Adaptive Array Processing Research Articles

Computer‐aided experimental studies for adaptive beamforming algorithms and array processing in engineering education

AbstractSmart antenna and digital antenna systems are considered as a crucial technology for current and future communication systems supporting 5G/6G and nonterrestrial‐networks communications. Based on the information of the received signal/s, smart antenna systems toward the radiation pattern to the desired direction/s. This technique is known as adaptive beamforming. The theories and the applications of adaptive beamforming are taught in engineering educational courses. This paper proposes a set of real‐time interactive experiments for adaptive beamforming algorithms in antenna array processing subject. The proposed experiments can be performed with the computer‐aided experimental studies tool prepared in the MATLAB® environment. The users can easily make comparisons and can perform trade‐offs between different types of adaptive beamforming algorithms, scenarios, and antenna architecture. This is used as a teaching tool for a graduate course on adaptive beamforming algorithms.

IDBD-Based Beamforming Algorithm for Improving the Performance of Phased Array Radar in Nonstationary Environments

Adaptive array processing technology for a phased array radar is usually based on the assumption of a stationary environment; however, in real-world scenarios, nonstationary interference and noise deteriorate the performance of the traditional gradient descent algorithm, in which the learning rate of the tap weights is fixed, leading to errors in the beam pattern and a reduced output signal-to-noise ratio (SNR). In this paper, we use the incremental delta-bar-delta (IDBD) algorithm, which has been widely used for system identification problems in nonstationary environments, to control the time-varying learning rates of the tap weights. The designed iteration formula for the learning rate ensures that the tap weights adaptively track the Wiener solution. The results of numerical simulations show that in a nonstationary environment, the traditional gradient descent algorithm with a fixed learning rate has a distorted beam pattern and reduced output SNR; however, the IDBD-based beamforming algorithm, in which a secondary control mechanism is used to adaptively update the learning rates, showed a similar beam pattern and output SNR to a traditional beamformer in a Gaussian white noise background; that is, the main beam and null satisfied the pointing constraints, and the optimal output SNR was obtained. Although the proposed algorithm contains a matrix inversion operation, which has considerable computational complexity, this operation could be replaced by the Levinson-Durbin iteration due to the Toeplitz characteristic of the matrix; therefore, the computational complexity could be decreased to O(n), so additional computing resources are not required. Moreover, according to some intuitive interpretations, the reliability and stability of the algorithm are guaranteed.

Array Element Selection Strategies for Interference Suppression in Reconfigurable Tripole Antenna Array Systems

Compared with the scalar array, the polarization-sensitive array (PSA) possesses a more powerful anti-jamming ability in adaptive array processing. However, each antenna of the traditional PSA requires two or more radiofrequency (RF) front-ends, which results in proliferation in computational complexity and even configuration redundancy that has little impact on performance. Besides, the RF front-end is much more expensive than the antenna, making the cost extremely high when considering a large PSA. Therefore, it is essential to search for a PSA reconfiguration approach that utilizes fewer RF front-ends and can generate excellent interference rejection performance. In this work, we devise a new PSA reconfiguration strategy, termed dipole selection, to employ fewer dipoles and RF front-ends to acquire a high signal to interference plus noise ratio (SINR). And the strategy is implemented by controlling the RF switch between the RF front-end and the dipole. The polarization-spatial correlation coefficient (PSCC), characterizing the difference between desired signal and interference in the polarization-spatial domain, is proposed to analyze the effect of PSA reconfiguration on anti-interference performance. Subsequently, the problem of dipole selection is addressed with two proposed methods, named Quadratic-Over-Linear Fraction (QOLF) and Correlation Measurement Elimination (CME), respectively. Numerical and experimental simulation results demonstrate the correctness and effectiveness of the proposed strategy and approaches.

