David W. Krout

Estimation of position from multistatic Doppler measurements

We implement and evaluate a method infer position from Doppler measurements in a multistatic sonar scenario and present a likelihood approach for doing so. Doppler measurements are used to create likelihood surfaces for each of the transmitter-receiver pairs. The likelihood surfaces are combined and can then be used as-is or combined with additional position measurements. The final likelihood surface is usable in a Bayesian-style tracker or can be used to estimate position of a contact for use in a contact-based tracker. We show how the estimate improves with the addition of multiple receivers and show how the use of Doppler information can improve tracking results.

Likelihood surface preprocessing with the JPDA algorithm: Metron data set

This paper presents tracking results on the Metron data set using the JPDA algorithm and a preprocessing likelihood surface formulation. The Metron data set is a simulated data set and is designed to be very difficult with large bearing and range errors which leads to high localization error for true detections. There are also significant amounts of clutter. Results using other data association algorithms such as the PDA, PDAFAI, and PDAFAIwTS were not good, which led to the use of a likelihood surface. The preprocessing step using the likelihood surface is key for achieving reasonable results. For the baseline tracking scenario where the truth is known, the results were encouraging. Extending this technique to include acoustic modeling and Doppler information will be topics of future research.

Probability of Target Presence for Multistatic Sonar Ping Sequencing

In this communication, the problem of determining effective pinging strategies in multistatic sonar systems with multiple transmitters is addressed. New algorithms are presented to determine effective pinging strategies for generalized search scenarios. An important part of this work is the development of metrics to be used in the optimization procedures. For maintaining search coverage, a ldquoprobability of target presencerdquo metric formulation is used. This formulation utilizes sonar performance prediction and a Bayesian update to incorporate negative information (i.e., searching an area but finding no targets) into the optimization procedure. The possibility of targets moving into previously searched areas is accounted for by using a Fokker-Planck (FP) drift/diffusion formulation. Monte Carlo simulations are used to show the accuracy and efficiency of this formulation. This formulation is shown to be computationally efficient compared to Monte Carlo simulations. It is also demonstrated that by choosing the ping sequence intelligently, the field performance can be improved compared to random or sequential ping sequencing.

Multi-Static Sonar Tracking Incorporating Environmentally-Adaptive SNR Estimates

This paper discusses an approach for improving tracking in a multi-static sonar field. Improved SNR estimates for contacts are used in a multiple target tracking algorithm. This paper presents comparative results for simulated and real data using a probabilistic data association (PDA) algorithm and PDA with amplitude information (PDAFAI)

Improving target tracking performance by incorporating classification information

This paper presents approaches for incorporating classification information into target tracking algorithms, specifically in a multistatic active sonar context. In addition, this paper describes the framework designed for simulation and classification of return time series from simulated targets and clutter in a realistic underwater environment. The simulated target and clutter returns are integrated into an existing contact-based tracking dataset (TNO Blind dataset) for which time series are unavailable. Simulations compare the integrating classification of contacts at different stages of tracking algorithms. Results show improvements in some tracking metrics with no degradation of the others.

Track state augmentation for estimation of probability of detection in multistatic sonar data

This paper presents results of augmenting the track state with an amplitude offset to predict the probability of detection for a target moving through a multistatic field. The amplitude offset in the state allows for the local modeling of the environment, accounting for environmental modeling errors, and differentiating between target types. The approach is evaluated on the PACsim multistatic sonar dataset, a simulated dataset created for tracker evaluation by the Multistatic Tracking Working Group. Tracking and data association are done using Monte Carlo Joint Probabilistic Data Association, which is a particle-filter based implementation of JPDA. Results on the simulated data suggest that improved modeling must be done for this approach to be viable.

Contact clustering and classification using likelihood-based similarities

This paper presents the results of using a likelihood-based clustering step before tracking on a multistatic sonar step. The likelihood-based clustering appropriately models the measurement noise and allows for the incorporation of features. The clustering step also allows for the rejection of clutter and fusion of the contact measurements within a cluster. After clustering, fusion and classification, the tracking results are improved over previous preprocessing methods. Results are shown for the three scenarios in the PACSim dataset.

Tracking drifting surface objects with aerial infrared and electro-optical sensors

Recently, researchers at the Applied Physics Laboratory at the University of Washington collected a unique dataset by suspending two cameras, one infrared and one electro-optical, from a balloon. This apparatus was then used to image objects drifting on the surface of Lake Washington. The authors took that data and built a processing stream to track the movements of those drifting surface objects.

Orthogonal transformation of output principal components for improved tolerance to error

Preprocessing of data to be learned by a neural network is typically done to improve neural network performance. Output processing is especially important since it directly affects the influence of error in the hidden layers on the error of the neural network output. Principal component analysis is a commonly used preprocessing method that can improve the network performance by reducing the output dimensionality and reducing the number of parameters in a neural network model. Transforming the principal components of the outputs with an orthonormal matrix prior to scaling can further improve network performance.

Posterior distribution preprocessing for passive DTV radar tracking: Simulated and real data

This work presents the results of a multistage tracking framework on two types of passive radar data. The framework consists of three stages: range/range rate tracking to reject clutter, a posterior distribution transmitter fusion step, and a JPDA-based tracker. The overall system is applied to a simulated passive DTV radar dataset which contains two sets of SFN transmitters. The simulated dataset includes range, range rate, and azimuth measurements. We also present results on a new passive DTV radar dataset, which includes multiple transmitters on different frequencies.