Where you can find the array signal processing easily? Is it in the book store? On-line book store? are you sure? Keep in mind that you will find the book in this site. This book is very referred for you because it gives not only the experience but also lesson. The lessons are very valuable to serve for you, that's not about who are reading this array signal processing book. It is about this book that will give wellness for all people from many societies.

Array signal processing

Ieee transactions® on aerospace and electronic systems

Particle filter (PF) based multi-target tracking (MTT) methods suffer from the curse of dimensionality. Existing strategies to combat this assume posterior independence between target states, in order to then sample targets independently, or to perform joint sampling of closely spaced targets only. When many targets are in proximity, these strategies either perform poorly or are too computationally expensive. We make two contributions towards addressing these limitations. Firstly, we advocate an alternative view of the use of posterior independence which emphasizes the statistical effect of assuming posterior independence on the Monte Carlo (MC) approximation of posterior density. Our analysis suggests that assuming posterior independence can provide a better MC approximation of the prior distribution at the next time, and therefore the posterior at the next time, without regard for how sampling is performed. Secondly, we present a computationally efficient, measurement directed, joint sampling method to cope with the target coupling and measurement ambiguity when targets are near each other. Consequently, we develop a PF which employs posterior independence while sampling targets jointly. This PF is applicable to both the traditional thresholded and track-before-detect style pixelized models. Simulation results for a challenging tracking scenario show that the proposed PF substantially outperforms existing approaches.

A Computationally Efficient Particle Filter for Multitarget Tracking Using an Independence Approximation

Particle filtering based track-before-detect method for passive array sonar systems

In this paper, we consider the problem of tracking multiple targets in a passive multi-static radar system using Doppler-only measurements. The number of targets is assumed unknown and time-varying. The Doppler measurements are subject to additive noise, clutter, and missed detections. Doppler-only measurements from a single sensor provide incomplete information about the target state, commonly referred to as single-sensor unobservability. In a passive multi-static radar system, the availability of multiple bistatic links naturally lends itself to the fusion of measurements from spatially distributed sensors. However, data fusion emerges as a computationally intensive problem in multi-sensor multi-target tracking algorithms. We propose a two-step sequential approach to solve the underlying problem. We first cast the underlying problem as a group sparse problem in a discretized position-velocity space. A group sparsity based algorithm is applied to simultaneously exploit the multi-static Doppler frequency measurements to directly obtain the instantaneous target state estimates in the Cartesian coordinate system. These estimates are then fed as inputs to the linear Gaussian mixture probability hypothesis density (GMPHD) filter, which removes the false measurements, compensates for missed detections and reduces the localization error. The optimal sub-pattern assignment metric, which jointly comprises a weighted contribution of cardinality error and localization error, is used to evaluate the performance of the proposed method. Simulation results show that the proposed method successfully handles the multi-target tracking problem and outperforms the existing random receiver selection based multi-sensor implementation of the GMPHD filter.

Group Sparsity Based Multi-Target Tracking in Passive Multi-Static Radar Systems Using Doppler-Only Measurements

This paper describes a Bayesian approach to detecting and tracking multiple moving targets using acoustic data from multiple passive arrays. We describe a surveillance application, where a collection of fixed-location passive acoustic arrays is charged with monitoring a predefined spatial region. Our approach combines a unique hybrid discrete-grid/particle approximation to the posterior with a dynamic density factorization. This results in a novel 2-D (X/Y) multisensor multitarget tracker that uses bearing measurements only. The efficacy of the algorithm is illustrated both in simulation and on collected at-sea data.

/pdf/multitarget-detection-and-tracking-using-multisensor-passive-j6so4nveuw.pdf

Multitarget Detection and Tracking Using Multisensor Passive Acoustic Data

This paper describes a Bayesian approach to detecting and tracking multiple moving targets using acoustic data from multiple passive arrays. Traditional undersea acoustic systems develop tracks at the single array level, requiring track association between nodes with nonlinear projections from measurement space to target space. In contrast, our nonlinear filtering approach fuses data at the measurement level and operates directly in the target state space. As such, this approach directly addresses both the nonlinear sensor to target state coupling as well as the ambiguities caused by bearings-only nature of the passive regime. In particular, our method better addresses these challenges by combining high-fidelity physics-based sensor statistical modeling, an innovative nonlinear Bayesian filter, and a unique method of handing the computational implementation.

/pdf/multitarget-detection-and-tracking-using-multi-sensor-1v3xccp3gj.pdf

Multitarget detection and tracking using multi-sensor passive acoustic data

The PDF tracker first entered Navy passive sonar in the Passive Tracking Algorithm (PTA) Air ASW program. Since then, it has fulfilled the role of frequency line tracker (frequency versus time surface) as well as broadband (bearing vs. time) tracker in various fleet systems. These deployments have included both stand-alone implementations and also integration with other tools. Examples are TRAFS and WSQ-11 in the surface community, as well as HPT, HCF, IPT, and ICF on submarine systems.

An Overview of the Probability Density Function (PDF) Tracker

PDF target detection and tracking

Tracking prominent scatterers provides a mechanism for scene-derived motion compensation of Synthetic Aperture
Radar (SAR) data. Such a process is useful in environments where GPS is unavailable and a lack of precise sensor
position data makes standard motion compensation difficult. Our approach to sensor positioning estimates range
histories of multiple isolated scatterers with high accuracy, then performs a geometric inversion to locate the scatterers in
three dimensions and estimate the platform's motion.
For high-accuracy scatterer range tracking, we first detect prominent scatterers using a CFAR criterion automatic
algorithm and then track them with a two-input Kalman Filter (KF) operation. These two steps provide accurate range
estimates of multiple scatterers over a sequence of SAR pulses. The KF state space is range and range-rate. We derive
data inputs to the KF algorithm from multiple SAR pulses, divided into Coherent Processing Intervals (CPI). Within
each CPI, individual scatterer peak amplitudes and phases are available to the algorithm.
Our approach to scene-derived motion compensation combines the high accuracy range history estimates with a novel
three-dimensional geometric inversion. This geometric inversion uses the range histories to estimate both 3D scatterer
location and 3D relative motions of the radar. We illustrate our KF-based approach to high-accuracy tracking and
demonstrate its application to estimating scene scatterer locations on synthetic and real collected SAR data.

/pdf/detection-and-tracking-of-prominent-scatterers-in-sar-data-8das9m5ppr.pdf

Detection and tracking of prominent scatterers in SAR data

Popular Tasks used by Researchers

How can I help with your research?

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

We encountered an error while loading the panel. Please try refreshing the page.

Something went wrong

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

Anyone with the link can view this chat. You can switch it back to private anytime.

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.

Benjamin Shapo

Author Tools

Chat about Author