Jinkuan Wang

Robust adaptive beamforming algorithm based on neural network

Adaptive beamforming is known to have resolution and interference rejection capability when the array steering vector is precisely known. However, the performance of adaptive beamforming techniques may degrade severely in the presence of mismatches between the assumed array response and the true array response. In this paper, we propose a novel neural network approach to robust adaptive beamforming. The proposed algorithm is based on explicit modeling of uncertainty in the desired signal array response and a three-layer radial basis function neural network (RBFNN), which belongs to the class of diagonal loading approaches. In the proposed algorithm, the computation of the optimum weight vector is viewed as a mapping problem, which can be modeled using a RBFNN trained with input/output pairs. The proposed algorithm has nearly optimal performance under good conditions, provides the robustness against the signal steering vector mismatches and makes the mean output array SINR consistently close to the optimal one. Simulation results validate substantial performance improvements relative to other adaptive beamforming methods.

Space-time matrix method for 2-D direction-of-arrival estimation

This paper presents a novel space-time matrix method for two-dimensional direction-of-arrival estimation of multiple narrow-band sources impinging on the far field of a planar array. Using the proposed method, both the elevation angle and the azimuth angle of a source can be estimated by the same eigenvector of a space-time matrix, or the elevation angle is estimated by the eigenvector, but the azimuth angle is estimated by the corresponding eigenvalue. At the same time, the paring of the estimated elevation angles and azimuth angles is automatically determined. Compared with the previous works, the proposed method can provide better performance with substantially reduced computational complexity since the associated eigenvalue decompositions are for smaller-sized data matrices. In addition, it can resolve the incoming rays with very close azimuth angles or very close elevation angles.

Target tracking using WSN based on multiagent coordination method

Aiming at the problems that WSN faces, we proposed a coordination method for target tracking based on multiagent in this paper. The sensor field is divided dynamically into several parts in order to perform distributed tracking. Sensors try to form dynamic coalition in each field for fulfilling tasks. When a target move to the sensing filed, the sensor nodes begin to forming coalition and then negotiations come on. Utilizing multiagent technology can make good use of each sensor nodespsila limited energy and perform tasks coordinately.

Target tracking based on multiagent and game theory in wireless sensor network

A new method for target tracking based on multiagent and game theory is proposed in this paper. When a target occur in the sensing field of wireless sensor network, the sensor nodes begin to forming coalition dynamically and then they start to negotiate with game theory. New coalition is formed to track it with the target moving. Utilizing multiagent method and game theory in wireless sensor network enables nodes to perform tasks coordinately to achieve some desired objectives.

Blind multiuser detection using the subspace-based linearly constrained LSCMA

The least squares constant modulus algorithm (LSCMA) is a popular constant modulus algorithm (CMA) because of its global convergence and stability. But the performance will degrade when it is affected by the problem of interference capture in the MC-CDMA system that has several constant modulus signals. In order to overcome this shortage, a linearly constrained LSCMA multiuser detection algorithm is proposed by using the spreading code of the desired user to impose linear constraint on the LSCMA. To further enhance the performance, we project the weight vector obtained by the proposed linearly constrained LSCMA algorithm onto the signal subspace and propose a subspace-based linearly constrained LSCMA multiuser detection algorithm. The proposed algorithm ensures the algorithm convergence to the desired user and suppresses the noise subspace in the weight vector. Thus the performance of the system is improved. Moreover, to reduce the computational complexity, an improved projection approximation subspace tracking with deflation (PASTd) algorithm is proposed for adaptive signal subspace estimation. The simulation results demonstrate that the proposed algorithm achieves better output signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER) performance than the traditional LSCMA algorithm, linearly constrained LSCMA algorithm and subspace-based MMSE algorithm.

Space-alternating generalised expectation-maximisation-based h-infinity channel estimator for multiple-input multiple-output-orthogonal frequency division multiplexing systems

This study presents a low-complexity and robust H-infinity channel estimator for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. The H-infinity estimator, which has never been devoted to MIMO-OFDM systems, could be implemented by applying a multiple-order auto-regression (AR) model. However, it may increase the design complexity of receivers and lead to poor real-time property when this model is used for MIMO-OFDM systems, which makes the authors abandon AR model. In order to reduce the number of matrices manipulations because of the each received OFDM symbols from different transmit antennas, the iterative space-alternating generalised expectation–maximisation (SAGE) algorithm is adopted. Furthermore, to deal with the effect of non-Gaussian noise (NGN) channels, because of various natural or man-made impulsive sources, an equivalent signal model (ESM) is introduced to alleviate the effect of this issue and enhance the robust of SAGE-based H-infinity estimator. Simulation results show that H-infinity estimator has almost the same bit error rate performance as optimal maximum a posteriori estimator. The performance gain afforded by using ESM can be substantial when compared with using the traditional signal model, which dramatically enhance the robustness of SAGE-based H-infinity estimator against NGN channels.

Time resources allocation in cognitive radar system

This paper considers the problem of simultaneously detecting and tracking multiple targets. The main problem discussed here is the time allocation of cognitive radars in a multitarget environment. Radars are used to detect, to locate and to identify target. The radar performs three main functions: search, tracing and weapon engagement. Each of functions occupies amount of time. It is a key point that to allocate radar time ressource effectively. For cognitive radar, it should have the basic function which is learning. The radar needs to manage its resources dynamically and interactively between the setting of radar parameters to optimize the tasks the setting of radar parameters to optimize the tasks to be carried out and perceive environment highlights the role in which knowledge and intelligence will be central in cognitive radar performance. In this paper, we develop the optimization criterion based on the detection probabilities.

A New Waveform Design Method for Cognitive Radar

In cognitive radar system, how the transmitted waveform adapts in response to information regarding the radar environment is an important problem. In this paper, the waveform design for cognitive radar is viewed as an optimization problem. Then a new waveform design method for cognitive radar is proposed, which uses IPM (interior-point method) to carry out the optimization task. The simulation results demonstrate the validity of our algorithm.

Effective antenna selection in MIMO systems under spatial correlated fading

Multiple input multiple output (MIMO) systems are considered as potential candidates for future high data rates wireless networks. However these systems present a practical problem which is the cost of hardware related to every additional antenna. In this paper, we present an effective suboptimal antenna algorithm based on frame error rate for MIMO systems. It only needs a sorted QR decomposition compared with the optimal antenna selection algorithm which requires an exhaustive search of the pseudo inverse of the channel matrix. The new algorithm achieves good performance that is very close to the optimal algorithm, decreases the computational complexity greatly, and improves the performance of the receivers. Simulation results demonstrate that the proposed antenna selection algorithm achieves excellent performance under i.i.d. channel, also the correlated.

Joint DOA-delay estimation based on space-time matrix method in wireless channel

This paper presents a novel space-time matrix method to jointly estimate the directions of arrival (DOAs) and delays of multipath propagation signals in wireless communications. Using the proposed approach, the DOAs and delays are estimated by the eigenvalues and by eigenvectors of the space-time matrix, respectively. The pairing of the estimated DOAs and delays is automatically determined by the relationship between eigenvalues and eigenvectors. Compared with the previous works, the space-time method can provide better performance with substantially reduced computational complexity. The simulation results show the effectiveness of the proposed algorithm.