Principles of Modern Radar - Volume 2 1891121537

692 CHAPTER 15 Multitarget, Multisensor TrackingTrack-level fusion (also sometimes referred to as decentralized fusion in academicliterature [7]), is motivated by this constraint. By performing measurement-to-trackassignment locally (within each sensor) and only periodically sharing the track states andcovariances, the track-level fusion architecture can afford much less frequent transmissionof data.Sensor fusion is required (based on necessity, observability, capacity, and robustness)for many radar applications, but poorly-executed fusion can actually degrade results relativeto single-radar performance. Both sensor fusion architectures (measurement-leveland track-level) are fraught with challenges. Section 15.3.2 provides an overview of thechallenges unique to measurement-level fusion. Section 15.3.3 then gives an overview ofthose unique to track-level fusion, Section 15.3.4 covers the challenges common to botharchitectures.15.3.2 Measurement-level Fusion and Associated ChallengesCommunication constraints pose the most obvious challenge for measurement-level fusion.Assuming that the sensors are capable of tracking a multitude of targets at highupdate rates, the sheer volume of measurements that must be transmitted via the communicationnetwork can be overwhelming. On-going research is being conducted to improvecommunication bandwidth and reliability, as well as to adaptively adjust the amount ofdata that must be transmitted (e.g., by creating pseudo-measurements [7] from a set ofmeasurements).Measurement-level fusion architectures can also be susceptible to missing, late, orout-of-sequence measurements. This is particularly problematic in architectures like theCooperative Engagement Capability (CEC), where all sensors receive all measurementsfrom all other sensors and rely on identical fusion and tracking algorithms to maintainsynchronized track pictures. A body of work exists on these topics in academic literature[3, 6]; close attention to implementation details is required to make these techniques workin practice.While sensor biases pose challenges in both fusion architectures, they manifest themselvesuniquely in each. In the measurement-fusion paradigm, the measurements must betransformed from local sensor coordinates (e.g., in sine space or range/azimuth/elevation)into Cartesian coordinates (e.g., Earth-centered, Earth-fixed) so that they can be fusedwith measurements from other sensors at disparate locations. To be sufficiently accurate,this conversion requires knowledge of internal sensor biases from the latest roundof calibration. Two main options for addressing this problem are available, but both consumeprecious communications bandwidth. Either the latest calibration results must beconveyed from the sensors to the fusion center, or the coordinate conversion must occurlocally (within the sensors) prior to transmission. The former option requires periodictransmission of (a potentially large amount of) calibration data, while the latter requiresfrequent transmission of larger measurements (e.g., 6 × 1 state and 21 unique covarianceterms, instead of a 3 × 1 state and 6 unique covariance terms).15.3.3 Track-level Fusion and Associated ChallengesCertain challenges are unique to track-level fusion. In particular, prior work [5] demonstratesthat estimation errors reported by two sensors on the same target are not independent.Rather, dependence arises due to common process noise used in the respective track