Academic literature on the topic 'Broadband ambiguity function'

This paper considers methods for estimating the mutual time delay of broadband signals recorded by satellites based multi-position systems for determining the location of a radiation source. All methods considered are based on modified algorithms for calculating the ambiguity function. The presented algorithms are based on the extraction of narrowband channels from the studied signals and their further optimal processing. The reliability criterion for mutual time delay estimation by the presented methods was evaluated. Based on the results and analysis of computational efficiency, viability of methods considered and their modifications was determined.

The study analysed possible resolution of two ground-based MIMO microwave camera designs by means of two-dimensional spatial cross-sections of a generalized ambiguity function. We used stepped-frequency modulated broadband signals as time-orthogonal probing signals for the microwave camera transmitters. We employed a mathematical simulation of the space-time signal processing system pertaining to MIMO microwave camera receivers to illustrate 2D and 3D microwave imaging of stationary extended multipoint objects situated at various altitudes above the earth surface.

This paper studies resolution of matched field processing for locating, in range and depth, a broadband underwater acoustic source from data measured at a single hydrophone receiver. For the case of an ideal rigid shallow-water waveguide with a pressure-release top boundary and a rigid bottom boundary, we derive approximations for the main-lobe widths of the ambiguity surface. The two cases studied in this paper are (1) when coherent measurements of the pressure are available, with the transmitted source waveform precisely known, and (2) when only measurements of the received signal power spectral density (PSD) are available, such as occur when the transmitted signal is random and unknown. The analysis uses the normal-mode expansion for the pressure field to derive approximate expressions for the ambiguity surface main-lobe widths, as a function of the number of modes and frequency band, for both range and depth. Numerical results are presented corroborating the analytical analysis. Finally, we argue that this ambiguity analysis also gives insights into real ocean waveguide localization characteristics under appropriate conditions, and show numerical simulations of matched field localization ambiguity surfaces for some realistic shallow-water Pekeris environments.

This paper studies resolution of matched field processing for locating, in range and depth, a broadband underwater acoustic source from data measured at a single hydrophone receiver. For the case of an ideal rigid shallow-water waveguide with a pressure-release top boundary and a rigid bottom boundary, the paper derives approximations for the main-lobe widths of the ambiguity surface. The two cases studied in this paper are (1) when coherent measurements of the pressure are available, with the transmitted source waveform precisely known, and (2) when only measurements of the received-signal pressure magnitude-squared are available, such as might occur when the transmitted signal is random and unknown. The analysis uses the normal-mode expansion for the pressure field to derive approximate expressions for the ambiguity-surface main-lobe widths, as a function of the number of modes and frequency band, for both range and depth. Numerical results are presented corroborating the analytical analysis. Finally, the paper argues that this ambiguity analysis can also give insights into Pekeris waveguides under appropriate conditions and shows numerical simulations of matched-field localization ambiguity surfaces for some realistic shallow-water Pekeris environments.

Syntax, the combination of meaning-devoid phonemes into meaningful words, which in turn are combined in structurally and semantically complex sentences, is fundamental to the unlimited expressiveness of human languages. Studying the functions of call combinations in non-human species provides insights into the evolution of such syntactic capabilities. Here, we investigated the combination of high amplitude broadband calls with low frequency rumble vocalizations in a highly social species, the African forest elephant Loxodonta cyclotis. Rumbles play an integral role in coordinating social interactions by transmitting socially relevant information, including individual identity. By contrast, broadband calls, such as roars, are thought to function as signals of distress and urgency as they are typically produced in situations of high emotional intensity. Functional changes associated with the combination of these calls remain little understood. We found that call combinations were produced by all age-sex classes but were most prevalent in immature individuals. We found that rumbles used singularly occurred in all five investigated social contexts, whereas single broadband calls were restricted to two resource-related contexts. Call combinations also occurred in all five contexts, suggesting an increase in the functional use of broadband calls when combined with rumbles, analogous to the generativity brought about through syntax in human speech. Moreover, combining calls appeared to lead to functional shifts towards high-stake contexts. Call combinations were more likely in competition contexts compared to single rumbles, and more likely in separation contexts compared to single broadband calls. We suggest that call combination in forest elephants may aide to reduce message ambiguity in high-stake situation by simultaneously communicating distress and individual identity, which may be critical to secure access to resources, reduce the risk of injury and to reunite with or recruit the support of the family group.

