denoted as bφ = 1⁄2bφ1, ⋯, bφK T, and the estimation of the array manifold is defined as Ĝ = Ggrid ∈ CM2×K, which is used for the polarization parameter estimation
The signal parameter estimation results of 17 sources using the proposed structure are shown in Figure 3, where the actual parameter values are marked in red
Summary
In a traditional scalar sensor array, the time delay of the phased array is used to estimate the direction of arrival (DOA). Motivated by the above facts, in this paper, we mainly consider the orthogonal dipole antennas and propose a compressive measurement-based orthogonal dipole sparse array structure for the joint estimation of signal parameters. The compression leads to a degradation on the estimation performance, the proposed structure still outperforms the conventional dipole sparse array with the same number of channels, which can be clearly observed from both the theoretical derived CRB expression and the experimentally obtained root mean square error (RMSE).
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