Yaakov bar-shalom

University of Connecticut | UConn · Electrical and Comp. Enrg.

Acoustic vector sensors provide a measurement of both the scalar acoustic pressure as well as the acoustic particle velocity vector in the orthogonal Cartesian directions, allowing for direction of arrival estimation of an acoustic source. Typically, acoustic vector sensors are used in array configurations in place of hydrophones to increase the ov...

This paper applies Maximum Likelihood Estimation (MLE) to the identification of a state-space model for inertial sensor drift. The discrete-time scalar state considered is either a first-order Gauss-Markov process or a Wiener process, both of which are common noise terms in inertial sensor noise models. The measurement model includes an additive wh...

This paper investigates Man-Portable Air Defense Systems (MANPAD) missile threat detection and an effective maneuvering strategy for aircraft (A/C) destruction avoidance. The 3D trajectory of a multistage missile (boost and sustain phases) under Pure Proportional Navigation is first estimated. The maximum likelihood estimator is used to estimate it...

All practical sensing operations must work with quantized data. In some settings, “high resolution” uniform quantization is used, and data are treated as approximately continuous. The aim of this work is to facilitate “cheap” central decision making by considering extremely low resolution quantization of data sent from distributed sensors. Along wi...

An iterative procedure to solve the nonlinear problem of fastest-path sailing vessel routing in an environment with variable winds and currents is proposed. In the routing of a sailing vessel, the primary control variable is the pointing (heading) of the vessel (assuming that the sails are chosen and trimmed optimally). Sailing vessel routing is hi...

Noise covariance estimation in an adaptive Kalman filter is a problem of significant practical interest in a wide array of industrial applications. Reliable algorithms for their estimation are scarce, and the necessary and sufficient conditions for identifiability of the covariances were in dispute until very recently. This chapter presents the nec...

This paper considers the problem of estimating the trajectory of a ballistic target in the reentry phase using 2D measurements (azimuth and elevation angles) from a moving passive sensor. Previous works investigated the estimation problem of an object in the thrusting and initial ballistic phase from a single fixed passive sensor. This work shows t...

We discuss and extend two approaches to target detection and motion parameter estimation in a very low observable (VLO) underwater multipath environment. The maximum likelihood probabilistic data association (ML-PDA) algorithm considers all possible path–measurement hypotheses, whereas the maximum likelihood probabilistic multiple hypothesis tracke...

Tracking with bistatic radar measurements is a challenging problem due to the nonlinear relationship between the radar measurements and the Cartesian coordinates, especially for long distances. This nonlinearity leads, for 3-D bistatic radar, to a non-ellipsoidal measurement uncertainty region in Cartesian coordinates, similar to a thin contact len...

Tracking with bistatic radar measurements is a challenging problem due to the nonlinear relationship between the radar measurements and the Cartesian coordinates, especially for long distances. This nonlinearity leads, for 3-D bistatic radar, to a non-ellipsoidal measurement uncertainty region in Cartesian coordinates, similar to a thin contact len...

In this work, we study the order statistic estimation and provide a simple solution to lidar bounding box (BB) centroid estimation for an autonomous vehicle (AV). The estimated BB centroid and its uncertainty will be used in object tracking as measurement and measurement noise variance; the latter is commonly not available from the sensor manufactu...

In a pursuit evasion scenario, a missile (pursuer) launched from the ground or the air aims to intercept an aircraft (evader) flying in formation. This article considers the problem of whether an aircraft is aimed by a missile or not, based only on the line-of-sight (LOS) measurements from an on-board passive sensor. The motion of the missile is as...

The goal of this article is to provide a real-time implementable algorithm to estimate the launch point (LP) of a thrusting object using angle-only measurements. The measurements are obtained in the object’s thrusting stage and from a single passive sensor (fixed or moving with constant velocity). It is assumed that one has a delayed acquisition, w...

In this article, we present an algorithm for the discrimination of the target of interest for the purpose of interdiction in the presence of several spurious targets that are intended to confuse the intercept decision. This is applied to an IR (infrared) detection-based tracking system on a low earth orbit (LEO) satellite and an optical sensor-base...

Inertial navigation system stationary fine alignment process is a critical step in reducing the initial errors of the attitude and sensor biases. While many studies had been made for tactical grade systems, less attention was given to low-cost sensors, which are a major player in today’s inertial sensors market. To fill this gap, a measurement stra...

Many threats (terrorist attacks, military actions, etc.) can be modeled by someone with relevant expert knowledge. A “threat” here implies a sequence of actions that evolve over time and are intended to culminate in a goal that from the article’s perspective is unfavorable. This work presents a method to model probabilistically these types of pro...

