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Pattern synthesis of the sparse linear array (SLA) has played an important role when the antenna size is extremely limited. Although grid‐based compressed sensing (CS) algorithms have been widely utilised to synthesise the SLA, the performance is greatly affected by the grid mismatch problem. To solve the problem, a reweighted gridless CS (RGCS) algorithm based on the reweighted atomic norm minimisation and the rotational invariance propagator method is introduced. In the RGCS algorithm, the number of antenna elements can be efficiently reduced through the reweighted gridless convex optimisation, which utilises a reweighted strategy to break the limit of the atomic norm and improves the performance of the SLA. More importantly, in addition to the focussed‐beam pattern, the proposed algorithm can also synthesise the SLA with the asymmetric beam pattern. Numerical experiments show that the RGCS algorithm can save about 18.75%–46% array elements for the uniform linear array.

This paper focuses on the method of an adaptive detector which works against Gaussian background with unknown parameters, such as covariance matrix. The authors design a tunable detector for point‐like targets by mixing the Kelly's generalized likelihood ratio test and the enhanced Rao test. The analytical expression of the false alarm probability and detection probability of the proposed detector are derived. The performances of the new scheme are assessed, and analysed in comparison with its natural counterparts. The results show that it can provide enhanced rejection capabilities of mismatched signals, at the price of a limited detection loss for matched signals.

For the high-speed and light autonomous underwater vehicle (AUV), it is moving freely, and the speed and attitude are in dynamic movement. Then, the traditional static and quasi-static ground alignment cannot be used any more. Using the optimisation-based alignment (OBA) scheme, this study presents a novel estimation method for fast initial coarse alignment of a AUV's strapdown inertial navigation system (SINS). The novel OBA method use the inertial measurement unit, the pressure sensor measurements and the navigational velocity ν v in v-frame produced by the motor thrust to solve the attitude estimation problem which is known as Wahba's problem. Unlike several current techniques, the presented estimation method is effective to any movement form. The simulation results show that the attitude determined by this novel method can meet the accuracy requirement of coarse alignment and it can be used as input for the fine alignment.

Direction of arrival (DOA) estimation is a basic and important problem in signal processing and has been widely applied. Its research has been advanced by the recently developed methods based on Bayesian compressive sensing (BCS). Among these methods, the ones combined with an off-grid (OG) model have been proved to be more accurate than the on-grid ones. However, the conventional BCS-based methods have a disadvantage of the slow speed. In this study, a high-efficiency iterative algorithm, based on the fast relevance vector machine and the OG model, is developed. This new approach applies to both the single- and multiple-snapshot cases. Numerical simulations show that the proposed method estimates DOAs more accurately than the l 1 -penalisation method and computes more efficiently than the conventional BCS-based methods. Finally, comparisons with state-of-the-art methods and Cramer-Rao bound are also reported.

In this study, the authors deal with the problem of parametric detection for relatively small targets using space–time adaptive processing (STAP). In contrast to the existing parametric STAP detectors, the proposed detectors perform range estimation by exploiting the spillover of the target energy between consecutive samples. To this end, the authors assume that the received useful signal is known up to a complex unknown deterministic factor parameter and the disturbance signal is modelled as a multichannel autoregressive Gaussian process. Moreover, the authors assume that a set of secondary data is available which are free of signal components, but have the same unknown parameters as the disturbance in the cells under test. Using these assumptions, the so-called simplified generalised likelihood ratio test (GLRT) and the two-step GLRT are derived and assessed. It is worth noting that the simplified GLRT is based on an asymptotic ML estimate of the amplitude, which leads to a simple and closed-form detection statistic. The performance assessment, conducted resorting to both simulated dataset and KASSPER dataset, has shown that the proposed decision schemes can provide accurate estimates of the target position within the cell under test and ensure enhanced detection performance compared with their natural competitors.

In this study, the authors deal with the problem of adaptive detection of point-like targets in Gaussian disturbance with unknown but persymmetric structured covariance matrix induced by the space and/or time symmetry of the sensing system. In this framework, they devise and assess two selective receivers exploiting the Rao test and the generalised likelihood ratio test design criteria. The performance assessment, conducted by Monte Carlo simulation, has shown that the proposed receivers can significantly outperform their unstructured counterparts and guarantee enhanced rejection performance of unwanted signals with respect to their natural competitors.

This study deals with the problem of detecting distributed targets in the presence of homogeneous and partially homogeneous Gaussian disturbance with unknown covariance matrix. The proposed detectors improve the adaptive beamformer orthogonal rejection test (ABORT) idea to address detection of distributed targets, which makes it possible to decide whether some observations contain a useful target or a signal belonging to the orthogonal complement of the useful subspace. At the design stage, the authors resort to either the plain generalised likelihood ratio test (GLRT) or ad hoc design procedures. Remarkably, the considered criteria lead to receivers ensuring the constant false alarm rate (CFAR) property with respect to the unknown quantities. Moreover, authors’ derivations show that the ad hoc detector for a partially homogeneous environment coincides with the generalised adaptive subspace detector. The performance assessment conducted by Monte Carlo simulation has confirmed the effectiveness of the newly proposed detection algorithms.

Synthetic aperture sequential beamforming (SASB) is a two-stage delay-and-sum beamforming procedure, which can be applied to B-mode imaging with an implementation of low complexity compared to the traditional synthetic transmit aperture imaging. A new SASB imaging scheme in order to compensate the effects of axial motion is proposed. The velocity and direction of the motion can be evaluated by cross-correlation between specific beams according to adjacent focus of the SASB. The performance assessment, conducted by using simulated data, reveals that the proposed method can correct the positions of tissue motion without extra firings.

For multi-target tracking (MTT) in the presence of clutters, both issues of state estimation and data association are crucial. This study tackles them jointly by Sequential Monte Carlo methods, a.k.a. particle filters. A number of novel particle algorithms are devised. The first one, which we term Monte-Carlo data association (MCDA), is a direct extension of the classical sequential importance resampling (SIR) algorithm. The second one is called maximum predictive particle filter (MPPF), in which the measurement combination with the maximum predictive likelihood is used to update the estimate of the multi-target's posterior. The third, called proportionally weighting particle filter (PWPF), weights all feasible measurement combinations according to their predictive likelihoods, and uses them proportionally in the importance sampling framework. We demonstrate the efficiency and superiority of our methods over conventional approaches through simulations.