A computationally efficient algorithm for parameter estimation of superimposed signals based on the two-step iterative EM (estimate-and-maximize, with an E step and an M step) algorithm is developed. The idea is to decompose the observed data into their signal components and then to estimate the parameters of each signal component separately. The algorithm iterates back and forth, using the current parameter estimates to decompose the observed data better and thus increase the likelihood of the next parameter estimates. The application of the algorithm to the multipath time delay and multiple-source location estimation problems is considered. >

/pdf/parameter-estimation-of-superimposed-signals-using-the-em-b1fg5f44tw.pdf

Parameter estimation of superimposed signals using the EM algorithm

Time delay estimation has been a research topic of significant practical importance in many fields (radar, sonar, seismology, geophysics, ultrasonics, hands-free communications, etc.). It is a first stage that feeds into subsequent processing blocks for identifying, localizing, and tracking radiating sources. This area has made remarkable advances in the past few decades, and is continuing to progress, with an aim to create processors that are tolerant to both noise and reverberation. This paper presents a systematic overview of the state-of-the-art of time-delay-estimation algorithms ranging from the simple cross-correlation method to the advanced blind channel identification based techniques. We discuss the pros and cons of each individual algorithm, and outline their inherent relationships. We also provide experimental results to illustrate their performance differences in room acoustic environments where reverberation and noise are commonly encountered.

/pdf/time-delay-estimation-in-room-acoustic-environments-an-2y9s0s5kf2.pdf

Time delay estimation in room acoustic environments: an overview

Worldwide, obstructions on watercourses have interfered with migratory pathways of fish species, reducing life-cycle success and often eliminating diadromous fish species altogether from river basins. Over the last century, efforts to mitigate these effects were initially directed at developing fishways for upstream, high-value migrant adult salmon. In more recent years, efforts have turned to developing fishways for other species. Results of past research suggest that the development of effective fishways requires biological knowledge of fish behaviour when encountering variable flows, velocity and turbulence, combined with hydraulic and civil engineering knowledge and expertise to develop facilities that provide appropriate hydraulic conditions that fish will exploit. Further, it often requires substantial financial resources for biological and hydraulic testing as well as engineering design, particularly where prior knowledge of the behaviour of target fish species does not exist. Where biological or engineering knowledge (or both) is absent, development of effective passage facilities must take on a trial and error approach that will almost certainly require years to attain success. Evaluations of existing adult and juvenile fish passage facilities, where they have been carried out, suggest that migrant fish reject areas with hydraulic conditions they determine unsuitable. Even well designed fish ladders or nature-like bypass channels for upstream migrants, even those with good attraction flows, will fail if incorrectly sited. Although progress has been made, developing successful installations for downstream migrants remains much more difficult, probably because downstream fish move with the flow and have less time to assess cues at entrances to any bypasses that they encounter. Copyright © 2011 John Wiley & Sons, Ltd.

https://hal.archives-ouvertes.fr/hal-00967134/document

Thinking like a fish: a key ingredient for development of effective fish passage facilities at river obstructions

Noninvasive species identification remains a longterm goal of fishers, researchers, and resource managers who use sound to locate, map, and count aquatic organisms. Since the first biological applications of underwater acoustics, four approaches have been used singly or in combination to survey marine and freshwater environments: passive sonar; prior knowledge and direct sampling; echo statistics from high-frequency measures; and matching models to low-frequency measures. Echo amplitudes or targets measured using any sonar equipment are variable signals. Variability in reflected sound is influenced by physical factors associated with the transmission of sound through a compressible fluid, and by biological factors associated with the location, reflective properties, and behaviour of a target. The current trend in acoustic target identification is to increase the amount of information collected through increases in frequency bandwidth or in the number of acoustic beams. Exclusive use of acoustics to identify aquatic organisms reliably will require a set of statistical metrics that discriminate among a wide range of similar body types at any packing density, and incorporation of these algorithms in routine data processing.

/pdf/acoustic-approaches-to-remote-species-identification-a-4ggtuu103y.pdf

Acoustic approaches to remote species identification: a review

Limitations of biotelemetry technology available in 2001 prompted the U.S. Army Corps of Engineers Portland District to develop a new acoustic telemetry system to monitor survival of juvenile salmonids through the Columbia River to the Pacific Ocean. Eight years later, the Juvenile Salmon Acoustic Telemetry System (JSATS) consists of microacoustic transmitters (12 mm long, 0.43 g weight in air), autonomous and cabled receiving systems, and data management and processing applications. Transmitter pulse rate can be user-defined and as configured for this case study was set at 5 seconds, with an estimated tag life of 30 days and detection range of 300 m. Before JSATS development, no technology existed to study movement and survival of fish smaller than 10 g migrating long distances from freshwater and into saltwater. In a 2008 study comparing detection probabilities, travel times, and survival of 4,140 JSATS-tagged and 48,433 passive integrated transponder (PIT)-tagged yearling Chinook salmon (Oncor...

