A training-based approach to classification of unknown transients with unknown arrival time and Doppler shift

The optimum test statistic for the detection of binary sure signals in stationary Gaussian noise takes a particularly simple form, that of a correlation integral, when the solution, denoted by q(t) , of a given integral equation is well behaved (L_{2}) . For the case of a rational noise spectrum, a solution of the integral equation can always be obtained if delta functions are admitted. However, it cannot be argued that the test statistic obtained by formally correlating the receiver input with a q(t) which is not L_{2} is optimum. In this paper, a rigorous derivation of the optimum test statistic for the case of exponentially correlated Gaussian noise R(\tau) = \sigma^{2} e^{-\alpha|\tau|} is obtained. It is proved that for the correlation integral solution to yield the optimum test statistic when q(t) is not L_{2} , it is sufficient that the binary signals have continuous third derivatives. Consideration is then given to the case where a, the bandwidth parameter of the exponentially correlated noise, is described statistically. The test statistic which is optimum in the Neyman-Pearson sense is formulated. Except for the fact that the receiver employs \alpha_{\infty} (which in general depends on the observed sample function) in place of \alpha , the operations of the optimum detector are unchanged by the uncertainty in \alpha . It is then shown that almost all sample functions can be used to yield a perfect estimate of \alpha . Using this estimate of \alpha , a test statistic equivalent to the Neyman-Pearson statistic is obtained.

1. Behavioral measures of target velocity discrimination, Doppler shift discrimination, and Doppler shift compensation are described for the fish-catching bat,Noctilio leporinus. These bats echolocate using a short constant frequency/frequency modulated (short-CF/FM) sonar pulse. 2. Two bats, trained by operant conditioning, performed an in-flight discrimination between sequentially-presented targets which differed in the velocity of movement in the line of the bat's flight path. Both bats discriminate target velocity differences as low as 40 cm/s. This discrimination ability is independent of the absolute speed or direction of target movement. 3. These same bats were subsequently trained by operant conditioning to discriminate between the presence or absence of an upward frequency (simulated Doppler) shift in artificial echoes of their sonar pulses. The smallest frequency shift that can be discriminated is 570 Hz, corresponding to the Doppler shift produced by a relative velocity of 170cm/s. 4. Since these bats cannot detect the Doppler shift associated with moving targets at the threshold of target velocity discrimination, we conclude that Doppler information is not used to make velocity measurements. Target velocity discrimination must instead be based upon range-rate information obtained from the wide band FM component of the sonar pulse. 5. Three of four bats partially compensate for Doppler shifts of their echoes produced by their flight velocity. During flight, the frequency of their sonar emissions is lowered enough to compensate for about 50% of the flight-induced Doppler shift. None of the bats compensate for target-induced Doppler shifts. 6. The functional significance of short CF signals and the echolocation of moving targets byNoctilio and other bats are discussed.

The problem of optimal signal design for coherent detection of binary signals in Gaussian noise is revisited under power and secrecy constraints. In particular, the aim is to select the binary transmitted signals in an optimal manner so that the probability of error is minimized at an intended receiver while the probability of error at an eavesdropper is maintained above a threshold value and the signal powers are limited. It is shown that an optimal solution exists in the form of antipodal signaling along the eigenvector corresponding to the solution of a maximum (possibly generalized) eigenvalue problem, which is specified explicitly based on the channel coefficient matrices and the noise covariance matrices at the intended receiver and the eavesdropper. Furthermore, optimal signal design can be performed in an efficient manner by solving a semidefinite programming (SDP) relaxation followed by a matrix rank-one decomposition. Numerical examples are provided to illustrate optimal solutions for three different but exhaustive cases.

Low-complex and low-power non-coherent energy detectors (EDs) are interesting for low data rate impulse radio (IR) ultra wideband (UWB) systems, but suffer from a loss in performance compared to coherent receivers. The performance of an ED also strongly depends on the integration interval (window size) of the integrator and the window position. This paper presents a non-coherent fourth-order detector (FD) which can discriminate between Gaussian noise signals and non-Gaussian IR-UWB signals by directly estimating the fourth-order moment of the received signal. The performance of the detectors is evaluated using realistic channels measured in a corridor, an office and a laboratory environment. The results show that bit-error-rate (BER) performance of the proposed FD receiver is slightly better than the ED in low signal-to-noise ratio (SNR) region and its performance improves as the SNR increases. In addition, BER of the FD receiver is less sensitive to overestimation of the integration interval making it relatively robust to variations of the channel delay spread. Finally, a criteria for the selection of integration time of the proposed detector is suggested.

Recent results on joint detection-estimation for discrete data are extended to the continuous data case for Gaussian signals in White Gaussian noise. Stochastic differential equations are obtained describing the temporal evolution of the sufficient statistic for minimum Bayes-risk detection and the optimal mean-square error estimate of the signal state vector. An approximate solution for the estimation equation is suggested.

MLP-CFAR for improving coherent radar detectors robustness in variable scenarios

In this paper, the problem of the incoherent detection of targets with unknown parameters in K-distributed clutter is tackled. Multilayer Perceptron (MLP) based detectors are designed to approximate the Average Likelihood Ratio (ALR) based one, which implements the Neyman-Pearson optimum detector for the defined composite hypothesis test problem. Conventional solutions such as the linear law and Constant False Alarm Rate (CFAR) detectors are used as references for evaluating the performance improvement achieved by the MLP solutions. Estimated Receiver Operating Characteristic (ROC) and detection curves show that the MLP detector outperforms the reference ones in all the considered cases of study.

