A two-dimensional (2-D) direction of arrival estimation method for L-shaped array with automatic pairing is proposed. It exploits the conjugate symmetry property of the array manifold matrix to increase the effective array aperture and the number of virtual snapshots simultaneously, and then applies the principle of MUSIC to construct an angle cost function and transforms the conventional 2-D search into 1-D via a Rayleigh quotient, which can greatly reduce the computation complexity. Finally, the azimuth and elevation angles are estimated without pair matching. Simulation results show that the proposed method has a better performance and can resolve more sources than some existing computationally efficient methods.

As L-shaped array can provide good angle estimation performance and is easy to implement, many two-dimensional (2-D) direction-of-arrival (DOA) estimation algorithms have been developed. In this letter, we present a low-computation-complexity DOA estimation method for L-shaped array that exploits the conjugate symmetry property of the array manifold matrix to increase the effective array aperture and improve the angle estimation performance. Hence, the DOA estimation is obtained via combining propagator method (PM) with ESPRIT algorithm, which does not need spectrum peak search or additional angle pair matching procedure. Simulation results demonstrate the effectiveness of the proposed method.

Non‐uniform L‐shaped array consisting of two nested arrays and its computationally efficient two‐dimensional direction‐of‐arrival (DOA) estimation method are developed in this study. The basic idea of the proposed method is to utilise the property of nested arrays and the conjugate symmetry property of the signal auto‐correlation function for different time lags to construct a conjugate augmented spatial–temporal cross‐correlation matrix (CAST‐CCM) and form joint diagonalisation structure from the signal subspace corresponding to the CAST‐CCM. Hence, the DOAs are estimated and paired automatically via signal subspace joint diagonalisation technique. The proposed method can handle underdetermined DOA estimation with automatic matching and deal with the angle ambiguity problem when multiple sources have the same azimuth or elevation angles. Meanwhile, the proposed method is computationally efficient without multidimensional search. The effectiveness of the proposed method is verified through computer simulations.

Automatic modulation classification (AMC) is an essential part in a cognitive radio receiver. Benefited from the discriminative constellation characteristics among most modulations, AMC methods based on constellation diagrams usually achieve pleasant performance. However, in noncooperation communication systems, constellation diagrams expressing modulations explicitly are difficult to obtain via blind symbol timing synchronization, especially in complicated wireless channels. Therefore, this article proposes a novel constellation diagram-based AMC architecture called attentive Siamese networks (ASNs) by considering multitiming constellation diagrams (MCDs) and selecting the proper symbol timings at the feature level, which is a more robust way than the conventional signal-level symbol timing synchronization. In detail, convolutional neural networks sharing the same parameters first extract deep feature vectors for MCDs. Then, an attention inference module weights all the deep feature vectors. Finally, AMC is realized based on the weighted feature vectors. Moreover, the ASN architecture can be trained end-to-end. Comparing with the state-of-the-art methods that take diverse representations of received baseband signals as input, experimental results based on the RadioML 2018.01A dataset and non-Gaussian noise dataset demonstrate that ASN achieves a remarkable improvement, whose classification accuracy goes over 99% when the signal-to-noise ratio (SNR) > 10 dB.

In this paper, a multiple-input multiple-output (MIMO) integrated radar and communication system based on orthogonal frequency division multiplexing (OFDM) signals is proposed. We consider a bistatic scenario. Exploiting superimposed pilots and orthogonal space-time block code (OSTBC) precoding, transmitted power can be effectively used in both information symbol sending and target sensing with little energy waste and performance degradation. The expectation maximization (EM) is employed to estimate unknown symbols and the channel jointly, which can take the place of matched filtering in traditional radar. In the EM algorithm, we propose to calculate the channel expectation by using Gaussian distribution of the symbol estimation, which can make full use of the OSTBC property of sensing spatial diversity with fast maximum likelihood decoding. The autocorrelation response, and the Doppler response of moving targets in slow time are verified by numerical experiments. Simulations also show superimposed pilot is suitable in the integrated radar and communication system, even when targets with large Doppler shift exist.

To solve the angle ambiguity problem when multiple sources have the same azimuth or elevation angles, a novel two-dimensional direction of arrival (2-D DOA) estimation method with L-shaped array based on conjugate augmented spatial temporal technique is developed in this letter. The basic idea of the proposed method is to utilize the conjugate symmetry property of the signal auto-correlation function with different delays to construct a conjugate augmented spatial-temporal cross-correlation matrix (CAST-CCM) and form joint diagonalization structure from the signal subspace corresponding to the CAST-CCM. Hence, the DOAs are estimated and paired automatically via joint diagonalization technique. The proposed method can handle the angle ambiguity problem efficiently and can also work in the underdetermined case. The effectiveness of the proposed method is verified through computer simulations.