The Juvenile Salmon Acoustic Telemetry System: A New Tool

Acoustic tag systems have been used for many years to study the behavior of fish in specific areas of interest. In particular tag systems are being used successfully to study the behavior of downstream migrating salmon smolts (Oncorhynchus spp.) as they approach hydro-electric dams. While field studies have demonstrated the potential for acoustic tag systems, little has been done to quantify their performance. This paper develops a method for predicting the accuracy of the “position estimates” provided by acoustic tag systems. General expressions are developed that can be applied to any particular deployment of a tag system which lead onto a method for the direct calculation of the “position error” as a function of hydrophone geometry, standard deviation of the signal arrival times, and the inaccuracies in the assumed sound velocities. This method is independent of the algorithm used to determine the position solution. Using the methods of analysis developed here some specific examples are presented that provide general guidelines that should be followed to achieve good performance when deploying an acoustic tag system.

A method for estimating the “position accuracy” of acoustic fish tags

There are many situations when it is important to know accurately the behavior of fish as a function of time and space in a fixed, three-dimensional volume. One example is the optimal design of techniques that minimize the mortality of fish approaching hydroelectric dams or the cooling intakes of a power plant. The behavior of fish in other fixed volumes, such as estuaries and open rivers, is also of interest in the case of many migrating fish stocks. Both active (echosounding) and passive systems based on acoustic-emitting tags implanted in fish have been used to collect behavioral data. Active acoustic systems, including those with electronically and mechanically steered beams, only insonify a small part of the total volume of interest at any given time. Tag systems, on the other hand, can be used to monitor the behavior of tagged fish over the entire volume. A number of advances in the implementation, deployment, and analysis of acoustic-tag systems have been made over the past few years. These improvements include techniques for positioning optimally the receiving hydrophones to minimize the location measurement errors, the development of acoustic-signal waveforms that provide both unique target identification and accurate location estimates, and the development of tracking algorithms that associate and track the multiple returns from an individual fish. These various techniques are described. Guidelines are presented for selecting the various parameters for the tag system, including the positions of the hydrophones. Specific examples that compare the predicted and actual performance of the tag systems are described.

/pdf/improved-techniques-for-studying-the-temporal-and-spatial-2h7ym2jhom.pdf

Improved techniques for studying the temporal and spatial behavior of fish in a fixed location

FM slide and pulsed‐tone signals at 2, 4, 8, and 16 kHz were transmitted from a source to a receiver at the same depth 1100 m distant. Three wholly refracted paths were observed in the return signals, and a discussion of the six‐hour time series of the arrival times and amplitudes for each path is presented. Evidence is given of acoustic frequency‐dependent scattering, and scattering from oceanic fine structure is suggested as the most likely physical mechanism to explain the observations.

Observations of the phase and amplitude of individual Fermat paths in a multipath environment

A recursive algorithm for estimating the spatial density of acoustic point scatterers is derived. This algorithm, which is based on the stochastic approximation estimation technique, is shown to be equivalent to an echo counter for low densities and to an energy detector or echo integrator for high densities. The problem of estimating fish density from a scattered acoustic signal is considered in detail The recursive algorithm is shown to have a mean‐squared error that is a factor of 6 lower than either an echo integrator or an echo counter in the medium density region.

Recursive algorithm for estimating the spatial density of acoustic point scatterers

Active hydroacoustic sampling techniques are widely used to monitor fish densities and behavior in aquatic environments. Since the 1970's, this technology has been used to assess a wide range of fisheries-related questions, including upstream and downstream migrations, stock assessments, behavioral evaluations, and others. When properly applied, hydroacoustics is a powerful and effective fisheries assessment tool, providing high sampling coverage that is unobtrusive and nonselective. Like any sampling technique, an understanding of the factors potentially influencing the ability to detect organisms and quantify the sampled volumes with a hydroacoustic system enhances the ability to accurately interpret the results and derive quantitative estimates. Several factors can affect the probability of detecting fish using hydroacoustics. These include: 1) the sampling environment; 2) hydroacoustic system data collection methodology and parameters; and 3) characteristics and distribution of the fish being monitored. Interactions between these factors can influence both the ability to detect fish of a given minimum size and the effective sampling volume of the hydroacoustic system. This paper describes methods for estimating the probability of detecting a fish within a hydroacoustic beam under different conditions. Examples demonstrating the effect of each of these parameters on the ability to detect fish using hydroacoustics are presented.

Development of a method for estimating the probability of detecting fish through a hydroacoustic beam

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

John E. Ehrenberg

Author Tools

Chat about Author