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.

The conventional methods used to estimate the direction of arrival (DOA) of linear frequency modulated (LFM) signals at low signal-to-noise ratios (SNRs), such as the echoes reflected by a small unmanned aerial vehicle (UAV), demonstrate major performance deterioration. In order to eliminate the problem and achieve highly accurate DOA estimation for low-SNR echoes, this paper proposes a novel estimation approach that applies two accumulative methods based on fractional Fourier transform (FrFT). According to the findings of this paper, the algorithm directly accumulates the FrFT result of each echo when it is not possible to determine in advance the speed of the target. When the radial velocity of the small UAV has been estimated ahead of time, the algorithm performs phase compensation to enhance the accumulative effect. Through coherent integration, the algorithm then extracts all the peaks of the target echo waveform, which are then used for the construction of a fractional autocorrelation matrix. Thereafter, multiple signal classification is employed for DOA estimation. Furthermore, with the proposed algorithm, the DOAs of multi-target echoes can be estimated accurately. The effectiveness of the proposed algorithm was verified using Monte–Carlo simulation trials, and the root-mean-square-error of DOA estimation was close to the Cramer–Rao bound.

Copy number alterations (CNAs) are hallmarks of cancer, which are now been routinely measured by different techniques and used for diagnostic and prognostic purpose. Efficient and accurate detection of the breakpoint positions in heterogeneous cancer sample measured with intrinsic random noise and subjected to technical and biological biases is a challenging practical and methodological problem. To improve the CNA estimates, the authors present the probabilistic approach for breakpoints detection that gives confidence masks (the system of local segmentation profiles with confidence probabilities) tuned using experts estimates. The authors show that the asymmetric exponential power distribution matches well the uncertainties (jitter) in the breakpoint locations. The confidence upper and lower boundary masks for the breakpoint location are built using this function. The confidence masks are then tuned based on the medical expert annotations of the training set of the breakpoints obtained by the standard circular binary segmentation (CBS) algorithm. Comparison of modified confidence masks and experts annotations on the testing set of CNA profiles of neuroblastoma showed improvement of the CNA estimates.

A non-adaptive space-time clutter canceller (NSCC) for multi-channel (MC) synthetic aperture radar (SAR) was proposed. First, a new three-part range equation was derived on the basis of the two-dimensional Taylor series expansion. Then, each part of the model was analysed. By compensating of the high-order coupling part and the compression of the Doppler extension part of the derived equation, the interlaced signal of a moving target and a clutter patch was easily separated in a space-time domain. The clutter signal in different pulses only contained a constant phase difference. Using radar parameters, the authors constructed a non-adaptive clutter canceller that prevented traditional space time adaptive processing (STAP) issues, such as secondary sample support, computational complexity burden, and unknown moving target information. Compared with the representative non-adaptive method, that is displaced phase centre antenna (DPCA), NSCC is robust to a small degree of parameter error. It can be applied when DPCA condition is not satisfied. The effectiveness of the proposed method was validations through simulation.

This study focuses on the analysis of a decode-and-forward relay-based asymmetric radio frequency-free space optical (RF-FSO) communication system. These types of communication systems are of very high speed, secure, and cost-effective. Such systems can be used to provide the last-mile access to many household users and can provide temporary network access during disasters and link failures. In the communication model analysed, the RF path is considered as Rayleigh fading, and the FSO path is considered as Málaga ()-distributed turbulence fading with the pointing error. Two types of detection schemes consisting of intensity modulation/direct detection and heterodyne detection are considered at the receiver for the analysis. For this cooperative communication system, novel mathematical expressions for the cumulative distribution function, probability density function, and moment generating function of the end-to-end signal-to-noise ratio are derived. With the aid of these statistical characteristics, new closed mathematical formulations are obtained for outage probability and the average bit error rate for different binary and M-ary modulation approaches.

Adaptive beamforming methods are sensitive to underlying assumptions on the environment, sources, or sensor array violation, especially when interferences are moving fast. In this study, the non-stationary interference source is estimated during the period in which snapshots are taken. Then, a new interference-plus-noise covariance matrix reconstruction is introduced which is derived from a simplified power spectral density function that can be used to shape the directional response of the beamformer. Finally, the beamformer is designed to impose nulls towards the regions of the moving interference based on the reconstructed covariance matrix. The essence of the proposed method is to express the inverse of the reconstructed covariance matrix which needs less computational complexity calculation. The effectiveness of the proposed method is demonstrated by numerical results.