Broadband focusing for partially adaptive beamforming

Simanapalli, S.; Kaveh, M.

doi:10.1109/7.250407

Cited by 24 publications

(7 citation statements)

References 28 publications

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“…Since the fractional bandwidth is varied by changing the carrier frequency, the received SNR, defined in (22), also changes. The received SNR for the first target is defined as SNR Rx,1 = 10 log 10 P Tx L 1 Fk B TB ,…”

Section: Simulation Resultsmentioning

confidence: 99%

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Efficient Multidimensional Wideband Parameter Estimation for OFDM Based Joint Radar and Communication Systems

et al. 2019

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In this paper, we propose a new pre-processing technique for efficient multidimensional wideband parameter estimation. One application is provided by an orthogonal frequency division multiplexing-(OFDM) based joint radar and communication system, which uses SIMO architecture. In this paper, the estimated parameters are given by the range (time delay), the relative velocity, and the direction of arrival (DoA) pairs of the dominant radar targets. Due to the wideband assumption, the received signals on different subcarriers are incoherent and, therefore, cannot fully exploit the frequency diversity of the OFDM waveform. To estimate the parameters jointly and coherently on different subcarriers, we propose an interpolation-based coherent multidimensional parameter estimation framework, where the wideband measurements are transformed into an equivalent narrowband system. Then, narrowband multidimensional parameter estimation algorithms can be applied. In particular, a wideband R-D periodogram is introduced as a benchmark algorithm, and we develop the R-D Wideband Unitary Tensor-ESPRIT algorithm. The simulations show that the proposed coherent parameter estimation method significantly outperforms the direct application of narrowband parameter estimation algorithms to the wideband measurements. If the fractional bandwidth is significant and the SNR is not too low, the estimates provided by the narrowband estimation algorithms can become inconsistent. Moreover, the interpolation order should be chosen according to the SNR regime. In the low SNR regime, interpolation with a lower-order (i.e., linear interpolation) is recommended. For higher SNRs, we propose an interpolation with higher-order polynomials, e.g., fourthorder (cubic splines) or even higher. ESPRIT, interpolation, joint radar and communication, periodogram, wideband OFDM. INDEX TERMS

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Section: Simulation Resultsmentioning

confidence: 99%

“…Then the results are combined by averaging over the obtained estimates [16], [17]. The latter approach [18]- [21], [22], [23] uses a coherent combination of signal subspaces at different frequencies. Then the parameters are estimated using this enhanced combined signal subspace.…”

Section: Introductionmentioning

confidence: 99%

Efficient Multidimensional Wideband Parameter Estimation for OFDM Based Joint Radar and Communication Systems

et al. 2019

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“…Here, F(ω 0 , ω k ) is the focusing matrix with focusing frequency ω 0 [37], X(ω k ) is the Fourier series of x(t) at ω k , with ω k = 2πk/T 0 , in which k is an integer, T 0 is the observation interval…”

Section: Data Modelmentioning

confidence: 99%

“…Analogous to the narrowband case, the focused interference plus noise covariance matrix R i + n used in either (37) or (41) may be practically replaced by the focused sample covariance matrix R xx , defined as…”

Section: Wideband Casementioning

confidence: 99%

Diversely polarised antenna‐array‐based narrowband/wideband beamforming via polarisational reconstruction matrix inversion

Lü

Huang

et al. 2018

IET signal process.

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The problem of beamforming using diversely polarised antenna array is addressed in the study. With the attempt to avoid signal cancellation caused by very small snapshot number, and/or look direction error and/or polarisation mismatch, the polarisational reconstruction matrix inversion based beamformer is developed for both the narrowband and wideband cases. The major contribution is on the reconstruction of the narrowband case interference-plus-noise covariance matrix and wideband case focused interference-plus-noise covariance matrix, for the cases of both completely polarised and partially or randomly polarised interferences, by the rank-1 and rank-2 spatial spectrum techniques, respectively. The performance of the proposed beamformer, against small snapshot number, look direction error, polarisation mismatch, and focusing error, was evaluated by extensive simulations, and compared with the polarisational extensions of some existing popular polarisation insensitive beamformers, in terms of the output signal-to-interference-plus-noise ratio values and the experimental deviation of the signalof-interest (SOI) estimate. The influences of direction difference between SOI and interference, degrees of polarisation of SOI and interference, and integrating range for reconstruction on beamforming performance are also studied.

