Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water

It is common for passive detection systems to have multiple detections over a given time interval. The focus of this research is the simultaneous inversion for environmental parameters and the localization of multiple moving broadband sources in shallow water environments. Individual sources are isolated using track beam methods on array subapertures. The subaperture track beam spectra are then input as element data into an inversion approach that uses simulated annealing to estimate both environmental and source parameters. Environmental parameters include those associated with the seabed and source parameters include source depth, initial range, initial bearing, speed, and course. A key concept is that of space‐time processing manifested in the cost function by coherent sums of product spectra over frequency, receiver pairs, and time sequence. Insight into the problem is obtained with simulated data by examining the coupling between individual parameters, including the coupling between source and environmental parameters. [Research supported by ONR.]

Estimating the sound speed profile by matching the acoustic intensity striation using a mounted horizontal line array is investigated. Due to constructive and destructive interference between normal modes, the acoustic intensity manifests range-frequency striation structure in shallow water. Using the environment and acoustic data measured during a tomography experiment conducted in the South China Sea in June, 2010, the possibility and validity of inverting for the water column sound speed profile by matching the acoustic intensity striations are studied. The results show that the fluctuation of the sound speed profile has obvious influence on the acoustic intensity striations. The acoustic data recorded in an experiment conducted in the South China Sea in June 2010 are analyzed to invert for the sound speed profiles. The sound speed profile is represented by Empirical Orthogonal Functions (EOFs) to reduce the unknown parameters. The estimates agree well with the measured sound speed profiles and most of the standard deviations are less than 1 m/s.

This paper considers probabilistic prediction of the future locations of a moving acoustic source in the ocean based on past locations as determined by Bayesian source tracking in an uncertain environment. The Bayesian tracking approach considers both source and environmental parameters as unknown random variables constrained by noisy acoustic data and prior information and numerically integrates the posterior probability density (PPD) over the environmental parameters to obtain a time‐ordered sequence of joint marginal probability surfaces over source range and depth. The integration is carried out using Markov‐chain Monte Carlo sampling methods which provide a large collection of track realizations drawn from the PPD. Applying a probabilistic model for source motion to each of these realizations produces a sequence of source range‐depth probability distributions for future times. These predictions account for both the uncertainty of the source‐motion model and the uncertainty in the state of knowledge o...

Energy efficiency is a primary problem in wireless sensor networks which employ a large number of intelligent sensor nodes to accomplish complicated tasks. Focused on the energy consumption problem in target tracking applications, this paper proposes a dynamic energy management mechanism with an improved particle filter prediction in wireless sensor networks. The standard particle filter is improved by combining the radial-basis function network to construct the process model and the novel algorithm is adopted to predict the prior position of target. For dynamic awakening, the idle interval of each sensor node is estimated according to its sensing tasks. A cluster head rotating approach is introduced from low-energy adaptive clustering hierarchy for collecting data through the large sensing field. A group of sensor nodes which are located in the vicinity of target will wake up and have the opportunity to report their data. Distributed genetic algorithm is performed on cluster heads to optimize the sensor node selection. In target tracking simulations, we verify that the improved particle filter has more robustness than standard particle filter against the sensing error and dynamic energy management enhances energy efficiency of wireless sensor networks.

In December 2013, an experiment using an autonomous underwater vehicle (AUV) as a moving source for water column sound speed profile (SSP) inversion was conducted in Mogan Lake, China. With in-situ conductivity, temperature, and depth (CTD) measurements, the empirical orthogonal functions were first constructed to represent the SSP. Radiated noises of the AUV were received on a vertical line array and processed via acoustic field matching to obtain estimates of the range-independent SSP, along with the position and velocity of the AUV and the water column depth. The frequency of the radiation noise of the AUV was approximately 14 kHz. At such high frequency, even though the source motion was slow, a significant Doppler shift/broadening was observed. To incorporate the Doppler effects to better match the measured data, a forward acoustic model is derived based on the waveguide Doppler and normal mode theory. An analytical solution of the forward model is obtained for arbitrary signal integration intervals with a monochromatic source, moving radially relative to the receiver. Through simulations and experimental data processing, the feasibility of using an AUV source for water column SSP inversion has been demonstrated; it is also shown that the waveguide Doppler model is more effective compared with the model that does not consider the waveguide Doppler effect.

