In this study, the authors address the problem of passive mixed near-field and far-field sources localisation using a uniform linear array (ULA) in which the signals received by the array may come from mixed sources. This study presents a new two stage cumulant-based multiple signal classification (MUSIC) algorithm for passive source localisation using fourth-order cumulants of a ULA data. The significant characteristic of the proposed algorithm is that it constructs a new special cumulant matrix to acquire more information of signals received by a ULA. Consequently, the proposed algorithm gives high direction of arrival (DOA) and range estimation accuracy, and alleviates the array aperture loss. Monte Carlo simulations are established to verify the effectiveness of the proposed method in increasing direction of arrival and range estimation accuracies.

This study investigates the problem of low probability of intercept (LPI)-based distributed multiple-input multiple-output (MIMO) radar waveform design against barrage jamming in signal-dependent clutter and coloured noise. Given the priori knowledge of the extended target impulse response, signal-dependent clutter, barrage jamming signals and coloured noise, the LPI-based scheme for optimal radar waveform design is proposed to minimise the total power consumption of the MIMO radar system by optimising the transmitted waveforms of different transmitters with a predetermined mutual information (MI) constraint for target characterisation performance. Firstly, the MI between the received echoes from the target at each receiver and the target impulse response is derived as a practical metric to characterise the parameter estimation performance of a target. Then, the LPI-based distributed MIMO radar waveform design strategy is developed. The resulting radar waveform optimisation problem is convex and solved analytically, whose solutions represent the optimum power allocation for each transmitter in the MIMO radar system. With the aid of numerical simulations, it is illustrated that to minimise the total transmission power, the optimal waveform should match with the target, clutter, jamming and coloured noise. In addition, it is also demonstrated that the LPI performance of the MIMO radar system can be significantly improved by employing the proposed radar waveform design scheme.

In this study, the authors propose an efficient extension of the standard empirical mode decomposition (EMD) for complex-valued univariate signal decomposition. The key idea of the extension is to convert a complex-valued univariate signal into a longer real-valued signal by augmenting the real part with the flipped imaginary part, and then to decompose it into intrinsic mode functions (IMFs) using the EMD once only. The bivariate IMFs are then retrieved from the obtained IMFs. Their empirical results on synthetic data show that the proposed method significantly outperforms the traditional bivariate EMD (BEMD) method in terms of computational efficiency while producing a comparable extraction error. Moreover, the proposed method shows better micro-Doppler signature analysis performance on physically measured continuous-wave radar data than that of the BEMD.

Future cellular systems will likely employ massive bi-dimensional arrays to improve performance by large array gain and more accurate spatial filtering, motivating the design of low-complexity signal-processing methods. The authors propose optimising a Kronecker-separable beamforming filter that takes advantage of the bi-dimensional array geometry to reduce computational costs. The Kronecker factors are obtained using two strategies: alternating optimisation and sub-array minimum mean square error (MMSE) beamforming with Tikhonov regularisation. According to the simulation results, the proposed methods are computationally efficient but come with source recovery degradation, which becomes negligible when the sources are sufficiently separated in space.

This study presents an improved multi-target multi-Bernoulli (IMeMBer) gamma Gaussian inverse Wishart (GGIW) filter for tracking multiple extended targets (ETs). The main contribution of this study consists of three parts, first, a novel method is proposed to obtain the unbiased cardinality estimation of multiple targets using the multi-Bernoulli recursion. As a variation of the existing cardinality-balanced MeMBer (CBMeMBer) filter, the presented filter is called the improved MeMBer filter, which overcomes the high detection probability limitation of the CBMeMBer filter. Second, based on the mathematical derivation, the IMeMBer filter is expanded to accommodate the characteristics of the ETs of which each target generates more than one measurement at each time step, and the GGIW method is used for its implementation. The resulting filter simultaneously provides the kinematic, extended and measurement rate states of ETs with an unknown and time-varying number. Third, the simulation results show that the presented filter achieves a considerable performance at the cost of less time, compared to the labelled multi-Bernoulli GGIW filter.