Broadband acoustic Doppler current profiler (ADCP) is widely used in agricultural water resource explorations, such as river discharge monitoring and flood warning. Improving the velocity estimation accuracy of broadband ADCP by adjusting the waveform parameters of a phase-encoded signal will reduce the velocity measurement range and water stratification accuracy, while the promotion of stratification accuracy will degrade the velocity estimation accuracy. In order to minimize the impact of these two problems on the measurement results, the ADCP waveform optimization problem that satisfies the environment constraints while keeping high velocity estimation accuracy or stratification accuracy is studied. Firstly, the relationship between velocity or distance estimation accuracy and signal waveform parameters is studied by using an ambiguity function. Secondly, the constraints of current velocity range, velocity distribution and other environmental characteristics on the waveform parameters are studied. For two common measurement applications, two dynamic configuration methods of waveform parameters with environmental adaptability and optimal velocity estimation accuracy or stratification accuracy are proposed based on the nonlinear programming principle. Experimental results show that compared with the existing methods, the velocity estimation accuracy of the proposed method is improved by more than 50%, and the stratification accuracy is improved by more than 22%.

Les contraintes de rapidité et de discrétion imposées à un système moderne de caractérisation du milieu océanique ont conduit au développement de la tomographie passive, définie comme un moyen discret et rapide d'estimation des paramètres d'un canal acoustique. Ce concept fait appel aux signaux existants dans le milieu et transmis par des sources d'opportunité. Les signaux d'opportunité sont inconnus à la réception mais contiennent des informations relatives aux paramètres physiques du canal défini entre la source et le récepteur. Le travail de recherche présenté dans ce mémoire est d´edié à la caractérisation des milieux sous-marins en utilisant des signaux bioacoustiques d'opportunité (sifflements à modulation fréquentielle). La méconnaissance du signal transmis, de la position et de la vitesse de la source acoustique d'opportunité rendent la tomographie passive difficile à mettre en oeuvre. La propagation dans l'environnement océanique et le mouvement inconnu de la source transforment conjointement les signaux d'opportunité enregistrés. Dans un premier temps, nous présentons de nouvelles méthodes d'estimation simultanée des paramètres environnementaux et des déformations engendrées par le mouvement dans le plan d'ambiguïté large-bande, dans un contexte d'émissions actives (le signal transmis est supposé connu). Ces méthodes, permettant de compenser les effets du mouvement dans les scénarios d'´emissions actives, sont appliquées et validées sur différents jeux de données simulées et réelles enregistrées en mer. Puis nous nous intéressons à la tomographie océanique acoustique passive sur un unique hydrophone. Dans ce contexte, le signal transmis, la position et la vitesse de la source sont entièrement inconnus. A partir des estimateurs développés pour les scénarios d'émissions actives, nous présentons une nouvelle méthodologie permettant d'estimer les paramètres environnementaux en utilisant des vocalises de mammifères marins enregistrées sur un unique hydrophone. Les informations extraites sur les signaux naturels d'opportunité sont ensuite utilisées pour estimer la position puis le vecteur vitesse de la source d'opportunité. Ces méthodes sont appliquées et validées sur différents jeux de données simulées et réelles enregistrées en mer
Quickness, secrecy and loudness constraints imposed by modern oceanic characterization led to passive tomography which is defined as a quick, secretive and quiet mean of estimating underwater propagation canals. This concept uses signals naturally existing in the medium and transmitted by opportunity sources. Opportunity signals are unknown at the receiver but they also carry information about canal physical properties. This research work is dedicated to underwater environments characterization using opportunity bioacoustic signals (dolphin whistles). Opportunity signals are simultaneously transformed by underwater propagation and the unknown motion effects. Firstly, we propose new methods for estimating simultaneously environmental parameters and transformations created by motion effects. These parameters are estimated in the broadband ambiguity plane for active tomography (the emitted signal is known) with unknown motion in the system. This work, allowing to compensate for motion effect in active scenarios, is validated on different simulated and real data. Then, we apply our signal processing methods to passive underwater tomography, using a single hydrophone. In this context, both the transmitted signal, source position and source speed are completely unknown. From the theory we developed for active tomography, we derive new methods allowing the estimation of impulse response using underwater mammals vocalization recorded on a single hydrophone. Information extracted on opportunity signals is then used for source position and speed estimation. These methods are applied and validated on different simulated and real data from at sea experiments