A polarization-sensitive array reconfiguration technique resorting to antenna selection for navigation interference mitigation

Polarization-sensitive array (PSA) processing works well in navigation system applications for interference mitigation. However, the fixed configuration of conventional PSA increases the computational burden of subsequent adaptive array processing and can even be inefficient and redundant in some cases. Furthermore, since the radio frequency (RF) front end is more costly than the antenna, it is urgent to exploit a PSA reconfiguration method that can lower the cost of array design and achieve sufficiently good anti-interference performance when employing fewer RF front ends and antennas. In this paper, a PSA reconfiguration technique maximizing the signal to interference plus noise ratio (SINR) is presented to solve the problem. Combining with adaptive processing, we derive the formula between the SINR and the polarization-spatial correlation coefficient (PSCC), thereby building the mathematical model with minimizing PSCC as the objective function. Then, the lower bound of the optimal PSCC, gained by the Lagrange Dual Relaxation method, is utilized to guide the selection of the antenna number. Finally, we adopt an iterative correlation measurement (ICM) approach to confirm the antenna element position of the optimal subarray with the minimum PSCC. Numerical simulations illustrate the effectiveness and correctness of the proposed technique and derived theoretical results.

Adaptive Array Processing based Wireless Energy Transmission for IoT Applications

Rechargeable energy sources are essential for the extreme deployment of Internet-of-Things (IoT) sensors with the massive growth in smart systems. In order to meet these requirements, wireless energy transmission (WET) provides demand based power to the sensors. Temporary energy storage is done using supercapacitors. This overcomes the drawback of release of hazardous wastes released by IoT connected disposables after their working life. WET is made possible through adaptive array processing. The system consists of a transmitting side with multiple antennas and a receiving side with a programmable energy harvester. Several far-field adaptive processing schemes such as conventional beamformers, multiple sidelobe canceller (MSLC), multiple beam antenna system, regenerative hybrid array, digital beamformer, and generalized sidelobe canceller are tested and compared with the proposed modified beamforming model for superior performance. As the number of antennas increases, the gain increases. Gain and cumulative distribution function are analyzed over multiple distances for multiple iterations. The received signal strength indicator (RSSI) is also estimated to validate the performance of the proposed model.

High Precision Pseudo-Range Measurement in GNSS Anti-Jamming Antenna Array Processing

Radio frequency interference has become a rising problem to the signal of the Global Navigation Satellite System (GNSS). An effective way to achieve anti-jamming is by using an antenna array in GNSS signal processing. However, antenna array processing will cause a decline in the accuracy of pseudo-range measurements because of the channel mismatch and some other non-ideal factors. To solve this problem, space–time or space–frequency adaptive array processing is widely used for interference cancellation while constraining the delay of each antenna at the same time. In this paper, an anti-jamming algorithm with a time-delay constraint is proposed, where one antenna is chosen as the reference and data from other antennas is corrected based on the signal received from it. The deduction and simulation results show that the proposed algorithm can effectively improve the accuracy of pseudo-range measurements without degradation of anti-jamming performance.

Robust adaptive array processing based on modified multistage Wiener filter algorithm

Multistage Wiener filter (MSWF) is a very efficient algorithm for adaptive array processing because of low-complexity and prominent rank-reduction advantage. However, if training sample data was contaminated by outliers, especially when outliers having the same DOA with target emerge, the MSWF results will be decreased severely. In this paper, MSWFs backward iteration was improved, and median cascaded canceller (MCC) strategy was adopted so that optimal weighting calculation can be obtained via sorting and median processing, meaning impact of outliers were removed effectively. Blocking matrix solving of MSWF forward iteration was completed by Householder transform to enhance fix-point format performance. The new-designed algorithm attained excellent compromise between robustness and complexity. To verify presented algorithm's performance, array with 50 elements was established in simulation platform, and the simulated results also proved it can cope with outlier-contaminated applications effectively.

