KEYWORDS: Data modeling, Systems modeling, Sensors, Detection and tracking algorithms, Performance modeling, Data fusion, Information fusion, Analytics, Data processing, Machine learning
In a prior paper, a probabilistic model for using context in fusion was developed. It was shown that context-based fusion could be represented by a Bayesian probabilistic model that contains situation and context data, as well as conditional probabilities for the random variables. In the same paper, a conceptual model of an adaptive real-time context management system was proposed to monitor fusion performance, and select the appropriate context in order to improve fusion performance. This paper represents an extension of the above paper by developing frameworks for an adaptive general real-time context management, with application to optimize the tracking performance of an airborne platform.
Previous papers (1997, 1999, 2000, 2001) have described a tracking approach which utilized a Combined Kalman Filter (CKF), adaptive tracking for maneuver tracking, the JVC association algorithm for association and Interacting Multiple Model (IMM) tracking for use in Airborne Early Warning (AEW) applications. In this paper we present our incorporation of IFF measurement data in addition to radar measurement data for tracking.
First our previous AEW radar tracking approach is briefly reviewed as most of this approach is still utilized for the incorporation of IFF measurement data in addition to radar measurements. Then we describe IFF sensor data and how we modeled it. Our modifications of correlation and association to account for the different types of radar and IFF sensor data and different types of tracks, Radar, IFF, or Radar/IFF tracks. We then introduce the notion of a combined cost for association of IFF measurements to tracks.
Finally we present simulation results showing results of just radar tracking, IFF tracking, and combined Radar/IFF tracking with performance Measures of Effectiveness, (MOE's).
Previous papers (1997, 1999, 2000) have described a tracking approach which utilized a Combined Kalman Filter (CKF), adaptive tracking for maneuver tracking, and the JVC association algorithm for reports to tracks, for use in Airborne Early Warning (AEW) applications. In this paper we present our incorporation of Interacting Multiple Model (IMM) tracking.
First our previous AEW tracking approach is briefly reviewed as most of this approach is still utilized and forms our baseline. The new IMM approach and equations are then described. Then the two IMM tracking approaches used are discussed. One involves a two model IMM containing two constant velocity models, one a low process noise and the other a high process noise model. The other approach involves three IMM filter models, a coordinated turn filter model, and the same two constant velocity filter models as in the two model IMM approach.
Results for both IMM approaches and the baseline tracker are shown. The results presented involve a 120 target scenario with two second update time with simulated radar data. In addition computer timing results are presented. These results indicate that while the three model IMM approach provides the best tracking results, it does so at a substantial computational cost. The two model IMM provides comparable tracking improvement but at a far less computational cost.
KEYWORDS: Detection and tracking algorithms, Radar, Algorithm development, Filtering (signal processing), Data processing, Signal processing, Sensors, Astatine, Radar signal processing, Data conversion
This paper is a continuation of two previous papers presented at past Signal and Data Processing of Small Target conferences held in 1997 and 1999. The 1997 paper , title Combined Kalman Filter and JVC Algorithms for AEW Target Tracking Applications, described AEW advanced tracking algorithm development and provided performance results for straight line targets. In the 1999 paper, Maneuver Tracking Algorithms for AEW Target Tracking Applications, modifications to the tracking and association algorithms necessary to track the remaining 100 maneuvering targets of the 120 target scenario were presented. The maneuvering targets include zig-zag, wave, ovals and racetrack trajectories.
In a previous paper, we described AEW advanced tracking algorithm development and showed performance results for straight line targets. In this paper we show modifications of the tracking and association algorithms necessary to track the remaining 100 maneuvering targets of the 120 targets scenario. As part of ongoing AEW advanced tracking algorithm development activities, a novel approach has been developed that utilizes the Unbiased Coordinate Conversion Filter developed by Bar-Shalom et al for range and azimuth angle processing from the radar with a standard EKF for rdot and angular measurements from other sensors identified as a combined Kalman filter. We also incorporated fading memory into the filter to make it responsive to target maneuvers.
Tracking for airborne early warning (AEW) weapon systems present a number of formidable challenges for any tracking and data fusion algorithms. Realistic scenarios involve thousands of targets in highly cluttered environments with multiple sensors. The E-2C weapon system must detect, track and identify these targets in as small a time frame as possible. As part of ongoing E-2C advanced tracking algorithm development activities a novel approach has been developed that utilizes the debiased coordinate conversion filter developed by Bar-Shalom and Lerro (1993) for range, and azimuth angle processing from the radar and standard EKF for rdot and other angular measurements from other sensors identified as a combined Kalman filter (CKF). To solve the data association problem the JVC algorithm [Jonker-Volgenant Castanon (1988)] was chosen because of favorable results from published studies and internally conducted in-house studies that demonstrate its speed and efficiency in solving the assignment problem for sparse matrices which is typical for E-2C applications. Results shown are based on a scenario consisting of 120 straight line and maneuvering targets overlaid on a previously recorded dense radar environment. Future plans have been initiated to incorporate other sensors and consider other association algorithms such as multi-hypothesis tracking (MHT) or interactive multiple model joint probabilistic data association filter (IMMJPDAF).
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