System for plotting subsoil structure and method therefor

Data for use in producing a tomograph of subsoil structure between boreholes is derived by pacing spaced geophones in one borehole, on the Earth surface if desired, and by producing a sequence of shots at spaced apart locations in the other borehole. The signals, detected by each of the geophones from the various shots, are processed either on a time of arrival basis, or on the basis of signal amplitude, to provide information of the characteristics of a large number of incremental areas between the boreholes. Such information is useable to produce a tomograph of the subsoil structure between the boreholes. By processing signals of relatively high frequencies, e.g., up to 100 Hz, and by closely spacing the geophones, a high resolution tomograph can be produced

Approximate maximum likelihood estimation of two closely spaced sources

The performance of the majority of high resolution algorithms designed for either spectral analysis or Direction-of-Arrival (DoA) estimation drastically degrade when the amplitude sources are highly correlated or when the number of available snapshots is very small and possibly less than the number of sources. Under such circumstances, only Maximum Likelihood (ML) or ML-based techniques can still be effective. The main drawback of such optimal solutions lies in their high computational load. In this paper we propose a computationally efficient approximate ML estimator, in the case of two closely spaced signals, that can be used even in the single snapshot case. Our approach relies on Taylor series expansion of the projection onto the signal subspace and can be implemented through 1-D Fourier transforms. Its effectiveness is illustrated in complicated scenarios with very low sample support and possibly correlated sources, where it is shown to outperform conventional estimators

GLRT-Based Framework for the Multidimensional Statistical Resolution Limit

International audienceRecently, a criterion for Multidimensional Statistical Resolution Limit (MSRL) evaluation, which is defined as the minimal separation to resolve two closely spaced signals depending on several parameters, was empirically proposed in [1] but without a statistical analysis. In this paper, we fill this lack by demonstrating that this MSRL criterion is asymptotically equivalent (upon to a translator factor) to a UMP (Uniformly Most Powerful) test among all invariant statistical tests. This result is an extension of a previous work on mono-dimensional SRL (i.e., when the signals only depend on one parameter). As an illustrative example, the 3-D harmonic retrieval case for wireless channel sounding is treated to show the good agreement of the proposed result

Temporal Leakage and Its Effects on Resolution in Deconvolution of Ultrasonic Signals

A common problem in ultrasonic nondestructive evaluation is the limitation imposed by the oscillatory nature of the ultrasonic pulse. Echoes from closely spaced reflectors often overlap and interfere, obscuring the true nature of the defect or layered system. Many deconvolution methods have been developed to remove the oscillatory response of the transducer from the received ultrasonic echo thereby improving the temporal resolution. While these methods have worked well on simulated signals, the results on real data have generally been much poorer [1-7]. Ultrasonic pulse shape variations, nonlinear effects or the breakdown of other model assumptions all contribute to this lower performance on real signals. An additional problem which has undergone little investigation is temporal leakage; it is very common and produces inaccuracy in the position and amplitude of deconvolved features. An understanding of its nature may help to improve resolution when deconvolving real signal

A Spatially Distributed Fiber-Optic Temperature Sensor for Applications in the Steel Industry

This paper presents a spatially distributed fiber-optic sensor system designed for demanding applications, like temperature measurements in the steel industry. The sensor system employed optical frequency domain reflectometry (OFDR) to interrogate Rayleigh backscattering signals in single-mode optical fibers. Temperature measurements employing the OFDR system were compared with conventional thermocouple measurements, accentuating the spatially distributed sensing capability of the fiber-optic system. Experiments were designed and conducted to test the spatial thermal mapping capability of the fiber-optic temperature measurement system. Experimental simulations provided evidence that the optical fiber system could resolve closely spaced temperature features, due to the high spatial resolution and fast measurement rates of the OFDR system. The ability of the fiber-optic system to perform temperature measurements in a metal casting was tested by monitoring aluminum solidification in a sand mold. The optical fiber, encased in a stainless steel tube, survived both mechanically and optically at temperatures exceeding 700◦C. The ability to distinguish between closely spaced temperature features that generate information-rich thermal maps opens up many applications in the steel industry

Ultra-wideband phased array radar for short-range imaging applications

Includes abstract.Includes bibliographical references (p. 105-108).Ultra-wide band (UWB) technology, as defined by the Federal Communication Commission (FCC) on February 2002, refers to signals or systems that have bandwidth ≥500 MHz or instantaneous fractional bandwidth ≥0.20 [2]. Compared to the conventional narrowband radar that operates with the same centre frequency, UWB radar offers many advantages, including high spatial resolution, for detecting closely-spaced target; and lower probability of interception, for stealth-like military application. There are many types of UWB waveform. The most obvious and simplest-to-generate UWB waveform is the impulse or short pulse. The pulse width of these impulses is usually sub-nanosecond, which enable a range resolution of 15 cm or less, when it is being transmitted in free space

Deterministic Cramer-Rao bound for strictly non-circular sources and analytical analysis of the achievable gains

Recently, several high-resolution parameter estimation algorithms have been developed to exploit the structure of strictly second-order (SO) non-circular (NC) signals. They achieve a higher estimation accuracy and can resolve up to twice as many signal sources compared to the traditional methods for arbitrary signals. In this paper, as a benchmark for these NC methods, we derive the closed-form deterministic R-D NC Cramer-Rao bound (NC CRB) for the multi-dimensional parameter estimation of strictly non-circular (rectilinear) signal sources. Assuming a separable centro-symmetric R-D array, we show that in some special cases, the deterministic R-D NC CRB reduces to the existing deterministic R-D CRB for arbitrary signals. This suggests that no gain from strictly non-circular sources (NC gain) can be achieved in these cases. For more general scenarios, finding an analytical expression of the NC gain for an arbitrary number of sources is very challenging. Thus, in this paper, we simplify the derived NC CRB and the existing CRB for the special case of two closely-spaced strictly non-circular sources captured by a uniform linear array (ULA). Subsequently, we use these simplified CRB expressions to analytically compute the maximum achievable asymptotic NC gain for the considered two source case. The resulting expression only depends on the various physical parameters and we find the conditions that provide the largest NC gain for two sources. Our analysis is supported by extensive simulation results.Comment: submitted to IEEE Transactions on Signal Processing, 13 pages, 4 figure

Application of ARMA modeling to multicomponent signals

This paper investigates the problem of estimating the parameters of a multicomponent signal observed in noise. The process is modeled las a special nonstationary autoregressive moving average (ARMA) process. The parameters of the multicomponent signal are determined from the spectral estimate of the ARMA model The spectral lines are closely spaced and the ARMA model must be determined from very short data records. Two high-resolution ARMA algorithms are developed for determining the spectral estimates. The first ARMA algorithm modifies the extended Prony method to account for the nonstationary aspects of noise in the model.For comPonents signals with good signal to noise ratio (SNR) this algorithm provides excellent results, but for a lower SNR the performance degrades resulting in a loss in resolution. The second algorithm is based on the work of Cadzow. The algorithm presented overcomes the difficulties of Cadzow's and Kaye's algorithms and provides the coefficients for the complete model not just the spen ral estimate. This algorithm performs well in resolving multicomponent signals when the SNR is low