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“…The mean output power of the conventional beamformer steered in the look direction is equal to the power of the source in the look direction. wideband signal like speech, one possibility is to process the signal in the frequency domain [57]. Hence, the problem of frequency dependency can be overcome by converting the time domain signal into frequency domain and using the narrowband beamforming for each frequency bin [58] [59] [60].…”

Section: Beamformingmentioning

confidence: 99%

Acoustic event detection and localization using distributed microphone arrays

Chakraborty

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Automatic acoustic scene analysis is a complex task that involves several functionalities: detection (time), localization (space), separation, recognition, etc. This thesis focuses on both acoustic event detection (AED) and acoustic source localization (ASL), when several sources may be simultaneously present in a room. In particular, the experimentation work is carried out with a meeting-room scenario. Unlike previous works that either employed models of all possible sound combinations or additionally used video signals, in this thesis, the time overlapping sound problem is tackled by exploiting the signal diversity that results from the usage of multiple microphone array beamformers. The core of this thesis work is a rather computationally efficient approach that consists of three processing stages. In the first, a set of (null) steering beamformers is used to carry out diverse partial signal separations, by using multiple arbitrarily located linear microphone arrays, each of them composed of a small number of microphones. In the second stage, each of the beamformer output goes through a classification step, which uses models for all the targeted sound classes (HMM-GMM, in the experiments). Then, in a third stage, the classifier scores, either being intra- or inter-array, are combined using a probabilistic criterion (like MAP) or a machine learning fusion technique (fuzzy integral (FI), in the experiments). The above-mentioned processing scheme is applied in this thesis to a set of complexity-increasing problems, which are defined by the assumptions made regarding identities (plus time endpoints) and/or positions of sounds. In fact, the thesis report starts with the problem of unambiguously mapping the identities to the positions, continues with AED (positions assumed) and ASL (identities assumed), and ends with the integration of AED and ASL in a single system, which does not need any assumption about identities or positions. The evaluation experiments are carried out in a meeting-room scenario, where two sources are temporally overlapped; one of them is always speech and the other is an acoustic event from a pre-defined set. Two different databases are used, one that is produced by merging signals actually recorded in the UPC¿s department smart-room, and the other consists of overlapping sound signals directly recorded in the same room and in a rather spontaneous way. From the experimental results with a single array, it can be observed that the proposed detection system performs better than either the model based system or a blind source separation based system. Moreover, the product rule based combination and the FI based fusion of the scores resulting from the multiple arrays improve the accuracies further. On the other hand, the posterior position assignment is performed with a very small error rate. Regarding ASL and assuming an accurate AED system output, the 1-source localization performance of the proposed system is slightly better than that of the widely-used SRP-PHAT system, working in an event-based mode, and it even performs significantly better than the latter one in the more complex 2-source scenario. Finally, though the joint system suffers from a slight degradation in terms of classification accuracy with respect to the case where the source positions are known, it shows the advantage of carrying out the two tasks, recognition and localization, with a single system, and it allows the inclusion of information about the prior probabilities of the source positions. It is worth noticing also that, although the acoustic scenario used for experimentation is rather limited, the approach and its formalism were developed for a general case, where the number and identities of sources are not constrained.

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Broadband focusing for partially adaptive beamforming

Cited by 24 publications

References 28 publications

Efficient Multidimensional Wideband Parameter Estimation for OFDM Based Joint Radar and Communication Systems

Efficient Multidimensional Wideband Parameter Estimation for OFDM Based Joint Radar and Communication Systems

Diversely polarised antenna‐array‐based narrowband/wideband beamforming via polarisational reconstruction matrix inversion

Acoustic event detection and localization using distributed microphone arrays

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