Showing papers on "Adaptive beamformer published in 2017"

A Robust and Efficient Algorithm for Coprime Array Adaptive Beamforming

Abstract: Coprime array offers a larger array aperture than uniform linear array with the same number of physical sensors, and has a better spatial resolution with increased degrees of freedom However, when it comes to the problem of adaptive beamforming, the existing adaptive beamforming algorithms designed for the general array cannot take full advantage of coprime feature offered by the coprime array In this paper, we propose a novel coprime array adaptive beamforming algorithm, where both robustness and efficiency are well balanced Specifically, we first decompose the coprime array into a pair of sparse uniform linear subarrays and process their received signals separately According to the property of coprime integers, the direction-of-arrival (DOA) can be uniquely estimated for each source by matching the super-resolution spatial spectra of the pair of sparse uniform linear subarrays Further, a joint covariance matrix optimization problem is formulated to estimate the power of each source The estimated DOAs and their corresponding power are utilized to reconstruct the interference-plus-noise covariance matrix and estimate the signal steering vector Theoretical analyses are presented in terms of robustness and efficiency, and simulation results demonstrate the effectiveness of the proposed coprime array adaptive beamforming algorithm

Multi-Resolution Codebook and Adaptive Beamforming Sequence Design for Millimeter Wave Beam Alignment

Abstract: Millimeter wave (mm-wave) communication is expected to be widely deployed in fifth generation (5G) wireless networks due to the substantial bandwidth available for licensed and unlicensed use at mm-wave frequencies. To overcome the higher path loss observed at mm-wave bands, most prior work focused on the design of directional beamforming using analog and/or hybrid beamforming techniques in large-scale multiple-input multiple-output systems. Obtaining potential gains from highly directional beamforming in practical systems hinges on sufficient levels of channel estimation accuracy, where the problem of channel estimation becomes more challenging due to the substantial training overhead needed to sound all directions using a high-resolution narrow beam. In this paper, we consider the design of multi-resolution beamforming sequences to enable the system to quickly search out the dominant channel direction for single-path channels. The resulting design generates a multilevel beamforming sequence that strikes a balance between minimizing the training overhead and maximizing beamforming gain, where a subset of multilevel beamforming vectors is chosen adaptively to maximize the average data rate within a constrained time. We propose an efficient method to design a hierarchical multi-resolution codebook utilizing a Butler matrix, i.e., a generalized discrete Fourier transform matrix. Numerical results show the effectiveness of the proposed algorithm.

Beamnet: End-to-end training of a beamformer-supported multi-channel ASR system

Abstract: This paper presents an end-to-end training approach for a beamformer-supported multi-channel ASR system. A neural network which estimates masks for a statistically optimum beamformer is jointly trained with a network for acoustic modeling. To update its parameters, we propagate the gradients from the acoustic model all the way through feature extraction and the complex valued beamforming operation. Besides avoiding a mismatch between the front-end and the back-end, this approach also eliminates the need for stereo data, i.e., the parallel availability of clean and noisy versions of the signals. Instead, it can be trained with real noisy multi-channel data only. Also, relying on the signal statistics for beamforming, the approach makes no assumptions on the configuration of the microphone array. We further observe a performance gain through joint training in terms of word error rate in an evaluation of the system on the CHiME 4 dataset.

Far-Field RF Wireless Power Transfer with Blind Adaptive Beamforming for Internet of Things Devices

Abstract: Wireless power transfer (WPT) has long been one of the main goals of Nikola Tesla, the forefather of electromagnetic applications. In this paper, we investigate radio-frequency beamforming in the radiative far field for WPT. First, an analytical model of the channel fading is presented, and a blind adaptive beamforming algorithm is adapted to the WPT context. The algorithm is computationally light, because we need not explicitly estimate the channel state information. Then, a testbed with a multiple-antenna software-defined radio configuration on the transmitting side and a programmable energy harvester on the receiving side is developed in order to validate the algorithm in this specific power application. From the results, it can be seen that the implementation of this version of beamforming indeed improves the harvested power. Specifically, at various distances from 50 cm to 1.5 m, the algorithm converges with two, three, and four antennas with an increasing gain as we increase the number of antennas. These encouraging results could have far-reaching consequences in providing wireless power to Internet of Things devices, our target application.

