A maximum likelihood (ML) acoustic source location estimation method is presented for the application in a wireless ad hoc sensor network. This method uses acoustic signal energy measurements taken ...at individual sensors of an ad hoc wireless sensor network to estimate the locations of multiple acoustic sources. Compared to the existing acoustic energy based source localization methods, this proposed ML method delivers more accurate results and offers the enhanced capability of multiple source localization. A multiresolution search algorithm and an expectation-maximization (EM) like iterative algorithm are proposed to expedite the computation of source locations. The Crame/spl acute/r-Rao Bound (CRB) of the ML source location estimate has been derived. The CRB is used to analyze the impacts of sensor placement to the accuracy of location estimates for single target scenario. Extensive simulations have been conducted. It is observed that the proposed ML method consistently outperforms existing acoustic energy based source localization methods. An example applying this method to track military vehicles using real world experiment data also demonstrates the performance advantage of this proposed method over a previously proposed acoustic energy source localization method.
We propose a novel random triggering-based modulated wideband compressive sampling (RT-MWCS) method to facilitate efficient realization of sub-Nyquist rate compressive sampling systems for sparse ...wideband signals. Under the assumption that the signal is repetitively (not necessarily periodically) triggered, RT-MWCS uses random modulation to obtain measurements of the signal at randomly chosen positions. It uses multiple measurement vector method to estimate the nonzero supports of the signal in the frequency domain. Then, the signal spectrum is solved using least square estimation. The distinct ability of estimating sparse multiband signal is facilitated with the use of level triggering and time-to-digital converter devices previously used in random equivalent sampling scheme. Compared to the existing compressive sampling (CS) techniques, such as modulated wideband converter (MWC), RT-MWCS is with simple system architecture and can be implemented with one channel at the cost of more sampling time. Experimental results indicate that, for sparse multiband signal with unknown spectral support, RT-MWCS requires a sampling rate much lower than Nyquist rate, while giving great quality of signal reconstruction.
In this paper, a comprehensive system behavioral model of frequency-interleaved analog-to-digital converters (FI-ADCs) is presented. The model addresses mismatch errors due to imperfect channel ...separation, incomplete anti-aliasing, in-phase/quadrature branch imbalances, and jitter-induced distortions. Using this model, an integrated analysis of combined channel mismatch effects is provided. Given error parameters or their distributions, closed-form expressions of the expected output signal-to-noise ratio and image rejection ratio are formulated for FI-ADCs with an arbitrary number of channels. The inherent circuit behaviors, sampling clock jitters, and local oscillator phase noise effects are also discussed. Based on the analysis and modeling, a discrete-time equivalent model for FI-ADC systems is derived. Therefrom, an integrated channel mismatch compensation scheme is proposed that contrasts existing signal recovery approaches which calibrate specific types of errors separately. The performance of this novel method is compared with conventional solutions using extensive simulations. Very favorable numerical results are observed.
A one-bit joint sparse representation direction of arrival (OBJSR-DOA) estimation approach is proposed in this letter. By exploiting the joint spatial and spectral correlations inherent in acoustic ...sensor array data, the proposed OBJSR-DOA approach provides reliable DOA estimation from only the sign bit of randomly subsampled acoustic sensor data. The random subsampling and single-bit quantization allow significant reduction of data to be transmitted to the fusion center without additional energy consumption requirement in the source coding/compression operation. Compared with existing compressive sensing-based DOA estimation methods, the superiority of the proposed approach in providing data volume reduction and performance improvement is verified by both simulations and field experiments using a prototype wireless sensor array network platform.
In this paper, we propose a novel multi-view image denoising algorithm based on convolutional neural network (MVCNN). Multi-view images are arranged into 3D focus image stacks (3DFIS) according to ...different disparities. The MVCNN is trained to process each 3DFIS and generate a denoised image stack that contains the recovered image information for regions of particular disparities. The denoised image stacks are then fused together to produce a denoised target view image using the estimated disparity map. Different from conventional multi-view denoising approaches that group similar patches first and then perform denoising on those patches, our CNN-based algorithm saves the effort of exhaustive patch searching and greatly reduces the computational time. In the proposed MVCNN, residual learning and batch normalization strategies are also used to enhance the denoising performance and accelerate the training process. Compared with the state-of-the-art single image and multi-view denoising algorithms, experiments show that the proposed CNN-based algorithm is a highly effective and efficient method in Gaussian denoising of multi-view images.
