Masakiyo Suzuki

A Novel AWSF Algorithm for DOA Estimation in Virtual MIMO Systems

Both Multiple Input and Multiple Output (MIMO) and Smart Antenna (SA) have been widely accepted as promising schemes to improve the spectrum efficiency and coverage of mobile communication systems. This paper addresses the issue of Direction of Arrival (DOA) estimation in Virtual MIMO (VMIMO) systems which adopt SA simultaneously. First of all, we propose a VMIMO scheme for DOA estimation in which a set of User Equipments (UEs) are grouped together to simultaneously communicate with the Base Station (BS) on a given Resource Block (RB). In this scheme, the BS has multiple antennas and can estimate DOA of each UE in the group simultaneously. However, in practical environment because of reflection and refraction of signals, the received signals may be coherent. It is desirable that the DOA estimation in VMIMO could provide high resolution when the number of independent signals is unknown, which can not be achieved by existing algorithms. In order to solve this problem, we propose an Automatic Weighted Subspace Fitting (AWSF) algorithm that can detect the number of independent signals automatically and show accurate DOA estimation. Then with consideration of computational cost and sampling cost while using the AWSF algorithm, we propose a utility factor to determine the optimal number of UEs in VMIMO scheme, i.e., the order of VMIMO. Finally, the performance of the proposed AWSF algorithm and the efficiency of the utility factor criterion are demonstrated by simulations.

A Novel Modification of PSO Algorithm for SML Estimation of DOA

This paper addresses the issue of reducing the computational complexity of Stochastic Maximum Likelihood (SML) estimation of Direction-of-Arrival (DOA). The SML algorithm is well-known for its high accuracy of DOA estimation in sensor array signal processing. However, its computational complexity is very high because the estimation of SML criteria is a multi-dimensional non-linear optimization problem. As a result, it is hard to apply the SML algorithm to real systems. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient method for multi-dimensional non-linear optimization problems in DOA estimation. However, the conventional PSO algorithm suffers two defects, namely, too many particles and too many iteration times. Therefore, the computational complexity of SML estimation using conventional PSO algorithm is still a little high. To overcome these two defects and to reduce computational complexity further, this paper proposes a novel modification of the conventional PSO algorithm for SML estimation and we call it Joint-PSO algorithm. The core idea of the modification lies in that it uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. Since this initialization space is already close to the solution of SML, fewer particles and fewer iteration times are needed. As a result, the computational complexity can be greatly reduced. In simulation, we compare the proposed algorithm with the conventional PSO algorithm, the classic Altering Minimization (AM) algorithm and Genetic algorithm (GA). Simulation results show that our proposed algorithm is one of the most efficient solving algorithms and it shows great potential for the application of SML in real systems.

PSO algorithm for exact Stochastic ML estimation of DOA for incoherent signals

The performance of Stochastic ML (SML) algorithm of Direction-of-Arrival (DOA) is much more superior to many other algorithms in array signal processing. However, the estimation of SML is a non-linear multi-dimensional optimization problem. Therefore its computational complexity is very high. In this paper, firstly we show exact definition of SML estimation of DOA for incoherent signals and brief description of the conventional solving method, Alternating Minimization (AM) algorithm. Then, we propose to use the Particle Swarm Optimization (PSO) algorithm to solve the estimation of SML. Also in this paper, we propose a method to optimize the inertia factor of PSO. Simulation results show that the computational complexity of the proposed PSO algorithm for SML estimation is much lower than that of the conventional AM algorithm.

A low computational complexity SML estimation algorithm of DOA for wireless sensor networks

We address the problem of DOA estimation in positioning of nodes in wireless sensor networks. The Stochastic Maximum Likelihood (SML) algorithm is adopted in this paper. The SML algorithm is well-known for its high resolution of DOA estimation. However, its computational complexity is very high because multidimensional nonlinear optimization problem is usually involved. To reduce the computational complexity of SML estimation, we do the following work. (1) We point out the problems of conventional SML criterion and explain why and how these problems happen. (2) A local AM search method is proposed which could be used to find the local solution near/around the initial value. (3) We propose an algorithm which uses the local AM search method together with the estimation of DML or MUSIC as initial value to find the solution of SML. Simulation results are shown to demonstrate the effectiveness and efficiency of the proposed algorithms. In particular, the algorithm which uses the local AM method and estimation of MUSIC as initial value has much higher resolution and comparable computational complexity to MUSIC.

