The existing robust narrowband beamformers based on probability-constrained optimiza- tion have an excellent performance as compared to several state-of-the-art robust beamforming algo- rithms. However, they always assume that the steering vector errors are small enough. Without this assumption, we extend the probability-constrained approach to a wideband beamformer. In addition, a novel robust wideband beamformer with frequency invariance constraints is proposed by introducing the response variation (RV) element. Our problems can be reformulated in a convex form as the it- erative second order cone programming (SOCP) problem and solved effectively using well-established interior point method. Compared with existing robust wideband beamformers, a more efficient control over the beamformer's response against the steering vector errors is achieved with an improved output signal-to-interference-plus-noise ratio (SINR).

/pdf/robust-adaptive-wideband-beamforming-using-probability-1t3ov1elo7.pdf

Robust Adaptive Wideband Beamforming Using Probability-Constrained Optimization

The presented work explores novel methods for synthesizing approximately frequency independent array factors at lower hardware complexity for wideband beamforming applications. The proposed approach employs 2-D infinite impulse response (IIR) digital beam filters together with nested uniform linear arrays (ULAs). The array is designed to have multiple levels of nesting. Each level of nesting consists of a ULA covering a temporal subband of the incident wideband signal. The use of nested arrays provides the required aperture size using a smaller number of elements compared to using a single ULA to capture the entire wideband signal. The use of different levels of nesting allows the operation of the digital processor for each sub-band at different clock rates. This is a hierarchical approach that saves both digital VLSI hardware and power consumption. The 2-D IIR digital beam filters that process each subband signal from each of the nested subarray achieves wideband beamforming. Simulations illustrate approximately frequency independent passbands as required in wideband beamforming.

Mixed microwave-digital and multi-rate approach for wideband beamforming applications using 2-D IIR beam filters and nested uniform linear arrays

In order to reduce the hardware and computational complexity of adaptive wideband beamformers, a novel adaptive wideband beamformer based on uniform linear array is proposed by the phase compensation. By compensating the difference of the phase response of the array output for each selcted frequency, this beamformer maintains the array output response of each discrete frequency close to that of the reference frequency. Then we reconstruct the novel constraint vector, and realize the wideband adaptive beamforming under the LCMV criteria. Numerical examples show that the proposed beamformer achieves a better performance.

https://www.ursi.org/Proceedings/ProcGA14/papers/ursi_paper2931.pdf

A novel adaptive wideband beamformer based on uniform linear array

Multiple-input multiple-output (MIMO) radars offer significant advantages over conventional standalone radars in terms of target detection and localization capabilities. However, to fully exploit their potential, such systems require excellent time and phase synchronization among the central unit and the radar heads. Only recently, thanks to microwave photonics (MWP) techniques, it has been possible to develop the first coherent MIMO radar demonstrators. In this paper, a simulation tool in MATLAB programming language is proposed to model coherent MIMO radars based on MWP techniques in maritime surveillance applications. Moreover, a novel approach for estimating the radar cross section of extended maritime targets is presented. The system performance in co-located and distributed MIMO configuration, as well as in single- and multi-band operations, is evaluated by means of relevant key performance indicators (KPIs). Simulations are carried out in two close-to-reality scenarios. In the first, a co-located MIMO radar is mounted on top of a patrolling vessel. In the second, a distributed MIMO radar is deployed inside a port. The proposed KPIs are analyzed in terms of both geometric and frequency diversities of the system, laying the foundations for general system optimization criteria valid in any given surveillance application. 

Performance Evaluation of Photonics-Based Coherent MIMO Radar Systems for Maritime Surveillance

To improve robustness of the wideband beamformer against look direction error, a low computational complexity robust wideband beamforming algorithm with sensor delay lines (SDLs) is proposed by extending the narrowband robust beamforming method to the wideband case. Based on the SDL wideband model, this wideband beamforming optimizing formula originally consists of a group of probabilistic constraints imposed on the sampled frequency points and the coefficient vector constraint. By constraining the Euclidean distance between the response at reference frequency points and that at others over the angles of interest, these probabilistic constraints can be replaced by just one constraint on the reference frequency point and the computational complexity is reduced. The last optimizing formula can be solved with interior point method after being reformed to the second order cone programming (SOCP). The simulations illustrate the effectiveness of the proposed algorithm. (5 pages)

http://ieeexplore.ieee.org/iel7/6607261/6624271/06624513.pdf

A novel probability-constrained approach to robust wideband beamforming with sensor delay lines

A least squares approach to the design of frequency invariant beamformers with sensor delay lines (SDL) in subbands is proposed, based on two different types of formulations. One is the constrained least squares (CLS) formulation, which is a much simple and efficient method, but requires potentially unstable matrix reversion. And the other is the constrained total least squares (CTLS) formulation, which is much stable without matrix inversion. CTLS methods' solutions can be obtained by finding the minimum generalized eigenvector of two matrices. A series of constraint designs with different requirements are presented. To measure the frequency invariant property more critically, the concept of normalized beam pattern variance is raised. Simulation results demonstrate that subband methods exhibit satisfactory frequency invariant property, mainlobe resolution, sidelobe attenuation and interference suppression. Moreover, compared to original fullband method, the subband method can improve beam pattern performance and release the cost of sensors with similar property, efficiently. (8 pages)

Least squares approach to the design of frequency invariant beamformer with sensor delay lines in subbands

The data transferring of sheer model size of CNN (Convolution Neural Network) has become one of the main performance challenges in modern intelligent systems. Although pruning can trim down substantial amount of non-effective neurons, the excessive DRAM accesses of the non-zero data in a sparse network still dominate the overall system performance. Proper data mapping can enable efficient DRAM accesses for a CNN. However, previous DRAM mapping methods focus on dense CNN and become less effective when handling the compressed format and irregular accesses of sparse CNN. The extensive design space search for mapping parameters also results in a time-consuming process. This paper proposes DASC: a DRAM data mapping methodology for sparse CNNs. DASC is designed to handle the data access patterns and block schedule of sparse CNN to attain good spatial locality and efficient DRAM accesses. The bank-group feature in modern DDR is further exploited to enhance processing parallelism. DASC also introduces an analytical model to facilitate fast exploration and quick convergence of parameter search in minutes instead of days from previous work. When compared with the state-of-the-art, DASC decreases the total DRAM latencies and attains an average of 17.1x, 14.3x, and 23.3x better DRAM performance for sparse AlexNet, VGG-16, and ResNet-50 respectively.

DASC: A DRAM Data Mapping Methodology for Sparse Convolutional Neural Networks

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A novel probability-constrained approach to robust wideband beamforming with sensor delay lines

Least squares approach to the design of frequency invariant beamformer with sensor delay lines in subbands

DASC: A DRAM Data Mapping Methodology for Sparse Convolutional Neural Networks