Robust adaptive array processing based on modified multistage Wiener filter algorithm

Robust adaptive array processing based on modified multistage Wiener filter algorithm

3D-OMP and 3D-FOMP algorithms for DOA estimation

Adaptive antennas and antenna array processing have vital effects on enhancing the performance of wireless communication networks. One of the most significant applications of adaptive antenna systems is the Direction of Arrival (DOA) detection. Several schemes have been proposed in the literature for DOA estimation in two-dimensional space. Among them, Orthogonal Matching Pursuit (OMP) algorithm provides several advantages in comparison to other schemes. The OMP algorithm reduces complexity and improves resolution of detection. Furthermore, in this algorithm, the number of signal emitters is not required to be known. In this paper, we extend the OMP algorithm and propose Three Dimensional OMP (3D-OMP) scheme for DOA estimation in three dimensional space. We provide a redundant dictionary for 3D-OMP scheme by employing azimuth and elevation angles. Simulation results show the high performance of the 3D-OMP algorithm when the signal to noise ratio is higher than −10dB. Moreover, the 3D-OMP algorithm has a high efficiency in detecting multiple signal sources, simultaneously. Then, we evaluate the accuracy of the estimated directions by the 3D-OMP algorithm via comparing the variance of estimation error with the Cramer-Rao bound. Nevertheless, DOA estimation by the 3D-OMP algorithm has a substantial challenge where it cannot distinguish between two adjacent signal sources. To resolve this challenge, we propose Three Dimensional Focused Orthogonal Matching Pursuit (3D-FOMP) algorithm. The 3D-FOMP algorithm is an improved version of the 3D-OMP algorithm. It can detect two adjacent signal sources when the beam former has a single peak corresponding to a direction between right directions. In addition, we show the 3D-OMP and 3D-FOMP algorithms have lower complexities in comparison to the 3D-MUSIC and 3D-ESPRIT schemes when Root Mean Square Error (RMSE)>0.3o. This accuracy is persuasive for most applications. Moreover, simulation results show the capability of the 3D-FOMP algorithm to detect adjacent signal sources and it outperforms the 3D-OMP, 3D-MUSIC and 3D-ESPRIT algorithms for all values of signal to noise ratio.

Noncontact Measurement of the Instantaneous Heart Rate in a Multi-Person Scenario Using <inline-formula> <tex-math notation="LaTeX">${X}$ </tex-math> </inline-formula>-Band Array Radar and Adaptive Array Processing

A noncontact measurement of the heart rate of a specific person using an X -band radar system with a four-element antenna array is presented. The system comprises a 2-D planar wide-beam antenna array with a four-channel network analyzer. The direction of arrival is estimated using the Capon method, and the directionally constrained minimization-of-power algorithm is then applied to the received signals. Signals from the four channels are adaptively combined to enhance the accuracy in estimating the instantaneous heart rate in a multi-person scenario. Measurement results indicate that the proposed system with the adaptive array processing technique is effective for the noncontact measurement of the heart rate of a specific person when there is more than one person in the scene.

High-Dimensional MVDR Beamforming: Optimized Solutions Based on Spiked Random Matrix Models

Minimum variance distortionless response (MVDR) beamforming (or Capon beamforming) is among the most popular adaptive array processing strategies due to its ability to provide noise resilience while nulling out interferers. A practical challenge with this beamformer is that it involves the inverse covariance matrix of the received signals, which must be estimated from data. Under modern high-dimensional applications, it is well known that classical estimators can be severely affected by sampling noise, which compromises beamformer performance. Here, we propose a new approach to MVDR beamforming, which is suited to high-dimensional settings. In particular, by drawing an analogy with the MVDR problem and the so-called “spiked models” in random matrix theory, we propose robust beamforming solutions that are shown to outperform classical approaches (e.g., matched filters and sample matrix inversion techniques), as well as more robust solutions, such as methods based on diagonal loading. The key to our method is the design of an optimized inverse covariance estimator, which applies eigenvalue clipping and shrinkage functions that are tailored to the MVDR application. Our proposed MVDR solution is simple, in closed form, and easy to implement.