Deep long short-term memory adaptive beamforming networks for multichannel robust speech recognition

Abstract: Far-field speech recognition in noisy and reverberant conditions remains a challenging problem despite recent deep learning breakthroughs. This problem is commonly addressed by acquiring a speech signal from multiple microphones and performing beamforming over them. In this paper, we propose to use a recurrent neural network with long short-term memory (LSTM) architecture to adaptively estimate real-time beamforming filter coefficients to cope with non-stationary environmental noise and dynamic nature of source and microphones positions which results in a set of timevarying room impulse responses. The LSTM adaptive beamformer is jointly trained with a deep LSTM acoustic model to predict senone labels. Further, we use hidden units in the deep LSTM acoustic model to assist in predicting the beamforming filter coefficients. The proposed system achieves 7.97% absolute gain over baseline systems with no beamforming on CHiME-3 real evaluation set.

Robust Adaptive Beamforming for Fast-Moving Target Detection With FDA-STAP Radar

Abstract: Frequency diverse array (FDA), which employs a small frequency increment across the array elements, is able to resolve range ambiguity. However, the frequency diversity results in angle-Doppler-defocusing of target especially at a high speed in space-time adaptive processing (STAP) radar, thus, causing serious detection performance degradation. In this paper, a robust adaptive beamforming approach is proposed for the FDA-STAP radar to enhance fast-moving target detection performance. In our solution, a large feasible region is employed to include the true steering vector of target. To avoid the trivial solution, an angle-Doppler-defocusing steering vector constraint is devised and incorporated into the large feasible region. The problem is formulated as a nonconvex quadratically constrained quadratic program which is efficiently solved via semidefinite relaxation technique. Because the retrieved steering vector of target is close to the true one, the performance is significantly improved. It is demonstrated via computer simulations that the proposed algorithm is superior to the state-of-the-art methods, which includes maintaining the mainlobe of the beampattern and improving the signal-to-clutter-plus-noise ratio performance.

Magnetic MIMO Signal Processing and Optimization for Wireless Power Transfer

Abstract: In magnetic resonant coupling (MRC) enabled multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems, multiple transmitters (TXs) are used to enhance the efficiency of simultaneous power transfer to multiple receivers (RXs) by constructively combining their induced magnetic fields, a technique termed “magnetic beamforming”. In this paper, we study the optimal magnetic beamforming design in a multiuser MIMO MRC-WPT system. We introduce and characterize the multiuser power region, which constitutes all the achievable power tuples for all RXs, subject to the given total power constraint over all TXs as well as their individual peak voltage and current constraints. For the special case without TX peak voltage and current constraints, we derive the optimal TX current allocation for the single-RX setup in closed-form and that for the multi-RX setup by applying the techniques of semidefinite relaxation (SDR) and time-sharing. In general, the problem is a nonconvex quadratically constrained quadratic programming (QCQP), which is difficult to solve. For the case of one single RX, we show that the SDR of the problem is tight and thus solve the problem efficiently. For the general case with multiple RXs, based on SDR we obtain two approximate solutions by applying the techniques of time-sharing and randomization, respectively. Moreover, we propose a new method to estimate the magnetic MIMO channel between TXs and RXs for practical implementation of magnetic beamforming. Numerical results show that our proposed magnetic channel estimation and adaptive beamforming schemes are practically effective and can significantly improve the power transfer efficiency and multiuser performance tradeoff in MIMO MRC-WPT systems compared with the benchmark scheme of uncoordinated WPT with fixed identical TXs' current.

