For the purpose of locating the orthogonal frequency division multiplexing (OFDM) signal emitter efficiently and accurately in multipath environment, an innovative direction position determination (DPD) algorithm based on the multiple carrier characteristic of OFDM signals is proposed in this paper. The time of the arrival (TOA), angle of arrival (AOA) and Doppler frequency shift information contained in the observed data are jointly employed to locate the emitter. High-dimensional search can be avoided by decoupling unknown parameters and the estimation performance is improved due to the expansion of the subspace. The compact closed-form expressions of the Cramer-Rao lower bound (CRLB) corresponding to the parameters of interest are also derived. Simulation results show that compared with the traditional DPD based on MUSIC algorithm, the localization performance of the proposed algorithm can attain the CRLB at higher noise levels.

Orthogonal time frequency space (OTFS) is a new communication waveform modulation method. It was first proposed in the field of communication to counter the doubly dispersive channel under high Doppler, but it also has great potential in the field of radar. This letter proposes an OTFS modulated waveform design method with dual functions of radar and communication based on phase perturbation, mainly studies the radar performance of OTFS waveform, derives the expression of the ambiguity function of OTFS waveform, focuses on the range-velocity two-dimensional sidelobe, and optimizes the peak sidelobe level of ambiguity function by adding phase perturbation to each communication symbol. Simulation results show that this algorithm can effectively reduce the peak sidelobe level of OTFS, and it only brings slight symbol error rate loss. The tradeoff between radar and communication can be achieved by adjusting the upper bound of the perturbation.

Orthogonal Time Frequency Space (OTFS) modulation holds significant potential for diverse applications in both sensing and communication fields. This paper mainly investigates waveform optimization for OTFS modulation, aiming to design pilot symbol matrices with low sidelobe levels and data symbol matrices with high communication rates under peak-to-average power ratio constraints. We first formulate the problem of minimizing the weighted integrated sidelobe levels and maximizing the communication rate in the delay-Doppler domain. Subsequently, to address the complicated optimization problem, a Majorization-Minimization based algorithm is proposed to decompose it into a series of subproblems. These subproblems are then reformulated as unconstrained optimization problems on the Stiefel manifold, and the Riemannian conjugate gradient method is employed to solve them efficiently. Moreover, a faster iterative algorithm is proposed based on second-order Taylor approximation to accelerate the convergence speed. Simulation results validate that the proposed algorithms effectively achieve pilot matrices with desirable ambiguity functions and data symbol matrices with high communication rates under various weighting factors.

The phase information's role in deep neural networks (DNNs) to solve the electromagnetic inverse scattering problems is investigated. The feedforward neutral network model with complex-valued (CV) data stream and the corresponding CV backpropagation training algorithm are proposed to realize CV convolutional neural networks. Numerical examples are carried out to demonstrate the phase information's role in DNNs in terms of generalization capability as well as the convergence speed in the training stage.

The orthogonality of multiple-input multiple-output (MIMO) radar transmission signals can be improved without changing the operating parameters of the radar through multipulse joint processing. An efficient echo processing algorithm and optimization criteria for a phase-coded set suitable for multi-pulse joint processing are introduced in this paper. The characteristics of both the normalized auto-ambiguity function and the crossambiguity function of the signals are considered in the optimization objective. In order to effectively improve the efficiency of generating phase-coded sets while ensuring signal orthogonality, an accelerated optimization algorithm based on global search combined with local search is proposed in this paper. More specifically, computational complexity is reduced by avoiding a large number of unnecessary calculations during the updating of the ambiguity functions after the code set is perturbed. The orthogonality gain brought by the optimization algorithm proposed in this article, as well as the time advantage of the accelerated optimization algorithm, is verified by simulation results.

This paper proposes a three-dimensional inverse synthetic aperture radar (ISAR) imaging method for high-speed targets in short-range using an impulse radar. According to the requirements for high-speed target measurement in short-range, this paper establishes the single-input multiple-output (SIMO) antenna array, and further proposes a missile motion parameter estimation method based on impulse radar. By analyzing the motion geometry relationship of the warhead scattering center after translational compensation, this paper derives the receiving antenna position and the time delay after translational compensation, and thus overcomes the shortcomings of conventional translational compensation methods. By analyzing the motion characteristics of the missile, this paper estimates the missile’s rotation angle and the rotation matrix by establishing a new coordinate system. Simulation results validate the performance of the proposed algorithm.