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.

The uniform white noise assumption is one of the basic assumptions in most of the existing direction-of-arrival (DOA) estimation methods. In many applications, however, the nonuniform white noise model is more adequate. Then, the noise variances at different sensors have to be also estimated as nuisance parameters while estimating DOAs. In this letter, different from the existing iterative methods that address the problem of nonuniform noise, a non-iterative two-phase subspace-based DOA estimation method is proposed. The first phase of the method is based on estimating the noise subspace via eigendecomposition (ED) of some properly designed matrix and it avoids estimating the noise covariance matrix. In the second phase, the results achieved in the first phase are used to estimate the noise covariance matrix, followed by estimating the noise subspace via generalized ED. Since the proposed method estimates DOAs in a non-iterative manner, it is computationally more efficient and has no convergence issues as compared to the existing methods. Simulation results demonstrate better performance of the proposed method as compared to other existing state-of-the-art methods.

In some applications, the signals received by an array are a mixture of signals emitted by both far‐field and near‐field sources. This study develops a new cumulant‐based multiple signal classification (MUSIC) algorithm for source localisation using a new structural sparse array for scenarios where both far‐field and near‐field sources coexist. The key feature of this algorithm is that it utilises fourth‐order cumulants to compute the virtual covariance matrix and constructs a new special cumulant matrix to acquire the largest number of virtual sensors and the largest array aperture for a given number of sensors. The authors provide a geometric proof to justify the utilisation of the proposed sparse linear array and compute the effective aperture of the array. The proposed algorithm increases resolution ability, direction of arrival (DOA) and range estimation accuracy, and the number of sources to be localised. Moreover, the new method has the main advantage that it does not use the information of all sensors; so that it provides somewhat low computational complexity while it uses many actual and virtual sensors. Monte Carlo simulations are provided to demonstrate the effectiveness of the proposed method.

Soft robots have recently demonstrated impressive abilities to adapt to objects and their environment with limited sensing and actuation. However, mobile soft robots are typically fabricated using laborious molding processes that result in limited actuated degrees of freedom and hence limited locomotion capabilities. In this paper, we present a 3D printed robot with bellowed soft legs capable of rotation about two axes. This allows our robot to navigate rough terrain that previously posed a significant challenge to soft robots. We present models and FEM simulations for the soft leg modules and predict the robot locomotion capabilities. We use finite element analysis to simulate the actuation characteristics of these modules. We then compared the analytical and computational results to experimental results with a tethered prototype. The experimental soft robot is capable of lifting its legs 5.3 cm off the ground and is able to walk at speeds up to 20 mm/s (0.13 bl/s). This work represents a practical approach to the design and fabrication of functional mobile soft robots.

Detection of unknown signals is considered under complex elliptically symmetric (CES) distributions, a wide family that includes several well-known distributions as special cases. We study the detection problem for unknown deterministic signals and random CES signals, both corrupted by CES noise. For detection of unknown deterministic signals, we form the generalized likelihood ratio test and reduce it to a sufficient test statistic, which is either the norm of the received vector or a function of it. Performance analysis is provided under both known and estimated noise scatter matrix. For detection of random CES signals, we form the likelihood ratio test, provide performance analysis, and establish conditions under which the sufficient test statistic is given by the norm of the received vector. Interestingly, this norm test statistic is proven to be the uniformly most powerful test for the detection of all CES distributed signals.

With 5G communication systems on the horizon, efficient interference management in heterogeneous multicell networks is more vital than ever. This paper investigates the linear precoder design for downlink multicell multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, where base stations (BSs) coordinate to reduce the interference across space and frequency. In order to minimize the overall feedback overhead in next-generation systems, we consider precoding schemes that require statistical channel state information (CSI) only. We apply the random matrix theory to approximate the ergodic weighted sum rate of the system with a closed form expression. After formulating the approximation for general channels, we reduce the results to a more compact form using the Kronecker channel model for which several multicarrier concepts such as frequency selectivity, channel tap correlations, and intercarrier interference (ICI) are rigorously represented. We find the local optimal solution for the maximization of the approximate rate using a gradient method that requires only the covariance structure of the MIMO-OFDM channels. Within this covariance structure are the channel tap correlations and ICI information, both of which are taken into consideration in the precoder design. Simulation results show that the rate approximation is very accurate even for very small MIMO-OFDM systems and the proposed method converges rapidly to a near-optimal solution that competes with networked MIMO and precoders based on instantaneous full CSI.

Having many benefits, phase‐mode transformation technique is used frequently in beamforming with circular arrays. Owig to the fact that this transformation is frequency dependent, some techniques are used to make this transformation appropriate for broadband applications. When this technique is used with uniform circular arrays, its performance degrades because of the approximations applied to the formulations. In this study, the authors propose a new beamforming method not only robust against uncertainty in the steering vector of the desired signal, but also robust against errors caused by the phase‐mode transformation. The authors assume that steering vector of the desired signal pertains to a ball set and ellipsoid sets are considered to cover the transformation matrix columns. The proposed beamforming method is based on semi‐definite programming. The performance of the proposed beamforming method is compared with several other beamforming techniques by using a simulation study.

The modified transpose Jacobian (MTJ) algorithm is a recently proposed algorithm used in manipulator control. Based on an approximated feedback linearization approach the MTJ does not need to a priori knowledge of the plant dynamics. In this paper, this scheme is extended to the complicated control problem of underactuated robots in Cartesian space. Based on the notion of transpose effective Jacobian, this new proposed algorithm is called modified transpose effective Jacobian (MTEJ) algorithm. The MTEJ control law employs stored data of the control command in the previous time step, as a learning tool to yield an improved performance, with no substantial need to a priori knowledge of the plant dynamics. Therefore, unlike a model-based algorithm, it is almost not affected by inaccuracies in mass properties. The gains of the proposed MTEJ can be selected more systematically, and do not need to be large, hence the noise rejection characteristics of the algorithm are improved. Obtained results show an improved performance compared to that of the transpose effective Jacobian and even model-based algorithms.

In this paper, we present a direction of arrival (DOA) tracking scheme involving a subspace tracking algorithm and a Kalman filter. The proposed subspace tracking algorithm is based on an interpretation of the signal subspace as the solution of a minimization of a constrained projection approximation task. We show that we can apply the matrix inversion lemma to solve this problem recursively. The proposed algorithm avoids the orthonormalization process after each update for post-processing algorithms which need orthonormal basis of the signal subspace. The DOA's are obtained via a Newton-type method initialized with the DOA's predicted by the Kalman filter. The tracking capability of the proposed algorithm is verified by computer simulations in a scenario involving targets with crossing trajectories.