Multichannel end-to-end speech recognition

Abstract: The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology. Using an attention mechanism in a recurrent encoder-decoder architecture solves the dynamic time alignment problem, allowing joint end-to-end training of the acoustic and language modeling components. In this paper we extend the end-to-end framework to encompass microphone array signal processing for noise suppression and speech enhancement within the acoustic encoding network. This allows the beamforming components to be optimized jointly within the recognition architecture to improve the end-to-end speech recognition objective. Experiments on the noisy speech benchmarks (CHiME-4 and AMI) show that our multichannel end-to-end system outperformed the attention-based baseline with input from a conventional adaptive beamformer.

Multichannel End-to-end Speech Recognition

Abstract: The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology. Using an attention mechanism in a recurrent encoder-decoder architecture solves the dynamic time alignment problem, allowing joint end-to-end training of the acoustic and language modeling components. In this paper we extend the end-to-end framework to encompass microphone array signal processing for noise suppression and speech enhancement within the acoustic encoding network. This allows the beamforming components to be optimized jointly within the recognition architecture to improve the end-to-end speech recognition objective. Experiments on the noisy speech benchmarks (CHiME-4 and AMI) show that our multichannel end-to-end system outperformed the attention-based baseline with input from a conventional adaptive beamformer.

Robust Adaptive Beamforming With Precise Main Beam Control

Abstract: Many efforts have been recently devoted to robust adaptive beamforming with main beam control such that sufficient robustness against large look direction mismatches can be achieved by flexibly adjusting the beamwidth and response ripple. However, most of the existing approaches inherently rely on assuming an exactly known array manifold, but have not yet addressed the issue of robust beamforming with precise main beam control in the presence of arbitrary steering vector uncertainties. This motivates us to develop a robust beamforming approach that is capable of accurately controlling the array main beam. Unlike the conventional methods, steering vector uncertainties are taken into account in the magnitude response constraints of the adaptive beamformer. This allows us to control the main beam as prescribed. As the resultant nonconvex problem has a more complicated formulation than that of the existing methods, techniques developed for solving the problem of robust beamforming with magnitude response constraints cannot be employed directly. To tackle this problem, the lower and upper norm bounds of the beamformer weight vector are derived. The semidefinite relaxation technique is then employed as approximate solver, ending up with iterative, grid search, and linearization solutions. Simulation results show that the proposed methods are able to precisely control the main beam magnitude response in the presence of steering vector uncertainties.

Performance Analysis of Time-Modulated Arrays for the Angle Diversity Reception of Digital Linear Modulated Signals

Abstract: Diversity occurs whenever several copies of the same transmitted signal arrive at a receiver. Such a situation allows for improving the performance of a wireless communication system transmitting over a radio channel without increasing the transmit power. Previous works have shown that time-modulated arrays are capable of faithfully acquiring digital signals while exploiting angular diversity through the adaptive beamforming of their harmonic patterns. In this work, we take a step further and consider a wireless communication system employing digital linear modulated signals and an innovative receiver that includes a time-modulated array and a maximum ratio combining subsystem. The maximum ratio combiner is adapted to optimally exploit the multipath channel angular diversity. The performance of the system is analyzed in terms of two metrics: the signal-to-noise ratio and the symbol error rate. The results are compared to those achieved with other receivers that include conventional antenna arrays, exhibiting the time-modulated array solution a good tradeoff between performance and hardware complexity.

Smart underground antenna arrays: A soil moisture adaptive beamforming approach

Abstract: Current wireless underground (UG) communication techniques are limited by their achievable distance. In this paper, a novel framework for underground beamforming using adaptive antenna arrays is presented to extend communication distances for practical applications. Based on the analysis of propagation in wireless underground channel, a theoretical model is developed which uses soil moisture information to improve wireless underground communications performance. Array element in soil is analyzed empirically and impacts of soil type and soil moisture on return loss (RL) and resonant frequency are investigated. Accordingly, beam patterns are analyzed to communicate with underground and above ground devices. Depending on the incident angle, refraction from soil-air interface has adverse effects in the UG communications. It is shown that beam steering improves UG communications by providing a high-gain lateral wave. To this end, the angle, which enhances lateral wave, is shown to be a function of dielectric properties of the soil, soil moisture, and soil texture. Evaluations show that this critical angle varies from 0° to 16° and decreases with soil moisture. Accordingly, a soil moisture adaptive beamforming (SMABF) algorithm is developed for planar array structures and evaluated with different optimization approaches to improve UG communication performance.