FOMP algorithm for Direction of Arrival estimation

Adaptive antennas and antenna array processing are much significant issues for improving the performance of wireless communication systems. One of the most important applications of adaptive antenna systems is the Direction of Arrival (DOA) estimation. Recently, compressive sensing algorithms, including convex relaxation methods and greedy algorithms, have been recognized as a type of novel DOA estimation methods. The Orthogonal Matching Pursuit (OMP) is an example of compressive sensing methods. Using the OMP method for DOA estimation has many advantages in comparison to other algorithms. In spite of these advantages, the DOA estimation by OMP algorithm has a substantial challenge. The OMP algorithm cannot distinguish between two adjacent signal sources. In the DOA estimation by OMP algorithm, when there are two adjacent sources, the mutual coherent condition of the compressive sensing methods is violated. The situation gets worse when there are two sources from two adjacent DOAs. In this situation, the beam former has a single peak. In this paper, we propose the Focused Orthogonal Matching Pursuit (FOMP) algorithm for estimation of DOA. The FOMP algorithm is an improved version of the OMP algorithm. It can detect two signal sources exactly when they are very close and beam former has a single peak corresponding to a direction between right directions. Simulation results demonstrate the advantages of the proposed scheme. It can be observed the FOMP algorithm could detect very close signal sources with a negligible error.

Hermitian matrix inversion algorithm

The work objective is speeding the covariance matrix converter of the adaptive antenna array interference by reducing the number of operations performed. A problem of developing an aprior information inversion algorithm relying on the Hermitian nature of the reversible matrix is considered. The proposed algorithm is based on a bordering method in contrast to the well-known algorithms based on method of Gaussian-Jordan elimination. Because of complexity and a large operation number, Gaussian-Jordan method does not allow realizing the real time signal processing in computing systems of the adaptive antenna arrays that are widely used in communication, radiolocation, and radio navigation systems. The proposed algorithm extends a well-known bordering method by taking into account Hermitian nature of the covariance interference matrix, and allows developing an algorithm based on the recursive relations. An obtained gain in amount of calculation is no less than 25% comparing to the method of Gaussian-Jordan elimination. The calculation amount decrease and a more simple form of relations used for the matrix inversion algorithm elaboration allow developing a more simple design of the adaptive antenna array processor for the matrix inversion.

Applications of an adaptive back-propagation method for passive acoustic localizations of marine mammal sounds

An adaptive back-propagation localization method utilizing the dispersion relation of the acoustic modes of low-frequency sound signals is reviewed in this talk. This method employs an adaptive array processing technique (the maximum a posteriori mode filter) to extract the acoustic modes of sound signals, and it is capable of separating signals from noisy data. The concept of the localization algorithm is to back-propagate modes to a location where the modes align with each other. Gauss-Markov inverse theory is applied to make the normal mode back-propagator adaptive to the signal-to-noise ratio (SNR). When the SNR is high, the localization procedure will push the algorithm to achieve high resolution. On the other hand, when the SNR is low, the procedure will try to retain its robustness and reduce the noise effects. Examples will be shown in the talk to demonstrate the localization performance with comparisons to other methods. Applications to baleen whale sounds collected in Cape Cod Bay, Massachusetts, will also be presented. Lastly, population density estimation using this passive acoustic localization method will be discussed.

Explicit Recursive and Adaptive Filtering in Reproducing Kernel Hilbert Spaces

This brief presents a methodology to develop recursive filters in reproducing kernel Hilbert spaces. Unlike previous approaches that exploit the kernel trick on filtered and then mapped samples, we explicitly define the model recursivity in the Hilbert space. For that, we exploit some properties of functional analysis and recursive computation of dot products without the need of preimaging or a training dataset. We illustrate the feasibility of the methodology in the particular case of the γ-filter, which is an infinite impulse response filter with controlled stability and memory depth. Different algorithmic formulations emerge from the signal model. Experiments in chaotic and electroencephalographic time series prediction, complex nonlinear system identification, and adaptive antenna array processing demonstrate the potential of the approach for scenarios where recursivity and nonlinearity have to be readily combined.

