Enhanced DOA estimation algorithms using MVDR and MUSIC

In this paper, the direction of arrival (DOA) and adaptive antenna array beamforming for the smart antenna applications are realized by using Minimum Variance Distortionless Response (MVDR) algorithm of DOA estimation. The MVDR supported by joint approximate diagonalization of eigenmatrices (JADE) algorithm is used to estimate the direction of arrivals of coherent signals in non-coherent groups. The generalized steering vectors using JADE algorithm are first estimated, and then the spectrum methods are realized to estimate the DOA of each signal. Root mean square error (RMSE) values are computed in different scenarios for the proposed method as well as JADE-MUSIC and the corresponding results are compared. Although both methods resolved the signals correctly, simulation results show that JADE based MVDR has better performances in the cases of low number of antenna elements and snapshots. The DOAs obtained are then processed using LMS adaptive beamforming algorithm to steer the main lobe of the radiation pattern to desired angles (signals) and the nulls to undesired angles (signals). The simulation results also reveal that adaptive beamforming is successfully done by annihilating the effects of undesired signals significantly.

This paper presents a brief overview of narrowband direction of arrival (DOA) estimation algorithms and techniques. A comprehensive study is carried out in this paper to investigate and evaluate the performance of variety of algorithms for DOA estimation. Two categories of DOA estimation algorithms are considered for discussion which are Classical methods and Subspace based techniques. Classical methods include Sum-and-Delay method and Capon’s Minimum Variance Distortionless Response (MVDR) while Subspace based techniques are multiple signal classification (MUSIC) and The Minimum Norm Technique. Also ESPIRIT technique is evaluated. Inefficiencies are pointed out and solutions are suggested to overcome these shortfalls. Simulation results shows that the MUSIC algorithm is able to better represent the DOAs of signals with more prominent peaks. The Min-Norm algorithm also identifies the DOAs of signals similar to the MUSIC algorithm, but produces spurious peaks at other locations. The MVDR method identifies the DOAs of signals, but the locations are not represented by sharp peaks, due to spectral leakage. The classical beamformer also produces several spurious peaks. MUSIC show higher accuracy and resolution than the other algorithms. It should be noted that MUSIC is more applicable because it can be used for different array geometries.

In signal processing applications smart antenna arrays are used to locate sources of various signals and for beamforming. Various algorithms have been developed to determine or estimate the Direction Of Arrival (DOA). Direction Of Arrival estimation is utilized in a number of applications, including radar, navigation, communication, etc. Beamforming is a technique used for directive signal transmission and reception. In a smart antenna system antenna arrays adjusts the main lobe towards the desire user and generates null in the direction of interfering users to provide suppression. Objective of this paper is to analyze the performance of Multiple Signal Classification (MUSIC) algorithm a subspace method to estimate DOA and compare with classical Bartlett method of DOA. Beamforming algorithms like LMS and NVSSLMS are studied,analyzed and its performance parameters compared.

Minimum variance distortionless response (MVDR) algorithm has broken through the Rayleigh limit restrictions and makes higher resolutions possible. However, they are easily affected by complex underwater acoustic environments and are not sufficiently robust, so it is difficult to use them in real underwater systems. Time reversal mirror technology is a new method of localization, which may be effectively applied to target detection and localization. To improve its robustness, the MVDR algorithm is combined with time reversal mirroring for passive detection and location. This method makes full use of underwater multipath channel information and reduces the effects of complex underwater environments. The performance of time-reversal MVDR that is based on fixed diagonal loading and robust capon beamforming (RCB) was also studied. Simulations, water tank experiment and sea trial proved it is feasible and practical Keywords: MVDR; time reversal mirror; robust capon beamforming; passive location

Third-Generation based Wide Band Code Division Multiple Access (W-CDMA) networks have been characterized for high data rate services accessible to a large number of users. But multipath and co-channel interference puts a limit on system capacity, throughput and new multimedia information services like wireless internet or video streaming. Smart antenna technique that includes Direction of Arrival (DOA) approach and Adaptive Beam forming algorithm is a promising means to improve the over-all system capacity and to overcome limited spectrum problem by effectively reducing the multipath and co-channel interference. In this paper, DOA estimation is done by Multiple Signal Classification (MUSIC) algorithm and Minimum Variance Distortion less Response (MVDR) algorithm is used for adaptive beam forming that continuously adjusts the weight of antenna arrays for creating a beam to track the desired user automatically, and to place null towards the interference user. Here, we improve on the MVDR method in order to assign the weightings. Instead of using a single linear equality constraint, a variable constraint has been used to obtain the optimum weights that decrease the gain in the undesired direction and desired direction comes out with more gain and without distortion. Obtained results are analyzed for the conventional method and the proposed method by observing beam forming spectrum by first varying number of antenna elements and then by varying the spacing between elements. For both the cases, the performance is better for the proposed method in terms of lower noise floor, more relative power and sharper peaks in the desired user direction than conventional method.