The performance of a hydrophone array can be evaluated by its coherent gain, which depends on the spatial correlation of both the signal of interest and the background noise between different array elements, where one hopes to maximize the former while minimizing the latter with array signal processing. In this paper, a computational vertical noise coherence map of the first zero-crossing is generated near Alvin Canyon, south of Martha’s Vineyard, Massachusetts, to study its dependence on the spatial variation in bathymetry, water column sound speed and sediment type. A two and three-dimensional Parabolic Equation propagation model based on reciprocity theory were used for the simulation. The results showed that the seabed parameters have the greatest impact on vertical noise coherence at the array location in the Alvin Canyon area, when compared to 3-D bathymetric and water column sound speed profile variability, especially in the shallower water. The analysis reveals the ideal spacing for a vertical hydrophone array for better signal detection in acoustic experiments. In the continental shelf and slope regions, the ideal spacing lies between 3λ⁄8 in deep water and λ⁄2 in shallow water, and for areas with strong bathymetric variations the ideal spacing can be determined by comprehensive numerical models.

In this paper, we propose three particle filters which are standard particle filter and two Gaussian particle filters for nonlinear system with missing measurements. For standard particle filter, we derive an explicit expression for the importance weights when the possible occurrence of measurement loss is taken into account. Based on this importance weights, a modified standard particle filtering algorithm with missing measurements is proposed. To improve sampling efficiency, we also propose two Gaussian particle filters for nonlinear system with missing measurements. For Gaussian particle filter, we derive the formulas for the importance density function when take the missing measurements into account. To fulfill the numerical computation of these formulas, we give two approximated methods based on local linearization and unscented transform. Based on these two approximated methods, the two Gaussian particle filtering algorithms with missing measurements are proposed. The effectiveness of the proposed methods are illustrated through a nonlinear simulation example.

This work attempts to exploit the difference in sound speed between oil and seawater to determine the location of large underwater oil plumes. Two perturbative inversion schemes, both based on the work of Rajan, et al. [JASA 82, 998–1017 (1987)] will be presented. Both methods can be used to estimate sound‐speed in the water column as a function of range and depth. The first technique uses horizontal wavenumber data which are estimated from the complex pressure field of a narrow band signal. The inversion result provides an estimate of sound speed profile along a two‐dimensional slice through the environment. The method is demonstrated using data from the Shallow Water 2006 (SW06) experiment. The second technique utilizes modal travel time data. When applied in a multi‐sensor deployment, the outputs of this inversion scheme provide a three‐dimensional characterization of the coastal environment. Furthermore, because sound propagates at a much faster speed than oil spill spreads, this technique can be used to track changes in the size and shape of the oil plume over time. [Work supported by ARL:UT IR&D]

The objectives of this paper are to analyze the effectiveness of parameters on sound propagation in a shallow-water environment. The procedure for calculation of transmission loss is only the method to analyze the influence of environmental parameters. The normal mode approach is carried out for the calculation of transmission loss. And it is conducted in the range independent environment Transmission loss for sound propagation in shallow water depends upon many natural variables such as sea surface, water medium, and sea bottom. Analyses are finalized on the results obtained by considering two types of sound channels. The results indicated that acoustic transmission loss in a shallow-water environment is dependent on the source & receiver depths, sea surface, sound speed profile (SSP) in water, sound speed in bottom, density of water & bottom, propagation range and frequency. It is necessary to mention that better transmission was found during the sound velocity increases with depth; whereas the poor transmission occurred in negative gradient channel.

SummaryThis paper addresses the problem of nonlinear time‐varying state and parameter estimation of induction machines (IMs) on the basis of a third‐order electrical model. The objectives of this paper are threefold. The first objective is to propose the use of an improved particle filter (IPF) with better proposal distribution for nonlinear and non‐Gaussian state and parameter estimation. The second objective is to extend the state and parameter estimation techniques (i.e., extended Kalman filter (EKF), unscented Kalman filter (UKF), particle filter (PF), and IPF) to better handle nonlinear and non‐Gaussian processes withouta prioristate information, by utilizing a time‐varying assumption of statistical parameters. In this case, the state vector to be estimated at any instant is assumed to follow a Gaussian model, where the expectation and the covariance matrix are both random. The third objective is to compare the performances of EKF, UKF, PF, and IPF in estimating the states of the power process model representing the IM (i.e, the rotor speed, the rotor flux, the stator flux, the rotor resistance, and the magnetizing inductance) and their abilities to estimate some of the key system parameters, which are needed to define the IM process model. The results show that the IPF provides a significant improvement over the PF because, unlike the PF, which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. This conclusion is also supported by the experimental results. Copyright © 2014 John Wiley & Sons, Ltd.