The problem of interest is the detection of transient signals in additive white Gaussian noise (AWGN) in the presence of missing signal observations (samples). Specifically, a fusion center aims at detecting the presence of transient signals by collecting measurements from individual sensors through erasure channels. Under the assumption that the fusion center can control the sampling procedure through a feedback channel, a strategy is proposed to adapt the sampling rate in response to sample missing with the goal of achieving accurate and timely decisions with the minimum communication cost measured by sampling rate. The proposed strategy is flexible in that it can be configured to suit different performance requirements. Compared with fixed-rate sampling, the proposed strategy achieves better tradeoff between Quality of Detection (QoD) and communication cost through dynamic adaptation.

We introduce a search method for a new class of gravitational-wave signals, namely long-duration O(hours - weeks) transients from spinning neutron stars. We discuss the astrophysical motivation from glitch relaxation models and we derive a rough estimate for the maximal expected signal strength based on the superfluid excess rotational energy. The transient signal model considered here extends the traditional class of infinite-duration continuous-wave signals by a finite start-time and duration. We derive a multi-detector Bayes factor for these signals in Gaussian noise using $\F$-statistic amplitude priors, which simplifies the detection statistic and allows for an efficient implementation. We consider both a fully coherent statistic, which is computationally limited to directed searches for known pulsars, and a cheaper semi-coherent variant, suitable for wide parameter-space searches for transients from unknown neutron stars. We have tested our method by Monte-Carlo simulation, and we find that it outperforms orthodox maximum-likelihood approaches both in sensitivity and in parameter-estimation quality.

The paper addresses the problem of estimating the chirp signals embedded in Gaussian noise. The proposed method is based on a model of the signal phase as a polynomial. This approach offers the opportunity to represent these signals by an adequate state space model and to apply standard Kalman filtering procedures in view to estimate the parameters of chirp signals. Procedure simulations were made on linear chirp sinusoids with time-varying amplitude and are consistent with the theoretical approach. The paper presents the most important results.

The paper discusses the problem of detecting transient signals of unknown waveforms in white Gaussian noise. The signals are modeled as impulse responses of rational transfer functions with unknown parameters. A modified generalized likelihood ratio test (MGLRT) is proposed and its statistical properties are analyzed for both known and unknown noise variances. The MGLRT involves constrained maximum likelihood estimation of the signal parameters. The performance of the MGLRT is compared to that of an optimal matched filter and an energy detector, for some test cases. Also, the theoretical distributions of the likelihood ratios under H 0 and H 1 are compared to experimental distributions obtained by Monte Carlo simulations.

Noise induced hearing loss (NIHL) as one of the major avoidable occupational related health issues has been studied for decades. To assess NIHL, the excitation pattern (EP) has been considered as one of the mechanisms to estimate the movements of the basilar membrane (BM) in the cochlea. In this study, two auditory filters, dual resonance nonlinear (DRNL) filter and rounded-exponential (ROEX) filter are applied to create two EPs, the velocity EP and the loudness EP respectively. Two noise hazard metrics are proposed based on two proposed EPs to evaluate hazardous levels caused by different types of noise. Moreover Gaussian noise and tone signals are simulated to evaluate performances of the proposed EPs and the noise metrics. The results show that both EPs can reflect the responses of the BM to different types of noise. For Gaussian noise there is a frequency shift between the velocity EP and the loudness EP. The results suggest that both EPs can be used for assessment of NIHL.

Optimal detection of a known FSK-modulated binary signal in additive white Gaussian noise using a matched filter receiver requires knowledge of second-order noise statistics. The dependence on noise statistics causes the probability of detection to be sensitive to errors in the noise variance value. This makes the optimal scheme of limited use in situations where the noise statistics are unknown and cannot be estimated reliably. We propose an alternative approach, which has a more easily calculated test statistic than the optimal method and yields a constant false detection rate, regardless of noise statistics. While this approach has sub-optimal detection probability, it is significantly advantageous in applications where a primary interest is to limit false detections strictly. In addition, it allows one to determine easily the required detection length (in symbols) to achieve a desired performance level.

<p indent="0mm">Ionospheric Doppler shift is the frequency shift of radio waves reflected by the disturbed ionosphere. The variations of the ionosphere, such as its vertical movements (Doppler velocity), can be obtained from the frequency shift. The important information is useful for monitoring and studying ionospheric disturbances. This dataset was generated by the ionospheric high-frequency Doppler shift monitor of the Peking University Ionosphere Station. The monitor received the short-wave time signal broadcast from the National Time Service Centre, and performed the Fast Fourier Transform (FFT) on the difference between the received signal and the standard reference signal to obtain the frequency shift spectrum results. The dataset can be used to obtain the Doppler shift and corresponding Doppler velocity, which can provide data support for space environment research and applications.

An ultra-wide-band (UWB) signal is characterized by a radiated spectrum with a very wide bandwidth around a relatively low center frequency. In this paper, we study the reduced fading margin property of UWB signals. To evaluate the fading margin, we compare the performance of UWB signals in an environment with only additive white Gaussian noise (AWGN) versus the performance of UWB signals in a dense multipath environment with AWGN. The assumption here is that the presence of multipath causes a small increase in the signal-to-noise ratio required to achieve reasonable levels of bit error rate. A numerical example confirms this assumption, more specifically, the example shows that to achieve a bit error rate equal to 10/sup -5/, we require about 13.5 dB in the AWGN case and about 15 dB in the multipath case, resulting in a fading margin of just 1.5 dB. This small fading margin can be understood by the ability of the UWB signal to resolve the dense multipath.