Exhaustive, Iterative and Hybrid Initial Access Techniques in mmWave Communications

Abstract: In millimeter wave (mmWave) communications with possible gigabit-per-second data rate, the severe path loss can effectively be alleviated by adaptive beamforming using antenna arrays. However, this complicates the initial access (IA), by which a mobile user equipment (UE) establishes a physical link connection with a mmWave base station (BS). The total duration of IA directional search can be very long since multiple preambles should be repeatedly transmitted for all transmit and receive beam pairs. In this work, we focus on IA techniques based on analog beamforming with uniform planar array antennas without context information from microwave BSs. We revisit current techniques of exhaustive search and iterative search based on BS training, and propose a novel hybrid training method, where in the first stage BS performs wide beam search, and in the second stage, the UE will perform reverse training according to the best wide beam decided in the first stage. For the three IA techniques, we derive the total consumed time slots and the received SNR expressions in each stage. Through simulations and comparisons, we demonstrate that the proposed hybrid training method shortens the searching delay and gives the same access error probability as iterative search.

Robust Adaptive Beamforming Against Mutual Coupling Based on Mutual Coupling Coefficients Estimation

Abstract: In an adaptive beamforming system, the mutual coupling effects among the array elements can seriously degrade the system performance. In this paper, we propose a robust adaptive beamforming algorithm using a uniform linear array (ULA) to mitigate the mutual coupling effects. The proposed algorithm is based on the fact that the mutual coupling matrix (MCM) of a ULA can be approximated as a banded symmetric Toeplitz matrix as the mutual coupling between two sensors is inversely related to their separation and is negligible for a few wavelengths away. By exploiting the structural characteristics of the MCM, a subspace-based method is used to estimate the mutual coupling coefficients of the ULA that yield a closed-form solution, and the MCM is further constructed. Then, the constructed MCM and array received data are used to reconstruct the interference-plus-noise covariance (INC) matrix. Finally, the robust adaptive beamformer is created through using the Capon principle and the reconstructed INC matrix. Unlike most of the existing algorithms, the proposed algorithm only requires prior knowledge of the array geometry. Simulation results indicate that our approach outperforms the compared algorithms in the presence of unknown mutual coupling and can achieve a performance close to the theoretical optimal value.

Sparse Array Quiescent Beamformer Design Combining Adaptive and Deterministic Constraints

Abstract: In this paper, we examine sparse array quiescent beamforming for multiple sources in interference-free environment. To maximize the output signal-to-noise ratio (SNR), the beamformer design comprises two intertwined stages, the determination of beamforming weights and the reconfiguration of array structure. The SNR maximization may produce high sidelobe levels, making the receiver vulnerable to interferences. We consider the problem of achieving maximum SNR beamforming subject to specified quiescent pattern constraints and, as such, combine both adaptive and deterministic approaches for sparse array configurations. We employ two convex relaxation methods and an iterative linear fractional programming algorithm to solve the nonconvex antenna selection problem for sparse array beamformers. Simulation examples demonstrate that the array configuration plays a vital role in determining the beamforming performance in interference-free scenarios.

Coprime array adaptive beamforming with enhanced degrees-of-freedom capability

Abstract: In this paper, we propose a novel coprime array adaptive beamforming algorithm based on virtual array spatial spectrum estimation to enhance the degrees-of-freedom (DOFs) capability. Specifically, the coprime array received signals are derived to virtual domain, where the spectrum estimation is performed on an equivalent virtual uniform linear array. Since the virtual array contains more virtual sensors than physical sensors, the DOFs capability is effectively enhanced. Meanwhile, the large array aperture offered by coprime array provides a higher resolution than uniform linear array. With the estimated sources' directions and interferences' power, the coprime array adaptive beamformer is designed by reconstructing the interference-plus-noise covariance matrix and desired signal steering vector. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.