An algorithm for finding the best solution of stochastic ML estimation of DOA

This paper addresses the issue of uniqueness of Stochastic or unconditional Maximum Likelihood (SML) estimation for direction-of-arrival (DOA) finding. The SML estimation is not unique inherently in the noise-free case unlike the Deterministic or conditional ML (DML) estimation of DOA. Since also in the noisy case, there is no guarantee that the SML estimation is unique, global search techniques fail to find DOA. However, the one closest to the DML estimate among the several global solutions can be considered to be the most adequate solution for DOA. This paper proposes an algorithm which uses a local search together with the DML estimation as initialization to find the best solution of the SML estimation. Finally some simulation results are shown to demonstrate the proposed algorithm is effective.

Practical band estimation for periodic superlattices by using semi-infinite periodic model

In this paper, we propose a practical band estimation method for periodic superlattices by using semi-infinite periodic model, which is located in the middle of finite periodic model and infinite periodic model. According to the model proposed, we can estimate not only band structures but also ripples in passbands for periodic superlattices with simple calculation by applying image parameters in circuit theory. This model may be useful for energy filter designing, since the ripples in passbands are the essential information related with energy filter abilities.

Efficient algorithms for optimal and suboptimal unconditional ML estimation of DOA

This paper presents efficient algorithms for optimal and suboptimal Unconditional Maximum Likelihood (UML) directions-of-arrival (DOA) finding. In the conventional UML formulation an important condition is missing. That is the non-negative definiteness of the covariance matrix of signal components. Because of the lack of the important condition, inadequate global solution appears in the solution space and global search fails to find adequate solution. Although the exact UML formulation solves this problem, it requires huge computational load because of eigenvalues required in each step of searching DOA. According to the investigation on the local soutions of the previous UML estimation, the exact solution is found in the local solutions in the case of good estimation condition, such as large snapshots and high SNR. This leads to the fact that local search for the previous UML criterion has a good chance to find the exact solution UML estimation. Although no exact solution could not be found in the local solutions of the previous UML estimation in the threshold region, such as small snapshots or low SNR, the local search has a chance to find suboptimum solutions of the exact UML estimation. This paper proposes two kind of efficient algorithms for the conventional UML to find the optimal or exact solutions and suboptimal solutions for exact UML estimation of DOA.

A JPSO algorithm for SML estimation of DOA

The estimation of DOA is an important problem in sensor array signal processing and its industrial applications. Among all the solving techniques for DOA, the Stochastic Maximum Likelihood (SML) algorithm is well-known for its high accuracy of DOA estimation. However, its computational complexity is very high because a multi-dimensional nonlinear optimization problem is involved. The Particle Swarm Optimization (PSO) algorithm is considered as a rather efficient way for multi-dimensional non-linear optimization problems in DOA estimation. However Conventional PSO algorithm usually needs a large number of particles and the iteration number is also a litter high when all the particles converge. As a result, the computational complexity is still a litter high. This paper proposes a low complexity Joint-PSO (JPSO) algorithm for SML estimation. It uses the solution of Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) and stochastic Cramer-Rao bound (CRB) to determine a novel initialization space. In this case, smaller number of particles and less iteration number are required. Therefore, the computational complexity can be greatly reduced. Simulation results are also shown to demonstrate the validity of proposed JPSO algorithm.

Efficient AM Algorithms for Stochastic ML Estimation of DOA

The estimation of direction-of-arrival (DOA) of signals is a basic and important problem in sensor array signal processing. To solve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML) is one of the most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the multidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses the issue of reducing computational complexity of SML estimation of DOA based on the Alternating Minimization (AM) algorithm. We have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an efficient AM (EAM) algorithm by dividing the SML criterion into two components. One depends on a single variable parameter while the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion (IAM) using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of SML estimation greatly, while IAM is the best. Another advantage of IAM is that this algorithm can avoid the numerical instability problem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value.

A novel modification of WSF for DOA estimation

This paper addresses the most basic and crucial problem in smart antenna, i.e., the estimation of DOA (Direction-of-Arrival) finding. The performance of smart antenna system greatly depends on the resolution of DOA. MUSIC (MUiltiple SIgnal Classification) and ESPRIT (Estimation of Signal Paramter via Rotational Invariance Technique) are the most classic two algorithms for DOA finding in real systems. However, these two algorithms cannot handle coherent signals directly which happens for example in multipath propagation and the performance will be greatly deteriorated if the pre-processing technique such as spatial smoothing is used. Therefore, the system employing these two algorithms usually works in the condition of Line-of-Sight (LOS), e.g., in suburb circumstance. WSF (Weighted Subspace Fitting) algorithm is a more superior technique which has much higher resolution and can handle coherent signals without any pre-processing. However, conventional WSF needs to know the independent number of signals, otherwise its performance will be deteriorated. In this paper, we propose a modified WSF algorithm for DOA. The proposed modified WSF can detect the independent number of signals automatically and show much higher resolution compared to conventional WSF and MUSIC.