This paper considers linear precoder optimization problems for multiple-input multiple-output (MIMO) systems. In addition to the conventionally used sum-power constraint, maximum eigenvalue constraint on the precoding matrix is also considered so as to account for power limitations imposed on each antenna by the linearity of its own power amplifier in practical implementations. A framework employing directional derivative is developed to obtain optimal precoder designs for different criteria including maximizing the information rate and minimizing the sum of mean-square error (MSE). It turns out that power allocations in such situations are piecewise linear in sum-power space. The piecewise linear property allows us to generate the entire path of solution through finding out a finite number of breakpoints. A Homotopy-type algorithm is then proposed to obtain the solution for an arbitrary sum-power constraint. The number of breakpoints to be determined in our exact piecewise linear solution is in fact only about two times of the number of transmit antennas, so that our method is super fast and outperforms existing approximate solutions in the literature in both effectiveness and efficiency. Simulated experiments are performed to verify our theoretical analysis.

Intelligent reflecting surface (IRS) has emerged as a promising technology for improving the spectrum and energy efficiency of next-generation wireless communications. Accurately acquiring channel state information (CSI) of the IRS-assisted wireless system is an essential task for reaping the passive beamforming gain of IRS. However, it is quite difficult to directly obtain the CSI of the IRS-assisted wireless system due to the inability of signal processing at the IRS. In this paper, we investigate the downlink channel estimation problem for IRS-assisted massive MIMO systems. We first present a new sparse recovery problem formulation for the cascaded downlink channel estimation, which exploits the sparsity of massive MIMO channels at the base station (BS) side and adopts the sub-surface idea to significantly reduce computational complexity and training overhead. In this case, the corresponding sparse signal recovery problem exhibits a row-sparse feature but is additionally affected by a coupling matrix. It is challenging to recover the row-sparse matrix with the traditional sparse representation methods, as the elements in the row-sparse matrix are highly coupled with each other. To meet the challenge, we employ a hybrid approximate message passing (AMP) framework for the cascaded downlink channel estimation, which consists of a part of expectation propagation approximation (EPA) and a part of two-level generalized approximate message passing (GAMP). We illustrate that combining EPA and GAMP into a hybrid framework can make up for their respective shortcomings. Numerical simulation results verify the superiority of the proposed method.

Compound bearing fault diagnosis is an essentially challenging task due to the mutual interference among multiple fault components. The state-of-the-art methods usually take the potential fault characteristic frequencies as the prior knowledge and then try to recover every fault component by exploiting the impulse signal sparsity. However, they inevitably suffer from algorithmic degradation caused by energy leakage, $l_{1}$ -norm approximation, and/or improper parameter selection. To handle these shortcomings, in this article, we propose a novel sparse Bayesian learning (SBL)-based method for the compound bearing fault diagnosis. We first present a new categorical probabilistic model to efficiently capture the truly-occurred fault components with a truncated feasible domain, which can greatly reduce the energy leakage effect. Then, we devise a more general SBL framework to recover the compound sparse impulse signal under the new categorical probabilistic model. The newly proposed method successfully avoids the $l_{1}$ -norm approximation and manual parameter selection; thus, it can yield much higher accuracy and robustness. Both simulations and experiments demonstrate the superiority of the developed method.

The performance of sparse signal recovery (SSR) can be enhanced by exploiting rich temporal correlation in the multiple snapshots of signal of interest. However, existing methods need to transform the temporally correlated multiple measurements SSR problem into its vectorization form, imposing huge computational cost for algorithmic realization. To overcome this drawback, we propose a novel formulation to model the temporal correlation so that variational Bayesian inference (VBI) can be applied to simplify the inference and a novel uncoupling trick is also proposed to reduce the computation. Theoretical and simulation results indicate that our method can bring a considerable computational complexity reduction and achieve a performance improvement for the temporally correlated SSR problem compared to the state of arts time-varying sparse Bayesian learning (TSBL) method.

Investigating the model ship dynamic positioning system by simulating the actual sea conditions in the laboratory can not only avoid the risks caused by the directly experiments on a true ship, but also reduce the costs. With the purpose of realizing the high accuracy control of the dynamic positioning, besides a high accuracy mathematical model of the ship, an important condition is that the position information provided by the position detection system must be accurate, reliable, and continuous. The global positioning system (GPS) signal is restricted when the model ship dynamic positioning system is set indoors. This paper describes a novel scheme for ship target tracking based on the multi-sensor data fusion techniques. To improve the accuracy of indoor positioning and ship target tracking, the characteristics of many sensors are systematically analyzed, such as radar, difference GPS, and ultrasonic sensors. Other important factors, including the indoor temperature, position, and environment, are also taken into account to further optimize the performance. Combining the Kalman filter method, the time alignment method, the coordinate transformation method, and the optimal fusion criterion method, the core algorithm of our framework employs the track correlation as the performance index of the optimal fusion. The experimental results indicate that our method outperforms the methods based on a single ultrasonic sensor. The maximum error between the estimated location and the real location is only 1.32 cm, which meets the standard for engineering applications.