Analysis of the Performance of Suppressing Interference of Double Stages Wiener Filter Based on Simulation Experiments

Measured data of hydrophone array could be interfered by underwater noise,so its essential to enhance the performance of suppressing interference for adaptive array processing. How to improve the performance of Double Stages Wiener Filter was discussed based on configuration principles. The influences of interference angle, array unit number and interference-to-noise ratio (INR) to suppressing interference have been analyzed through principle analysis and simulation experiments under different circumstances, including single or multiple disturb signals. Experiments show that the performance of suppressing interference depends on interference angle and INR more and array unit sum a little.

Research on the Performance of Suppressing Interference Using Double Stages Wiener Filter in Underwater Noise Measurement

Measured data of hydrophone array could be interfered by underwater noise,so its essential to enhance the performance of suppressing interference for adaptive array processing. How to improve the performance of Double Stages Wiener Filter was discussed based on configuration principles. The influences of interference angle, array unit number and interference-to-noise ratio (INR) to suppressing interference have been analyzed through principle analysis and simulation experiments under different circumstances, including single or multiple disturb signals. Experiments show that the performance of suppressing interference depends on interference angle and INR more and array unit sum a little.

Correspondence - Beam-domain eigenspace-based minimum variance beamformer for medical ultrasound imaging

The eigenspace-based minimum variance (ESBMV) beamformer can provide good imaging resolution and contrast; however, the performance is achieved at the cost of high computational complexity. In adaptive array processing, the beamspace method is an efficient way to lower the computational complexity. In this paper, we combine the beamspace method with the ESBMV beamformer and propose a beamdomain ESBMV beamformer. We demonstrate the feasibility of introducing the beamspace into the ESBMV beamformer and propose an effective method of forming the transform matrix based on the spatial spectrum of the array signals. We also illustrate the performance of the proposed beamformer when resolving point scatterers and a cyst phantom with both simulated and experimental data. The results show that the proposed method can achieve performance comparable to the ESBMV beamformer within much shorter time.

Efficient Robust Broadband Antenna Array Processor in the Presence of Look Direction Errors

The performance of adaptive antenna array processors is known to decline severely in the presence of array imperfections and uncertainties. Hence the robustness of an array processor against uncertainties is an important issue. Diagonal loading of the correlation matrix is a popular technique applied to the standard Frost processor to increase robustness of the array system. This paper presents a number of robust beamforming algorithms using efficient realization techniques. The techniques do not use a presteered array and allow an array designer to specify the frequency response of the processor in the look direction and steer the main beam of specified frequency response in an arbitrary direction. The proposed techniques are computationally efficient and applicable in environments with strong mutual coupling. Simulation results show the effectiveness of the proposed beamforming algorithms and compare the performance of the beamformer using the proposed techniques with that of existing methods.

Design and Implementation of Real-Time Software Radio for Anti-Interference GPS/WAAS Sensors

Adaptive antenna array processing is widely known to provide significant anti-interference capabilities within a Global Navigation Satellite Systems (GNSS) receiver. A main challenge in the quest for such receiver architecture has always been the computational/processing requirements. Even more demanding would be to try and incorporate the flexibility of the Software-Defined Radio (SDR) design philosophy in such an implementation. This paper documents a feasible approach to a real-time SDR implementation of a beam-steered GNSS receiver and validates its performance. This research implements a real-time software receiver on a widely-available x86-based multi-core microprocessor to process four-element antenna array data streams sampled with 16-bit resolution. The software receiver is capable of 12 channels all-in-view Controlled Reception Pattern Antenna (CRPA) array processing capable of rejecting multiple interferers. Single Instruction Multiple Data (SIMD) instructions assembly coding and multithreaded programming, the key to such an implementation to reduce computational complexity, are fully documented within the paper. In conventional antenna array systems, receivers use the geometry of antennas and cable lengths known in advance. The documented CRPA implementation is architected to operate without extensive set-up and pre-calibration and leverages Space-Time Adaptive Processing (STAP) to provide adaptation in both the frequency and space domains. The validation component of the paper demonstrates that the developed software receiver operates in real time with live Global Positioning System (GPS) and Wide Area Augmentation System (WAAS) L1 C/A code signal. Further, interference rejection capabilities of the implementation are also demonstrated using multiple synthetic interferers which are added to the live data stream.