Adaptive detection in nonhomogeneous environments using the generalized eigenrelation

This letter considers adaptive detection of a signal in a nonhomogeneous environment, more precisely under a covariance mismatch between the test vector and the training samples, due to an interference that is not accounted for by the training samples, e.g., a sidelobe target or an undernulled interference. We assume that the covariance matrices of the test vector and the training samples verify the so-called generalized eigenrelation. Under this assumption, we derive the generalized likelihood ratio test and show that it coincides with Kelly’s detector

Echo Cancellation - A Likelihood Ratio Test for Double-talk Versus Channel Change

Echo cancellers are in wide use in both electrical (four wire to two wire mismatch) and acoustic (speaker-microphone coupling) applications. One of the main design problems is the control logic for adaptation. Basically, the algorithm weights should be frozen in the presence of double-talk and adapt quickly in the absence of double-talk. The control logic can be quite complicated since it is often not easy to discriminate between the echo signal and the near-end speaker. This paper derives a log likelihood ratio test (LRT) for deciding between double-talk (freeze weights) and a channel change (adapt quickly) using a stationary Gaussian stochastic input signal model. The probability density function of a sufficient statistic under each hypothesis is obtained and the performance of the test is evaluated as a function of the system parameters. The receiver operating characteristics (ROCs) indicate that it is difficult to correctly decide between double-talk and a channel change based upon a single look. However, post-detection integration of approximately one hundred sufficient statistic samples yields a detection probability close to unity (0.99) with a small false alarm probability (0.01)

Detection of gravitational-wave bursts with chirplet-like template families

Gravitational Wave (GW) burst detection algorithms typically rely on the hypothesis that the burst signal is "locally stationary", that is it changes slowly with frequency. Under this assumption, the signal can be decomposed into a small number of wavelets with constant frequency. This justifies the use of a family of sine-Gaussian templates in the Omega pipeline, one of the algorithms used in LIGO-Virgo burst searches. However there are plausible scenarios where the burst frequency evolves rapidly, such as in the merger phase of a binary black hole and/or neutron star coalescence. In those cases, the local stationarity of sine-Gaussians induces performance losses, due to the mismatch between the template and the actual signal. We propose an extension of the Omega pipeline based on chirplet-like templates. Chirplets incorporate an additional parameter, the chirp rate, to control the frequency variation. In this paper, we show that the Omega pipeline can easily be extended to include a chirplet template bank. We illustrate the method on a simulated data set, with a family of phenomenological binary black-hole coalescence waveforms embedded into Gaussian LIGO/Virgo-like noise. Chirplet-like templates result in an enhancement of the measured signal-to-noise ratio.Comment: 8 pages, 6 figures. Submitted to Class. Quantum Grav. Special issue: Proceedings of GWDAW-14, Rome (Italy), 2010; fixed several minor issue

The potential role of auditory prediction error in decompensated tinnitus: An auditory mismatch negativity study

Introduction: Some tinnitus subjects habituate to their tinnitus but some others do not and complain of its annoyance tremendously. Normal sensory memory and change detection processes are needed for detecting the tinnitus signal as a prediction error and habituation to tinnitus. The purpose of this study was to compare auditory mismatch negativity as the index of sensory memory and change detection among the studied groups to search for the factors involving in the perception of tinnitus and preventing habituation in decompensated tinnitus subjects. Methods: Electroencephalography was recorded from scalp electrodes in compensated tinnitus, decompensated tinnitus, and no tinnitus control subjects. Mismatch negativity was obtained using the oddball paradigm with frequency, duration, and silent gap deviants. Amplitude, latency, and area under the curve of mismatch negativities were compared among the three studied groups. Results: The results showed lower mismatch negativity amplitude and area under the curve for the higher frequency deviant and for the silent gap deviant in decompensated tinnitus group compared to normal control and compensated tinnitus group. Conclusions: This study revealed a deficit in sensory memory and change detection processing in decompensated tinnitus subjects. This causes persistent prediction errors; tinnitus signal is consistently detected as a new signal and activates the brain salience network and consequently prevents habituation to tinnitus. Mismatch negativity is proposed as an index for monitoring tinnitus rehabilitation. © 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc

Mismatched Quantum Filtering and Entropic Information

Quantum filtering is a signal processing technique that estimates the posterior state of a quantum system under continuous measurements and has become a standard tool in quantum information processing, with applications in quantum state preparation, quantum metrology, and quantum control. If the filter assumes a nominal model that differs from reality, however, the estimation accuracy is bound to suffer. Here I derive identities that relate the excess error caused by quantum filter mismatch to the relative entropy between the true and nominal observation probability measures, with one identity for Gaussian measurements, such as optical homodyne detection, and another for Poissonian measurements, such as photon counting. These identities generalize recent seminal results in classical information theory and provide new operational meanings to relative entropy, mutual information, and channel capacity in the context of quantum experiments.Comment: v1: first draft, 8 pages, v2: added introduction and more results on mutual information and channel capacity, 12 pages, v3: minor updates, v4: updated the presentatio

A 14-Bit Oversampled SAR ADC With Mismatch Error Shaping and Analog Range Compensation

DAC mismatch is a major challenge for high-resolution ADCs. This brief proposes an analog-detection-based input range compensation technique for high-resolution ADCs with mismatch error shaping (MES). By applying a pre-comparison and suitably switching the DAC MSB, the input loss caused by MES is compensated. By adopting a flying-capacitor sampling technique, the prediction errors found in prior solutions are avoided. The prototype 14-bit SAR ADC achieves 80.4 dB SNDR and 93 dB SFDR in a 4 kHz signal bandwidth with an OSR of 16. It only occupies 0.0034 mm2 and consumes 0.656μW under a 0.8 V supply, leading to a Schreier figure-of-merit of 178.3 dB. These features make it suitable for miniaturized high-performance IoT and biomedical systems.</p

A 14-Bit Oversampled SAR ADC With Mismatch Error Shaping and Analog Range Compensation

DAC mismatch is a major challenge for high-resolution ADCs. This brief proposes an analog-detection-based input range compensation technique for high-resolution ADCs with mismatch error shaping (MES). By applying a pre-comparison and suitably switching the DAC MSB, the input loss caused by MES is compensated. By adopting a flying-capacitor sampling technique, the prediction errors found in prior solutions are avoided. The prototype 14-bit SAR ADC achieves 80.4 dB SNDR and 93 dB SFDR in a 4 kHz signal bandwidth with an OSR of 16. It only occupies 0.0034 mm2 and consumes 0.656μW under a 0.8 V supply, leading to a Schreier figure-of-merit of 178.3 dB. These features make it suitable for miniaturized high-performance IoT and biomedical systems.</p

Detection of a signal in linear subspace with bounded mismatch

We consider the problem of detecting a signal of interest in a background of noise with unknown covariance matrix, taking into account a possible mismatch between the actual steering vector and the presumed one. We assume that the former belongs to a known linear subspace, up to a fraction of its energy. When the subspace of interest consists of the presumed steering vector, this amounts to assuming that the angle between the actual steering vector and the presumed steering vector is upper bounded. Within this framework, we derive the generalized likelihood ratio test (GLRT). We show that it involves solving a minimization problem with the constraint that the signal of interest lies inside a cone. We present a computationally efficient algorithm to find the maximum likelihood estimator (MLE) based on the Lagrange multiplier technique. Numerical simulations illustrate the performance and the robustness of this new detector, and compare it with the adaptive coherence estimator which assumes that the steering vector lies entirely in a subspace