This paper extends previous work on location and intensity estimation for measurement extraction of targets in a focal plane array (FPA). Prior work has been done to extract single targets and two targets of equal intensity, while the present work explores the case where two targets have unequal and unknown intensities. Here we assume a Gaussian po...

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown . Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this pape...

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown. Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this paper...

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown . Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this pape...

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown . Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this pape...

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown . Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this pape...

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown . Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this pape...

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown . Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this pape...

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown . Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this pape...

The Kalman filter requires knowledge of the noise statistics; however, the noise covariances are generally unknown . Although this problem has a long history, reliable algorithms for their estimation are scant, and necessary and sufficient conditions for identifiability of the covariances are in dispute. We address both of these issues in this pape...

This paper considers measurement extraction for two closely-spaced objects (CSOs) with unknown equal intensities in an imaging sensor's focal plane array (FPA). Given a screen of FPA data, the first part of the measurement extractor, target location estimator, can extract the location estimates for two targets or one, with the corresponding accurac...

This paper considers the real-time recovery of a fast time series by using sparsely sampled measurements from sensors whose sampling speeds are prohibitively slow originally. Specifically, when the fast signal is an autoregressive process, we propose an online information recovery algorithm that reconstructs the dense underlying temporal dynamics f...

As bias estimation methods are developed, it becomes necessary to obtain the bound on bias estimation for more complex bias and sensor models. 3-dimensional (3-D) sensors, such as radars commonly used in applications, contain both scale and additive biases in sine space which result in a nonlinear estimation problem that may have poor observability...

In target tracking systems involving data fusion it is common to encounter sensor measurement biases that contribute to the tracking errors. There is extensive research into estimating sensor biases, but very little research into bias estimation in the dynamic case, meaning that biases that change over time are addressed. This paper investigates th...

This paper deals with measurement extraction, from an optical sensor's Focal Plane Array (FPA), of a streaking target. We use a model that assumes pixels are separated by dead zones and model the streaking target's point spread function (PSF) as a Gaussian PSF that moves during the optical sensor's integration time. We make an assumption that the t...

Sensor registration refers to the estimation/compensation of systematic (bias) errors, in contrast to the random errors from sensor noise. Various methods have been proposed for bias estimation of multiple optical sensors using common targets of opportunity. However, the proposed solutions required the use of multiple (two or more) optical sensors...

The trajectory estimation problem of a thrusting/ballistic object in 3-D space has been previously solved with 2-D measurements (azimuth and elevation angles from a fixed passive sensor, either starting from the launch time or with a delayed acquisition) under the assumption of constant mass. However, since the mass decreases as the fuel burns this...

This paper considers the real-time recovery of a fast time series (e.g., updated every T seconds) by using sparsely sampled measurements from two sensors whose sampling intervals are much larger than T (e.g., MT and NT, where M and N are integers). Specifically, when the fast signal is an autoregressive process, we propose an online information rec...

This paper considers the real-time recovery of a fast time series (e.g., updated every T seconds) by using sparsely sampled measurements from two sensors whose sampling intervals are much larger than T (e.g., MT and NT, where M and N are integers). Specifically, when the fast signal is an autoregressive process, we propose an online information rec...

Tracking with bi-static sonar or radar measurements is challenging due to the fact that the measurements are nonlinear functions of the Cartesian state. The performance of existing approaches, including the Extended Kalman Filter (EKF) and sigma point Kalman filters such as the Unscented Kalman Filter (UKF) and Cubature Kalman Filter (CKF), may not...

In this paper we provide a new methodology using an exoatmospheric target of opportunity seen in a satellites borne sensor's field of view to estimate the sensor's biases simultaneously with the state of the target. Each satellite is equipped with an Infra Red (IR) sensor that provides the Line Of Sight (LOS) measurements azimuth and elevation to t...

In the field of target tracking, a tremendous amount of work has been performed on improving the ability of many different algorithms to detect and track a target in the presence of clutter and other interfering targets. However, to date, surprisingly little work has been performed on analyzing whether or not, for a given target in a given clutter/...

This paper considers the measurement extraction for a point target from an optical sensor's focal plane array (FPA) with a dead zone separating neighboring pixels. Assuming that the energy density of the target deposited in the FPA conforms to a Gaussian point spread function (PSF) and that the noise mean and variance in each pixel are proportional...

The state of a thrusting/ballistic object moving in 3D space can be determined uniquely by a 4-dimensional parametervector (launch azimuth and elevation angles, drag coefficient and specific thrust, with the latter two assumed constant) given the location of the launch point (LP). Using the first line of sight (LoS) measurement and the known launch...

In active sonar and radar target tracking, measurements consist of position and often also include range rate. Tracking algorithms use these measurements over time to estimate target state comprising position, velocity and, where applicable, turn rate. In most cases there is an underlying assumption in the tracking algorithm that the target is a “p...