Benjamin J. Slocumb

Tracking in the presence of range deception ECM and clutter by Decomposition and Fusion

Range deception, such as range-gate-pull-off (RGPO) is a common electronic countermeasure (ECM) technique used to defeat or degrade tracking radars. Although a variety of heuristic approaches/tricks have been proposed to mitigate the impact of this type of ECM on the target tracking algorithms, none of them involve a systematic means to reject the countermeasure signals. This paper presents a general and systematic approach, called Decomposition and Fusion (DF) approach, for target tracking in the presence of range deception ECM and clutter. It is effective against RGPO, range-gate-pull-in, and range false target ECM techniques for a radar system where the deception measurements have virtually the same angles as the target measurement. This DF approach has four fundamental components: (a) decomposing the validated measurements by determination of range deception measurements using hypothesis testing; (b) running one or more tracking filters using the detected range deception measurements only; (c) running a conventional tracking-in-clutter filter using the remaining measurements; (d) fusing the tracking filters by a probabilistically weighted sum of their estimates. Several algorithms within the DF approach are discussed.

Parameter estimation for periodic discrete event processes

Methods for estimating the pulse repetition interval and the pulse time-of-arrival jitter variance of a periodic pulse train (PT) are given. The estimators operate on data comprising a set of pulse time-of-arrival measurements which are corrupted by additive noise (timing jitter). Parameter estimators are formulated using two jitter models common to PT applications. The estimators include a closed-form maximum likelihood approach, recursive least squares estimation, and a Kalman filter approach that incorporates both jitter models. Comparison with Cramer-Rao lower bounds shows the estimators to be statistically efficient for a correct choice of jitter model. The estimator performance penalty for using the incorrect jitter model is also presented. Finally, the utility of the estimators is demonstrated using PT measurements from an operational radar.<<ETX>>

A polynomial phase parameter estimation-phase unwrapping algorithm

This paper describes a new approach for estimating the polynomial phase coefficients of an unknown signal with constant amplitude. Our approach uses linear regression on the unwrapped signal phase, a technique introduced by Tretter (1981) and further developed by Djuric and Kay (see IEEE Trans. on Signal Processing, vol.38, no.12, p.2118-2126, 1990). The proposed technique expands upon the previous work by developing a recursion for conducting the phase unwrapping concurrently with the coefficient estimation. The unwrapping performance is improved because of the use of current coefficient estimates. A secondary result provided by the new algorithm is an instantaneous phase estimate. Simulations show improvements in mean-squared error threshold level of the coefficient estimates over the method of Djuric and Kay.<<ETX>>

ECM modeling for assessment of target tracking algorithms

We describe techniques, concepts, and important issues associated with the development of electronic countermeasures (ECM) models for testing advanced target tracking algorithms. The developments are specifically geared toward the benchmark radar model which has been presented by Blair et al. (see Proc. ACC, p.2071-5, 1994, and Proc. ACC, p.2601-05, 1995). Our ECM focus is specifically on range denial and range deception ECM techniques.

Tracking a maneuvering target using jump filters

The concept of the jump filter, developed in previous work, is employed to track the maneuvering targets in the benchmark problem. The jump filter, which is based on the technique of input estimation, provides a simple and straightforward approach to track maneuvering targets. The jump filter tracks the benchmark data with an average track loss of about 1% using a 2 Hz sample rate.

Tracking a maneuvering target in the presence of false returns and ECM using a variable state dimension Kalman filter

In this paper, we show the performance of a variable state dimension Kalman filter for tracking a maneuvering target under the real-world conditions defined in the second benchmark problem. The second benchmark problem extends the first benchmark problem by including false alarms (FA) and electronic countermeasures (ECM). A modified version of the nearest neighbor PDA data association method of Fitzgerald (1986) is used to handle multiple measurement conditions. Adaptive waveform and dwell revisit time selection methods, and track filter coasting, are used to handle the uncertainties introduced by false alarms, missed detection, maneuvers, and ECM. Special features for integrating the adaptive methods into the variable state dimension filter are discussed. The results of this paper should provide a baseline to which other more sophisticated tracking and data association approaches can be compared.

Maximum-likelihood estimation applied to quantum-limited optical position-sensing

Star-tracking systems, optical communication systems, and infrared tracking systems are examples in which the measurement and correction of alignment errors between the optical source and receiver must be made. In this paper, a new position estimator is developed that retains accuracy even under poor SNR conditions. This estimator is derived using an estimation theoretic approach to the problem of tracking a quasi-stationary object given photoevent data in a continuously distributed detector. A maximum likelihood position estimator is derived via application of the expectation-maximization (EM) algorithm. Simulation results are given to show that under low SNR conditions, the estimator performance is superior to that of the commonly used centroid estimator.© (1990) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Surveillance radar range-bearing centroid processing

In non-monopulse mechanically scanned surveillance radars, each target can be detected multiple times as the beam is scanned across the target. To prevent redundant reports of the object, a centroid processing algorithm is used to associate and cluster the multiple detections, called primitives, into a single object measurement. This paper reviews several techniques for centroid processing, and presents a new center of mass algorithm that is implemented with the recursive least squares algorithm. The new algorithm has a unique gating process to enable the primitive measurement association. Simulation results of the new algorithm are reported. Multiple object merged measurement handling issues within the centroid processing context are discussed.

Pulse train PDA analysis and deinterleaving filter

This paper develops the pulse train probabilistic data association filter (PT-PDAF) for use in pulse train analysis and deinterleaving applications. The approach is based on a state-space formulation of the pulse train evolution model. The PDA approach overcomes real-world problems of false and missing pulses which cause the basic Kalman filter to break down. Simulations are developed to show that the PT-PDAF approach is superior to a nearest neighbor filter. An augmented PDA approach which incorporates available pulse parameter measurements such an angle of arrival into the PDA algorithms is shown to further improve the filter performance.

A state space approach to joint AOA and period estimation for a class of periodic discrete event processes

We examine the utility of a state space approach to pulse train signal modelling and address the problem of jointly estimating the angle of arrival (AOA) and periodicity of an intercepted radar pulse train signal. Kalman filter based techniques are developed and the mean square error performance of the estimators evaluated for two separate state space models. Robust methods are also suggested and implemented for dealing with the problem of false and missing pulses.