Multi-input and multi-output (MIMO) radar systems possess numerous advantages, such as scanning an entire region much faster than a phased array, enhancing the spatial resolution, mitigating interference and multipath fading, and improving the probability of detection of targets. MIMO radar systems use adaptive beamforming techniques such as the minimum variance distortionless response (MVDR) to obtain the best possible estimation of the direction of arrival (DOA) of the targets. The MVDR algorithm has been thoroughly investigated for traditional phased array radars. For MIMO radar systems, however, it requires significant signal processing, which can introduce substantial latency, especially in the case of large MIMO systems where few hundred antennas are used; this can be solved by using a Graphics Processor Unit (GPU), which contains thousands of cores and can execute many operations in parallel. This paper presents a MATLAB-based approach for GPU parallelization of the minimum variance distortionless (MVDR) beamforming algorithm in a MIMO radar system. Two MIMO radar systems are considered. The first one is a simulated MIMO radar which is used for automotive adaptive cruise control (ACC), and the second one is an experimental monostatic MIMO radar that is based on a vector network analyzer (VNA). It is shown that the GPU achieved a speedup of up to 7 times while successfully detecting all targets.

We note that the cost function of the MUSIC (multiple signal classification) algorithm is quadratic in an array vector and that it can be expressed in a least squares form. Based on this observation, we present a rigorous Levenberg-Marquardt (LM) formulation of the MUSIC algorithm for simultaneous estimation of an azimuth and an elevation. We show a convergence property and compare the performance of the LM-based MUSIC algorithm with that of the standard MUSIC algorithm via Monte-Carlo simulation. We also compare the performance of the MUSIC algorithm with that of the Capon algorithm both for the standard implementation and for the LM-based implementation.

60GHz wireless communication technology is considered to be one of the most promising technology in future wireless communication domains. The information of the DOA (direction of arrival) is the foundation for adaptive array antenna to adjust the optimal beam direction as well as track mobile terminal, and the method to obtain the precise DOA is the key technology of the adaptive beam forming. The STMV (Steered Minimum Variance) algorithm is a kind of high resolution wideband adaptive beam forming method, which takes advantages of the STCM (Steered Covariance Matrix) to estimate the adaptive beam forming weight vector so as to obtain DOA information. In this paper, we attempt to bring STMV algorithm into DOA estimation for 60GHz signals. It is proved practicable to apply STMV algorithm in 60GHz DOA estimation via MTALAB simulation. Comparing with the MVDR (Minimum Variance Distortionless Response) algorithm, the STMV algorithm shows excellent performance that it needs less fast beats in the required resolution, also has smaller variance.

Uniform linear array (ULA) geometry does not perform well for direction of arrival (DOA) estimation at directions close to the array endfires. Shirvani and Akbari solved this problem by displacing two elements from both ends of the ULA to the top and/or bottom of the array axis. Shirvani-Akbari array (SAA) presents a considerable improvement in the DOA estimation of narrowband sources arriving at endfire directions in terms of DOA estimation accuracy and angular resolution. In this paper, all new proposed SAA configurations are modelled and also examined, numerically. In this paper, two well-known DOA estimation algorithms, multiple signal classification (MUSIC) and minimum variance distortionless response (MVDR), are used to evaluate the effectiveness of proposed arrays using total root mean square error (RMSE) criterion. In addition, two new scenarios are proposed which divide angular search to two parts, directions close to array endfires as well as middle angles. For middle angles, which belong to (−70∘≤𝜃≤70∘), ULA is considered, and for endfire angles, the angles which belong to (−90∘≤𝜃≤−70∘) and (70∘≤𝜃≤90∘), SAA is considered. Simulation results of new proposed scenarios for DOA estimation of narrowband signals show the better performance with lower computational load.

Detecting direction of arrival (DoA) using array antenna is considered as common challenge in different typeS of applications, such as sonar, radar, wireless communications. DoA estimation plays an important role in direction finding applications. Further, effective DoA estimation technique is the primary requirements for smart antenna system to realize beam forming technique. Multiple signal classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance (ESPRIT) algorithms have been widely studied, and they are considered as fundamental for the majority of the DoA methods in literature. In this paper, the principal of DoA estimations is presented. The main focus of the current paper is on the classical methods (Bartletts and MVDR), MUSIC and ESPRIT algorithms. Review of various efforts that have been made to enhance the performance of these algorithms was acheived. Then compared their performances using simulation results. Further, applying MUSIC algorithm to estimate the frequency of the multiple incident signals. The results show that MUSIC algorithm has higher resolution compared to other presented techniques. It is also shown that the accuracy of ESPRIT method is mainly rely on the number of antenna elements. Finally, the principle of Eigenvalues-based method was proposed. It is shown that this method has good resolution with very low complexity as it does not need decomposition of covariance matrix.