The fusion of multiple Computer Aided Detection/Computer Aided Classification (CAD/CAC) algorithms has been shown to be effective in reducing the false alarm rate associated with the automated classification of bottom mine-like objects when applied to side-scan sonar images taken in Very Shallow Water (VSW) environments [C.M. Ciany et al., 2001],[C.M. Ciany et al., 2000]. The fusion of CAD/CAC algorithms from Raytheon and NSWC Coastal Systems Station (CSS) also has been demonstrated in the shallow water environment on a single sonar data set [C.M. Ciany et al., 2002]. This paper extends the shallow water CAD/CAC/Fusion performance analysis to an additional set of sonar data taken in the Gulf of Mexico during June 1998, and adds the outputs of a third CAD/CAC algorithm from Lockheed Martin to the fusion processing. The fusion algorithm accepts the classification confidence levels and associated contact locations from the three different CAD/CAC algorithms, clusters the contacts based on the distance between their locations, and then declares a valid target when a clustered contact passes a prescribed fusion criterion. Four different fusion criteria are evaluated: the first based on the Fisher Discriminant, the second and third based on simple and constrained binary combinations of the multiple CAD/CAC processor outputs, and the fourth based on a constrained optimization approach that minimizes the total number of false alarms over the clustering distance and cluster confidence factor thresholds for a given probability of correct classification. The resulting performance of the four fusion algorithms is compared, and the overall performance benefit of a significant reduction of false alarms at high correct classification probabilities is quantified.

Generally, most inversion approaches model the seabed as a stack of range-independent homogeneous layers with unknown geoacoustic parameters and layer numbers. In our previous study, we established a layered geoacoustic seabed model based on sub-bottom profiler data to characterize low-frequency (100–500 Hz) airgun signal propagation at short ranges (0–20 km). However, when applying the same model to simulate high-frequency (500–1000 Hz) explosive sound signal propagation, it failed to adequately reproduce the observed significant transmission loss phenomenon. Through systematic analysis of transmission loss (including water column sound speed profiles, seabed topography, and sediment properties), this study proposes a range-dependent layered geoacoustic model using the Range-dependent Acoustic Model–Parabolic Equation (RAM-PE). Stepwise inversion implementation has successfully explained the observed experimental phenomena. To generalize the proposed model, this study further introduces a trans-dimensional inversion framework that automatically resolves sediment property interfaces along propagation paths. The method effectively combines prior information with trans-dimensional inversion techniques, providing improved characterization of range-dependent seabed environments.

Most of the countries have paid great attention to flood water level monitoring and tracking because flood may damages people's life and property. Since flood water level fluctuate highly nonlinear, it is very difficult to predict the flood water level. The particle filter algorithm is well known as a very effective solution for handling nonlinear problems. Thus, in this paper, this algorithm is applied to predict the flood water level. There are many variations of particle filter. This paper proposes Sequential Importance Sampling (SIS) particle filter to solve the above mentioned problem. SIS is the basic particle filter. However, the problems with SIS particle filter are the particle degeneration phenomenon, when after a few iterations only a few particles have nonzero weight. So, Sampling Importance Resampling (SIR) particle filter is also introduced as the improved particle filter. From the simulation results using Matlab, SIR particle filter outperforms SIS particle filter by comparing the Root Mean Square Error (RMSE) value.

Seismic source parameters are one of the important specifications for a seismic survey. Conventional thinking is that larger sources produce higher energy, and thereby improve data quality. However, in shallow water areas offshore Abu Dhabi, United Arab Emirates (U.A.E.), the source size is limited due to operational constraints such as shallow water depth and several scattered production facilities. To address these issues, a pilot seismic survey was acquired in a shallow water area with the main objectives: to determine data quality versus source parameters; and to optimize source parameters for future seismic surveys. In this survey, several datasets were acquired with different source parameters, however, using the same source vessel and air-gun array. Then, data quality for each dataset was evaluated at various processing stages. We show that shorter shot-point interval (SPI) and appropriate processing, e.g. noise attenuation in a domain with a closer trace interval, could compensate for a lack of source energy, then even a small source could achieve the required data quality with the aid of suitable SPI and processing. The results should encourage investigating even smaller sources which are easier for the operations and friendlier to the environment.

Induction machine is highly nonlinear model with states that change with operating point and temperature. In these cases, estimating these variables from other easily obtained measurements can be extremely useful. This paper deals with the problem of state estimation of induction machine on the basis of a third-order electrical model using Bayesian methods. The performances of Bayesian estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), the unscented Kalman filter (UKF), the particle filter (PF), and the developed improved particle filter (IPF). The estimation results, which are validated using simulations, show that IPF provides improved estimation performance over PF, even with abrupt changes in estimated states, and both of them can provide improved accuracy over UKF and EKF. These advantages of the IPF are due to the fact that it uses a better proposal distribution that takes the latest observation into account.