The cross ambiguity function (CAF) has been commonly used to find time difference of arrival (TDOA) and frequency difference of arrival (FDOA). In most cases, direct computation of the CAF by using a conventional method such as fast Fourier transform is too computationally intensive. Thus, a two-stage approach consisting of a coarse mode to find rough TDOA/FDOA estimates and a fine mode for precise estimation was introduced. However, there has been no methodology for selecting an interpolation factor determined by the sampling frequency and target precision which significantly affects the computational complexity. In addition, even if the computational complexity can be reduced by using the optimal interpolation factor, the huge transmission data through the datalink between sensors and the central station still remains to be an obstacle for an electronic warfare (EW) system. In this respect, we derive an optimal interpolation factor and then propose a new two-stage TDOA/FDOA estimation algorithm using a resampling block to reduce the computational complexity and the data size simultaneously in EW systems. In the proposed method, the optimal interpolation factor can be used irrespective of the sampling frequency and the target precision. Simulation results show that the optimal interpolation factor efficiently reduces the computational burden without the loss of estimation performance.

Linear frequency modulation (LFM) signals have been widely used for target detection in active sonar systems due to their robustness to reverberation. However, LFM active sonar requires a large number of reference signals for detecting targets moving with unknown speeds. To obtain more accurate detection results, more reference signals are required, resulting in an increased computational burden and memory size. To cope up with this problem, we propose a new fast target detection method that is robust to the variation of unknown target speed. A large number of reference signals come into a single reference signal by aligning them with precalculated time-shifts, which is followed by a summation process. Both narrowband and wideband cases are considered. The proposed method secures a signal-to-noise ratio (SNR), approaching that of the optimal matched filter output, that is also robust to the variation of target speed and thus it is very useful for the practical use in antitorpedo torpedoes or supercavitating underwater missiles that need to equip low-complexity and robust signal processing systems. Moreover, a rough Doppler estimation is presented using the proposed replica design method. Performance analyses show that the proposed method provides output SNR close to the optimal performance and that the computational load is extremely reduced as compared to the conventional LFM target detection method.

In passive emitter localisation, the accuracy of an estimator is significantly affected by the geometric relationship between the sensors and the emitter. The estimation performance varies with the manoeuvre strategies of sensors, even if the same estimator is utilised to locate an emitter. To achieve desirable performance by providing a guideline for manoeuvring sensors, this study analysed the effects of relative sensor‐emitter geometry/velocity for when time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements are utilised to locate an unknown emitter in an electronic warfare (EW) system. The analysis starts with the derivation of a partitioned Fisher information matrix (FIM) from the partial derivatives of TDOA/FDOA measurements. Then, the Cramer–Rao lower bounds on the variances of the estimates for an emitter position and velocity are computed from the submatrices of the FIM. Based on the analyses, the estimation accuracies are investigated according to the relative sensor‐emitter geometry/velocity under selected sensor manoeuvre scenarios. Simulations are included to verify the theoretical results, followed by discussions for establishing a sensor geometry and manoeuvre strategy for EW activities.

We consider a signal detection problem by using deep learning techniques in a multiple-input multiple-output (MIMO) decode-forward (DF) relay channel. There exist some suboptimal detectors such as the near maximum likelihood (NML) detector and the NML with two-level pair-wise error probability (NMLw2PEP) detector in the channel. However, the NML detectors require an exponentially increasing complexity as the number of transmit antennas increases. More seriously, without the channel state information (CSI) of the source-relay (SR) link, there is no detector that can achieve good performance even at high complexity. In this paper, we propose a deep learning approach to the NML (DL-NML) detector that achieves good performance with low complexity regardless of whether the CSI of the SR link is known or not at the destination. The DL-NML detector can detect signals in changing channels after a single training by using randomly generated channels. Furthermore, we propose a linear detector and a semidefinite relaxation approach to the NML detector to compare with the DL-NML detector in performance and complexity. The complexity analysis and simulation results validate the superiority of the proposed DL-NML detector.

This study presents a two‐step least‐squares approach for estimating the position and velocity of a moving emitter using time difference of arrival (TDOA) and frequency‐difference of arrival (FDOA) measurements in a two‐dimensional scenario. The proposed method first estimates the emitter position based on the asymptotes of TDOA hyperbolae and then linearises the non‐linear equations of FDOAs using the estimate of the emitter position obtained in the first stage. Although the proposed method has lower performance than other methods in terms of estimation accuracy, it provides a closed‐form solution without geometric constraints of sensor placement and an initial guess of emitter parameters. Since many methods derive the estimates of position and velocity with iterative numerical techniques, the proposed method can be a good alternative to set the initial guess under conditions of low measurement error. Simulation results are included to validate the algorithm, and its performance is compared with the Cramer–Rao lower bound.