In this paper, we formulate and experimentally demonstrate direction of arrival (DoA) estimation with a two-port composite right/left handed (CRLH) leaky-wave antenna (LWA) using the multiple signal classification (MUSIC) algorithm. The CRLH LWA consists of a cascade of metamaterial unit cells, periodically modulated using varactor diodes. By changing the voltages across series and shunt varactors, the antenna is able to uniformly steer its radiation pattern. The proposed modified MUSIC algorithm, uses both antenna's input ports to estimate the DoA. Conventionally, the MUSIC algorithm needs to calculate the correlation matrix from the output vector of an array of antenna elements. In this work, we show in detail how the correlation matrix can be computed when using leaky-wave antennas. The algorithm is demonstrated using LWA in an anechoic chamber and the estimated DoA results are in good agreement with the predicted angles.

Joint estimation of azimuth and elevation angles is of great importance in source localization and channel characterization. Firstly, some basic knowledge of three typical parametric estimation algorithms are introduced in this paper, i.e., multiple signal classification (MUSIC), Unitary estimation of signal parameter via rotational invariance technique (ESPRIT), and space-alternating generalized expectation-maximization (SAGE) algorithms. Each algorithm is capable of extracting both azimuth angle of arrival (AAoA) and elevation angle of arrival (EAoA) of multi-paths jointly. It is pointed out that the SAGE and MUSIC algorithms have higher complexity than the Unitary ESPRIT algorithm due to the iteration/angle searching procedure. Secondly, impacts of antenna number, closely spaced paths, and signal-to-noise ratio (SNR) on estimation performance of three algorithms are analyzed. Results show that the Unitary ESPRIT algorithm has lower accuracy in comparison with the MUSIC and SAGE algorithms when antenna number and SNR are large. Finally, three algorithms are applied to estimate multipath parameters in 16 GHz massive MIMO channel measurements. It is shown that the Unitary ESPRIT algorithm performs less satisfactory in MPCs extraction, while MUSIC can provide comparable results with the SAGE algorithm.

gold-MUSIC based DOA estimation using ULA antenna of DS-CDMA sources with propagation delay diversity

Multiple-input-multiple-output (MIMO) radar is well-known for providing high-resolution direction-of-arrival (DOA) estimation by forming a large-scaled sum coarray utilizing waveform diversity. However, the sacrifice is that a large number of snapshots are required to estimate the sample covariance matrix. When the number of training snapshots is limited, the performance of subspace-based DOA estimation method, such as multiple signal classification (MUSIC), deteriorates due to the distortion of noise subspace. In order to improve the accuracy of DOA estimation using MIMO radar in the case of few snapshots, we propose a method to refine the covariance matrix iteratively. The sampled covariance matrix is iteratively refined by subtracting cross-correlation terms using generalized inner product based on the previous DOA estimates. Finally, the MUSIC algorithm is implemented based on the refined sample covariance matrix to update the DOA estimates until achieving termination condition. Simulation results demonstrate that the additional covariance matrix refinement step enhances the accuracy of DOA estimation using MIMO radar in the case of limited snapshots significantly.

A multiple signal classification (MUSIC)-based approach known as the time-space-time MUSIC (TST-MUSIC) is proposed to jointly estimate the directions of arrival (DOAs) and the propagation delays of a wireless multiray channel. The MUSIC algorithm for the DOA estimation is referred to as the spatial-MUSIC (S-MUSIC) algorithm. On the other hand, the temporal-MUSIC (T-MUSIC), which estimates the propagation delays, is introduced as well. Making use of the space-time characteristics of the multiray channel, the proposed algorithm-in a tree structure-combines the techniques of temporal filtering and of spatial beamforming with three one-dimensional (1-D) MUSIC algorithms, i.e., one S-MUSIC and two T-MUSIC algorithms. The incoming rays are thus grouped, isolated, and estimated. At the same time, the pairing of the estimated DOAs and delays is automatically determined. Furthermore, the proposed approach can resolve the incoming rays with very close DOAs or delays, and the number of antennas required by the TST-MUSIC algorithm can be made less than that of the incoming rays.