Probabilistic spatial dictionary based online adaptive beamforming for meeting recognition in noisy and reverberant environments

Abstract: Here we propose online adaptive beamforming for automatic speech recognition (ASR) in meetings in noisy, reverberant environments. The proposed method is based on recently developed mask-based beamforming, in which accurate mask estimation and diarization are paramount. Real-world experiments have shown that mask-based beamforming enables accurate ASR in meetings in small noise and reverberation with a signal-to-noise ratio (SNR) of 15–25 dB and a reverberation time (RT) of 120–350 ms. In this paper, we deal with a more adverse condition: meetings in large noise and reverberation with an SNR of 3–15 dB and an RT of 500 ms. To this end, we exploit a probabilistic spatial dictionary, a dictionary that consists of a pre-trained probability distribution of source location features for each potential speaker location. This dictionary enables us to perform mask estimation and diarization for beamforming accurately, even in the above adverse condition. The proposed method reduced the word error rate (WER) on real meeting data by 54.8% relative to our previous beamforming method.

Beamformer With Constant-Gm Vector Modulators and Its Spatial Intermodulation Distortion

Abstract: Spatial interference rejection in analog adaptive beamforming receivers can improve the distortion performance of the circuits following the beamforming network, but is susceptible to the nonlinearity of the beamforming network itself. This paper presents an analysis of intermodulation product cancellation in analog active phased array receivers and verifies the distortion improvement in a four-element adaptive beamforming receiver for low-power applications in the 1.0-2.5-GHz frequency band. In this architecture, a constant-Gm vector modulator is proposed that produces an accurate equidistance square constellation, leading to a sliced frontend design that is duplicated for each antenna element. By moving the transconductances to RF, a fourfold reduction in power is achieved, while simultaneously providing input impedance matching. The 65-nm implementation consumes between 6.5 and 9 mW per antenna element and shows a +1 to +20 dBm in-band and out-of-beam third-order intercept point due to intermodulation distortion reduction.

A Fast and Robust Beamspace Adaptive Beamformer for Medical Ultrasound Imaging

Abstract: Minimum variance beamformer (MVB) increases the resolution and contrast of medical ultrasound imaging compared with nonadaptive beamformers. These advantages come at the expense of high computational complexity that prevents this adaptive beamformer to be applied in a real-time imaging system. A new beamspace (BS) based on discrete cosine transform is proposed in which the medical ultrasound signals can be represented with less dimensions compared with the standard BS. This is because of symmetric beampattern of the beams in the proposed BS compared with the asymmetric ones in the standard BS. This lets us decrease the dimensions of data to two, so a high complex algorithm, such as the MVB, can be applied faster in this BS. The results indicated that by keeping only two beams, the MVB in the proposed BS provides very similar resolution and also better contrast compared with the standard MVB (SMVB) with only 0.44% of needed flops. Also, this beamformer is more robust against sound speed estimation errors than the SMVB.

Robust Adaptive Beamforming Using Noise Reduction Preprocessing-Based Fully Automatic Diagonal Loading and Steering Vector Estimation

Abstract: Diagonal loading provides a powerful and effective way to improve the robustness of the standard Capon beamformer. Several parameter-free robust adaptive beamformers (RAB) are considered in this paper. We reveal that the performances of them have somewhat degradation when the number of snapshots or that of sensors is large. To solve this problem, we emphatically study the well-known generalized linear combination-based method, the performance of which may degrade severely when the number of sensors increases, and propose a novel parameter-free technique, which is a combination of noise reduction preprocessing technique and truncated minimum mean square error criterion. As most of the parameter-free RAB techniques are very sensitive to the desired signal steering vector mismatch, this paper further proposes to construct a series connection between these RAB techniques and a steering vector estimation (SVE) method, where the SVE is implemented by a convex optimization technique. Simulation results show that the proposed method can achieve a promising performance in comparison with the competing methods.

