The primary components of a target track are the estimated state vector and its error variance-covariance matrix (or
simply the covariance). The estimated state indicates the location and motion of the target. The track covariance should
indicate the uncertainty or inaccuracy of the state estimate. The covariance is computed by the track processor and may
or may not realistically indicate the inaccuracy of the state estimate. Covariance Consistency is the property that a
computed variance-covariance matrix realistically represents the covariance of the actual errors of the estimate. The
computed covariance of the state estimation error is used in the computations of the data association processing function;
consequently, degraded track consistency might cause misassociations (correlation errors) that can substantially degrade
track performance. The computed covariance of the state estimation error is also used by downstream functions, such as
the network-level resource management functions, to indicate the accuracy of the target state estimate. Hence, degraded
track consistency can mislead those functions and the war fighter about how accurate each target track is.
In the past, far more attention has been given to improving the accuracy of the estimated target state than in improving the
track covariance consistency. This paper addresses performance metrics of covariance consistency. Monte Carlo
simulation results illustrate the characteristics of the proposed metrics of covariance consistency.
A monopulse radar is able to derive accurate angular measurements via intelligent processing of its sum and difference channel returns. Recently there have emerged techniques for angular estimation of several unresolved targets, meaning targets that are, in principle, merged within the same radar beam, can be extracted separately. The key is the joint exploitation of information in several range bins. Here we show the performance of this approach in a high-fidelity simulation: we observe considerable improvement in track RMSE, but little corresponding gain in track completeness. Coupled with a hidden Markov model on target number, however, the performance is impressive.
Proc. SPIE. 5204, Signal and Data Processing of Small Targets 2003
KEYWORDS: Target detection, Radar, Signal to noise ratio, Statistical analysis, Detection and tracking algorithms, Data modeling, Electroluminescence, Monte Carlo methods, Antennas, Algorithm development
A key assumption in monopulse based angle-of-arrival (AOA) estimators is that at most one return from a single object is present in each range cell, or equivalently in each sample of the matched filter output. These algorithms break down if the data consists of merged measurements-multiple target returns contained in the same range cell. The proposed technique makes use of data from a three channel monopulse radar to estimate the AOA of two targets from merged measurements. Specifically, the technique capitalizes on the structure of squint beams in conjunction with multiple range samples to resolve the multiple targets. The paper focuses on the development of the new algorithm along with results from computer simulations that demonstrate its viability.
To illustrate the utility of this technique to target tracking problems, comparative Monte Carlo results of performance of a tracker with the new technique and conventional monopulse AOA estimates are provided.
In many tracking applications, and particularly those in ballistic
missile defense, one concern involves the continuous tracking of
an object that separates into two objects. Reliable tracking
without track breaks demands early recognition of such a split,
preferably well in advance of the two objects becoming resolvable
by the radar. In previous work, signal processing techniques for
detecting the presence of unresolved objects and angle-of-arrival
estimation for unresolved targets have been developed for
monopulse radars. In this paper, these techniques are reviewed and
extended. Techniques for detecting the presence of unresolved
objects are treated for the case of idealized resolution, in which
all of the energy for a target is returned in a single range
resolution sample or cell. The approaches work solely on monopulse
angle statistics and rely on idealized range resolution. The
requirement for idealized range resolution is relaxed by using
joint statistics with adjacent matched filter returns. The AOA
estimation and detection of the presence of unresolved objects for
non-ideal resolution are then addressed. The performances are
demonstrated using a high fidelity software simulation tool for
A tracklet is the estimate of a target state or track that is equivalent to an estimate based only a few measurements. Typically, tracklets are considered to reduce the communications costs between sensors and remote global or fusion trackers. The literature includes several methods for computing tracklets. Some of the methods compute tracklets from measurements, while others compute tracklets from the sensor-level tracks. Some of the methods ignore or omit process noise from the modeling, while others methods attempt to address the presence of process noise. The tracking of maneuvering targets requires the inclusion of process noise. When a tracklet that was developed for nonmaneuvering targets (i.e., no process noise) is used for tracking maneuvering targets, the errors of the tracklet will be somewhat cross-correlated with data from other sensors for the same target, and it is referred to as a quasi-tracklet. Due to some important practical considerations, the impact of maneuvering targets on the performance of tracklets has not been thoroughly addressed in the literature. An investigation that includes the critical practical considerations requires computer simulations with realistic target maneuvers and pertinent evaluation criteria (i.e., computation of errors). In this paper, some of the practical issues concerning the use of tracklets for tracking maneuvering targets are discussed, and the results from a simulation study of the impact of target maneuvers on tracking with tracklets are given. The study considered a fusion tracker receiving tracklets from multiple sensors at dispersed locations and targets maneuvering with either random accelerations or deterministic maneuvers. Tracklets from measurements and tracklets from tracks were studied. Since process noise was added to sensor and fusion trackers to account for target maneuvers, the tracklet methods studied are technically quasi-tracklets. A novel technique is used to compare the performance of tracklets for targets maneuvering randomly with that for targets performing deterministic maneuvers.