A Decorrelation Constant Modulus Algorithm for Multitarget Separation Using Adaptive Arrays

In this paper, an adaptive digital filter algorithm is presented to optimize the signal-to-noise ratio of an antenna array with multiuser signal inputs. These synchronous communication signals have constant envelope property and are transmitted through a Multiple-Input/Multiple-Output （MIMO） linear channel. This Multiuser Constant Modulus Algorithm （MU-CMA） using an antenna array can compensate for both Interuser Interference （IUI） and Intersymbol Interference （ISI）. The method is derived by minimizing the cost function of the constant modulus signals as well as cross-correlations between them. The simulation result in this paper shows the performance of this algorithm.

Effects of Power Inversion Spatial Only Adaptive Array on GNSS Receiver Measurements

The Spatial Only Processing Power Inversion(SOP-PI) algorithm is frequently used in Global Navigation Satellite System(GNSS) adaptive array receivers for interference mitigation because of its simplicity of implementation. This study investigates the effects of SOP-PI on receiver measurements for high-precision applications. Mathematical deductions show that if an array with a centro-symmetrical geometry is used, ideally,SOP-PI is naturally bias-free; however, this no longer stands when non-ideal factors, including array perturbations and finite-sample effect, are added. Simulations are performed herein to investigate how exactly the array perturbations affect the carrier phase biases, while diagonal loading and forward-backward averaging are proposed to counter the finite-sample effect. In conclusion, whether SOP-PI with a centro-symmetrical array geometry will satisfy the high precision demands mainly depends on the array perturbation degree of the element amplitude and the phase center.

Fractional Processing Based Adaptive Beamforming Algorithm

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing complexity.The goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity lower.The effectiveness of the algorithm will be studied and evaluated in this context.In this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of resources.This algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational complexity.The FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight adaptation.The derivation of the algorithm is provided and supported by mathematical convergence analysis.Performance is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various parameters.The results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter plots.FLMS outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

New multitarget constant modulus array for CDMA systems

A new mulfitarget constant modulus array is proposed for CDMA systems based on least squares constant modulus algorithm. The new algorithm is called pre-despreading decision directed least squares constant modulus algorithm （D-DDLSCMA）. In the new algorithm, the pre-despreading is first applied for multitarget arrays to remove some multiple access signals, then the despreaded signal is processed by the algorithm which united the constant modulus algorithm and decision directed method. Simulation results illustrate the good performance for the proposed algorithm.

Performance Analysis of Anti-Interference Wireless Packet Networks for LEO Micro-Satellite with Adaptive Nulling Antenna Array

Information integrity is key to successful operations in intricacy environments in the future, especially when strong interferences exist. This paper presents the design of a novel wireless packet network receiver system for Low Earth Orbit （LEO） micro-satellites with adaptive nulling antenna arrays. It uses three types of interference suppression in cascade： namely spread spectrum, adaptive array nulling, and transform domain filtering. This paper proposes a pilot channel-aided method in order to make full advantage of this arrangement, and analyzes its throughput and delay performance using the Markov chain model. Our results show that this method can achieve excellent delay and throughput performance： When the number of array antenna is 8, its throughput increase relative to the standard Slot-ALOHA protocol is 125 96.

Improved Space-Time Selective Interference Cancellation Scheme for CDMA Systems

Space-time selective parallel interference cancellation(ST-SPIC) is a computationally effective approach combining multiuser detection (MUD) with antenna array technology for CDMA systems. The exploitation of signal reliability is a key issue in ST-SPIC. In order to improve the reliability estimation, a pair of reliability thresholds are introduced. Then an improved selective interference cancellation algorithm is proposed to exploit the reliability accordingly. More practical space-time processing algorithms are also incorporated in the proposed ST-SPIC scheme to overcome the limitation caused by some idealised assumptions taken in the original ST-SPIC scheme. Numerical results show that the proposed ST-SPIC scheme outperforms its traditional counterpart in a CDMA microcell environment.

Two-Dimensional Direction Finding via Sequential Sparse Representations

The problem of two-dimensional direction finding is approached by using a multi-layer Lshaped array. The proposed method is based on two sequential sparse representations,fulfilling respectively the estimation of elevation angles,and azimuth angles. For the estimation of elevation angles,the weighted sub-array smoothing technique for perfect data decorrelation is used to produce a covariance vector suitable for exact sparse representation,related only to the elevation angles. The estimates of elevation angles are then obtained by sparse restoration associated with this elevation angle dependent covariance vector. The estimates of elevation angles are further incorporated with weighted sub-array smoothing to yield a second covariance vector for precise sparse representation related to both elevation angles,and azimuth angles. The estimates of azimuth angles,automatically paired with the estimates of elevation angles,are finally obtained by sparse restoration associated with this latter elevation-azimuth angle related covariance vector. Simulation results are included to illustrate the performance of the proposed method.

Coordinated adaptive beamformer over distributed antenna network

The spatial diversity of distributed network demands the individual filter to accommodate the topology of interference environment. In this paper, a type of distributed adaptive beamformer is proposed to mitigate interference over coordinated antenna arrays network. The proposed approach is formulated as generalized sidelobe canceller （GSC） structure to facilitate the convex combination of neighboring nodes＇ weights, and then it is solved by unconstrained least mean square （LMS） algorithm due to simplicity. Numerical results show that the robustness and convergence rate of antenna arrays network can be significantly improved in strong interference scenario. And they also clearly illustrate that mixing vector is optimized adaptively and adjusted according to the spatial diversity of the distributed nodes which are placed in different power of received signals to interference ratio （SIR） environments.