Optimizing neural-network supported acoustic beamforming by algorithmic differentiation

Abstract: In this paper we show how a neural network for spectral mask estimation for an acoustic beamformer can be optimized by algorithmic differentiation. Using the beamformer output SNR as the objective function to maximize, the gradient is propagated through the beamformer all the way to the neural network which provides the clean speech and noise masks from which the beamformer coefficients are estimated by eigenvalue decomposition. A key theoretical result is the derivative of an eigenvalue problem involving complex-valued eigenvectors. Experimental results on the CHiME-3 challenge database demonstrate the effectiveness of the approach. The tools developed in this paper are a key component for an end-to-end optimization of speech enhancement and speech recognition.

Directivity-Beamwidth Tradeoff of Massive MIMO Uplink Beamforming for High Speed Train Communication

Abstract: High-mobility adaption and massive multiple-input multiple-output (MIMO) application are two primary evolving objectives for the next generation high-speed train (HST) wireless communication system. In this paper, we consider how to design a location-aware beamforming for the massive MIMO system in the high traffic density HST network. We first analyze the tradeoff between beam directivity and beamwidth, based on which we present the sensitivity analysis of positioning accuracy. Then, in order to guarantee a high efficient transmission, we derive an optimal problem to maximize the beam directivity under the restriction of diverse positioning accuracies. After that, we present a low-complexity beamforming design by utilizing location information, which requires neither eigendecomposing (ED) the uplink channel covariance matrix (CCM) nor ED the downlink CCM. Finally, we study the beamforming scheme in the future high traffic density HST network, where a two HSTs encountering scenario is emphasized. By utilizing the real-time location information, we propose an optimal adaptive beamforming scheme to maximize the achievable rate region under limited channel source constraint. Numerical simulation indicates that a massive MIMO system with less than a certain positioning error can guarantee a required performance with satisfying transmission efficiency in the high traffic density HST scenario and the achievable rate region when two HSTs encounter is greatly improved as well.

Directivity-Beamwidth Tradeoff of Massive MIMO Uplink Beamforming for High Speed Train Communication

Abstract: High-mobility adaption and massive Multiple-input Multiple-output (MIMO) application are two primary evolving objectives for the next generation high speed train (HST) wireless communication system. In this paper, we consider how to design a location-aware beamforming for the massive MIMO system in the high traffic density HST network. We first analyze the tradeoff between beam directivity and beamwidth, based on which we present the sensitivity analysis of positioning accuracy. Then, in order to guarantee a high efficient transmission, we derive an optimal problem to maximize the beam directivity under the restriction of diverse positioning accuracies. After that, we present a low-complexity beamforming design by utilizing location information, which requires neither eigen-decomposing (ED) the uplink channel covariance matrix (CCM) nor ED the downlink CCM (DCCM). Finally, we study the beamforming scheme in future high traffic density HST network, where a two HSTs encountering scenario is emphasized. By utilizing the real-time location information, we propose an optimal adaptive beamforming scheme to maximize the achievable rate region under limited channel source constraint. Numerical simulation indicates that a massive MIMO system with less than a certain positioning error can guarantee a required performance with satisfying transmission efficiency in the high traffic density HST scenario and the achievable rate region when two HSTs encounter is greatly improved as well.

A Single-Chip 64-Channel Ultrasound RX-Beamformer Including Analog Front-End and an LUT for Non-Uniform ADC-Sample-Clock Generation.