Linear optical processors (such as optical spectrum analyzers, correlators, optical Wigner processors, and ambiguity function processors)1 can rapidly extract classification features from wide bandwidth signals. However, with dimension-increasing processing (such as the ambiguity function) the output information rate can massively exceed the input capacity of digital computers used for classification. An optical classifier, such as an adaptive optical neural network,2 however, can potentially provide a throughput rate to match the output of the optical feature extractor. As a demonstration of this concept, a broadband communications signal classfier was constructed by cascading an acousto-optic spectrum analyzer with an adaptive holographic pattern classifier using a photorefractive crystal of Fe:LiNbOr. Experimental results obtained by using this two-stage processor asa shift invariant classifier are included. This configuration requires an error-driven learning pathway for weight modification to implement an adaptive classifier. A multilayer system to classify radar returns from an isolated aircraft by using an adaptive neural network classifying the output of an optical ambiguity function processor is proposed.

Downhole fluid analysis has the potential to resolve ambiguity in very complex reservoirs. Downhole fluid spectra contain a wealth of information to fingerprint a fluid and help to assess continuity. Commonly, a narrowband spectrometer with limited number of channels is used to acquire optical spectra of downhole fluid. The spectral resolution of this type of spectrometer is low due to limited number of narrowband channels. In this paper, we demonstrate a new type, compressive sensing (CS) based broadband spectrometer that provides accurate and high-resolution spectral measurement. Several specially designed broadband filters are used to simplify the mechanical, electrical, optical, and computational construction of a spectrometer, therefore provides measurement of fluid spectrum with high signal-to-noise ratio, robustness, and a broader spectral range. The compressive sensing spectrometer relies on reconstruction technique to compute the optical spectrum. Based on a large spectral database, containing more than 10000 spectra of various fluids at different temperature and pressure conditions, which were collected using conventional high resolution spectrometer in a lab, the basis functions of the optical spectra of three types of fluids (water, oil and gas/condensate) can be extracted. The reconstruction algorithm first classifies the fluid into one of three fluid types based on multichannel CS spectrometer measurements, the optical spectrum is reconstructed by using linear combination of the basis functions of corresponding fluid type, with weighting coefficients determined by minimizing the difference between calculated detector responses and measured detector responses across multiple optical channels. The reconstructed data may then be used for purposes such as contamination measurement, fluid property trends for reservoir continuity assessment, and digital sampling. Digital sampling is the process of extrapolating clean fluid properties from formation fluids not physically sampled. The reconstruction spectrum covers wavelengths from 500 nm to 3300 nm, which is a wider spectral region than has historically been accessible to formation testers. The expanded wavelength range allows access of the mid-infrared spectral region for which synthetic drilling-fluid components typically have higher optical absorbance. This reconstruction spectra may allow contamination to be directly determined. This paper will discuss the CS optical spectrometer design, fluid classification and spectral reconstruction algorithm. In addition, the applicability of the technique to fluid continuity assessment, sample contamination assessment and digital sampling will also be discussed.