Smart sensors differ from conventional ones in their ability to process a high amount of data close to the source and in their ability to communicate in a bi-directional way. We review the design of a distributed perception system using the CoMES design flow. This perception system is made of a collection of smart sensors connected by a fieldbus. We report on the ultrasonic smart sensor design part, sensor which aims at delivering features related to the perceived environment. We especially focus on the high-level design of such a subsystem, design which results in a functional structure describing the internal solution in a technology-independent way. Thanks to the CoMES technology, this solution can be validated through simulation before considering the architectural design aspects leading to the prototyping step.

The paper deals with the analytical evaluation of the asymptotic detection and false-alarm probabilities of multicycle and single-cycle detectors operating in additive white Gaussian noise with unknown and nonrandom spectral level, which are based on the cyclostationarity properties of the signal to be intercepted. The receiver operating characteristics are derived by using the asymptotic complex normality and the covariance expression of the sample average estimator of the cyclic covariance in an observed discrete-time series. A numerical example for interception of a binary phase-shift keying signal is considered.

The miniaturization of GSM handsets creates nonlinear acoustical echoes between the microphone and the loudspeaker when the signal level is high. Nonlinear adaptive filtering can tackle this problem but the computational complexity has to be reduced by restricting the number of coefficients introduced by the nonlinear models. This paper compares the performance of different nonlinear models. In a first training stage we use the OLS (orthogonal least squares) identification method to find models using the fewest coefficients along with a good fitting accuracy. In a second filtering stage these parsimonious models are used to adaptively filter the GSM signals.

The purpose of this paper is to evaluate theoretically in terms of receiver operating characteristics (ROC) the performance of a statistical test (based on the second order statistic) for the presence of cyclostationarity in a noisy observation. In order to obtain false alarm and detection probabilities, we consider the following specific situation: we choose a cycle frequency contained in a binary-phase-shift-keyed (BPSK) signal of interest (SOI) (BPSK signal is considered for its increasing attention in digital communications) and we test the presence of cyclostationarity of the SOI in a noisy observation. Two examples are considered, the SOI is buried either in a stationary Gaussian noise or in an another BPSK signal.

This paper deals with detection of weak cyclostationary signals embedded in colored Gaussian noise. We consider the normalized correlation function of the noise to be known and the noise power to be unknown. We propose a temporal structure of the single cycle detector which includes a prewhitening filter. We compare the performance of this detector to the classical radiometer and the modified radiometer. The performance is quantified in terms of receiver operating characteristics for two different noise power spectral densities. We compute the theoretical deflection, this measure gives a means to choose the best cyclic frequency used in the single cycle detector. We conclude that cyclic methods outperform radiometric methods when the noise and the signal power spectral densities strongly overlap and for a unknown noise power.

The autoregressive principal component technique uses the singular value decomposition (SVD) of an augmented dimension estimated autocorrelation matrix R to provide an accurate identification of frequencies in white noise. To avoid the eigen-decomposition of the matrix R, S.M. Kay and A.K. Shaw (1988) have applied a transformation on the inverse of R that truncates the eigenvalues associated with the noise. However, this technique requires the inversion of R and of another matrix. Two transformations that are applied directly to the matrix R are proposed. One is based on the matrix exponential and the other on component matrices. Another transformation analog to the MUSIC method without calculus of the eigenvectors is also proposed.>