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A novel digital adaptive blind calibration technique for frequency-interleaved analog-to-digital converters (FI-ADCs) with arbitrary numbers of channels is proposed. It performs the estimation and ...correction of multiple inherent circuit deficiencies alternately and recursively in the background, including spectral leakage, harmonic folding, jitter, I/Q mirror and aliasing images, and suffices to deliver projected calibration capabilities with deliberately chosen initial guesses. We are of the opinion that this is the first study to address the joint channel identification and error compensation problem encountered in the FI-ADC design. For the efficient implementation, an analytical time-interleaving-like equivalent model is formulated such that the number of unknown mismatch parameters is significantly reduced, and signals devoted to cyclic estimation are maximally decimated. This derived model not only propounds a better perspective for comprehension and interpretation of the FI system mechanism but also enables sufficient scalability and flexibility of the developed calibration framework. Extensive simulation results further show that this novel architecture achieves a good balance between the computational complexity and convergence, and provides an exceptional and relatively flat effective number of bit (ENOB) performance over a wide bandwidth.
This paper connects the linear steady-state systematic error models of the time- and frequency-interleaved analog-to-digital converters (ADCs). Exposing their relations is of importance because ...estimation and compensation methods developed for one architecture may therefore apply to the other. Most critical impairments in both ADC structures include static mismatches and random jitter. The former has been well studied and can be generalized to the model connection, whereas not much is known regarding the latter. To support designers becoming more capable of making optimal design and architectural decisions on parallel ADCs, comprehensive phase noise analysis and comparison are carried out to reveal the distinctions between these two sampling architectures. Design examples with considerations are also provided for demonstration purposes.
The frequency-interleaved analog-to-digital converter features large bandwidth, high speed, medium-to-high resolution with respectable jitter robustness, albeit suffering from circuit impairments ...such as spectral leakages, harmonic interferences, in-phase/quadrature and aliasing images. For the first time, a digital compensation framework is introduced to minimize these impacts simultaneously. In this framework, channel recombination is performed before calibration. It circumvents the needs for analog harmonic rejection and channel separation, thus enabling significant simplification of the complex analog frontend design. A comprehensive system model that incorporates all the aforementioned distortions is formulated and leads to an iterative solution. Given the mismatch parameters, simulation outcomes validate the feasibility of this proposed framework and promise impressive performance benefits.
A cross-layer design methodology that jointly optimizes the link-layer network flow rate and the physical layer transmission power wherein rate is developed for energy-constrained underwater wireless ...sensor networks. The objective of the design is to maximize the lifetime of the network operation for a given network topology and a bound of the total transmission time of all links. Initially, each node is set to transmit at the most energy-efficient transmission rate, which is derived using an underwater acoustic channel model. A constrained least square problem then is formulated which yields an analytical solution of the optimal network flow at each link that maximizes the network lifetime. If the timing constraint is not met, a new iteration will begin with the restriction on per-node energy consumption incrementally relaxed. Usually, the algorithm converges within a couple of iterations. Two design examples, a linear configured network, and a rhombus configured network are used to demonstrate the design procedures. Extensive simulations have been performed and superior performance (longer network lifetime) of this proposed design has been observed.
In this paper, we present a generalized iterated Kalman filter (GIKF) algorithm for state estimation of a nonlinear stochastic discrete-time system with state-dependent multiplicative observation ...noise. The GIKF algorithm adopts the Newton-Raphson iterative optimization steps to yield an approximate maximum a posteriori estimate of the states. The mean-square estimation error (MSE) and the Cramér-Rao lower bound (CRLB) of the state estimates are also derived. In particular, the local convergence of MSE of GIKF is rigorously established. It is also proved that the GIKF yields a smaller MSE than those of the generalized extended Kalman filter and the traditional extended Kalman filter. The performance advantages and convergence of GIKF are demonstrated using Monte Carlo simulations on a target tracking application in a range measuring sensor network.
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