Abstract: A 64-channel RX digital beamformer was implemented in a single chip for 3-D ultrasound medical imaging using 2-D phased-array transducers. The RX beamformer chip includes 64 analog front-end branches including 64 non-uniform sampling ADCs, a FIFO/Adder, and an on-chip look-up table (LUT). The LUT stores the information on the rising edge timing of the non-uniform ADC sampling clocks. To include the LUT inside the beamformer chip, the LUT size was reduced by around 240 times by approximating an ADC-sample-time profile w.r.t. focal points (FP) along a scanline (SL) for a channel into a piece-wise linear form. The maximum error between the approximated and accurate sample times of ADC is eight times the sample time resolution (Ts) that is 1/32 of the ultrasound signal period in this work. The non-uniform sampling reduces the FIFO size required for digital beamforming by around 20 times. By applying a 9-dot image from Field-II program and 2-D ultrasound phantom images to the fabricated RX beamformer chip, the original images were successfully reconstructed from the measured output. The chip in a 0.13-um CMOS occupies 30.25 [Formula: see text] and consumes 605 mW.

Hypothesis of Improved Visualization of Microstructures in the Interventricular Septum with Ultrasound and Adaptive Beamforming.

Abstract: In this work, in vivo ultrasound cardiac images created with Capon's minimum variance adaptive beamformer are compared with images acquired with the conventional delay-and-sum beamformer. Specifically, we provide three views of a human heart imaged through the parasternal short-axis, the parasternal long-axis and the apical four-chamber views. The minimum variance beamformer produced images with improved lateral resolution, resulting in better resolved speckle structure and improved edges, especially on close investigation of the interventricular septum. These improvements in image quality might possibly improve the visualization of microstructures in the human heart.

Apodized adaptive beamformer.

Abstract: A number of studies aimed at improvement of ultrasound image quality, such as spatial resolution and contrast, have been conducted. Apodization is known as an important factor that determines image quality. However, in the case of amplitude and phase estimation (APES) beamforming, a kind of adaptive beamformer that has been employed in medical ultrasound recently, only rectangular apodization has been used in the previous studies. In this study, apodization was employed in adaptive beamforming, and its effects on image quality were examined in phantom experiments. We recently proposed a modified APES beamformer that reduces the computational complexity significantly using sub-aperture beamforming. In this study, the total receiving aperture was divided into four sub-apertures, and the APES beamforming was applied to the output from the four sub-apertures. Before the delay-and-sum (DAS) beamforming in each sub-aperture, echoes received by individual transducer elements were apodized with rectangular, Gaussian, and two Hanning functions, where the apodization with two Hanning functions realized lateral modulation of the ultrasonic field. The lateral spatial resolution was evaluated by the full width at half maximum of an echo from a string phantom, and the image contrast was evaluated using a cyst phantom. The modified APES beamformer realized a significantly better spatial resolution of 0.38 mm than that of the conventional delay-and-sum beamformer (0.67 mm), even with rectangular apodization. Using Gaussian apodization, the spatial resolution was further improved to 0.34 mm, and contrast was also improved from 4.3 to 5.1 dB. Furthermore, an image obtained by the modified APES beamformer with apodization consisting of two Hanning functions was better “tagged” as compared with the conventional DAS beamformer with the same apodization. Apodization was shown to be effective in adaptive beamforming, and an image obtained by the adaptive beamformer with lateral modulation seemed to have potential for improvement of the accuracy in measurement of tissue lateral motion.

Medical photoacoustic beamforming using minimum variance-based delay multiply and sum

Abstract: Delay-and-Sum (DAS) beamformer is the most common beamforming algorithm in Photoacoustic imaging (PAI) due to its simple implementation and real time imaging. However, it provides poor resolution and high levels of sidelobe. A new algorithm named Delay-Multiply-and-Sum (DMAS) was introduced. Using DMAS leads to lower levels of sidelobe compared to DAS, but resolution is not satisfying yet. In this paper, a novel beamformer is introduced based on the combination of Minimum Variance (MV) adaptive beamforming and DMAS, so-called Minimum Variance-Based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation leads to some terms which contain a DAS equation. It is proposed to use MV adaptive beamformer instead of existing DAS inside the DMAS algebra expansion. MVB-DMAS is evaluated numerically compared to DAS, DMAS and MV and Signal-to-noise ratio (SNR) metric is presented. It is shown that MVB-DMAS leads to higher image quality and SNR for about 13 dB, 3 dB and 2 dB in comparison with DAS, DMAS and MV, respectively.