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This work presents a new network centric architecture for multiple frame assignment (MFA) tracking. The architecture improves on earlier network tracking schemes by allowing trackers to broadcast decisions about their local soft-level associations, via the Soft Associated Measurement Reports (SAMRs). The SAMR may be followed by an "Oops" message, i...
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... Although a wide variety of architectures are available for track fusion systems, [3][4][5] in this paper we explore a centralized approach where the data from all local trackers are fused into system-level tracks at one location. A block diagram of this configuration is shown in Figure 1(b). ...
A common problem in video-based tracking of urban targets is occlusion due to buildings and vehicles. Fortu-nately, when multiple video sensors are present with enough geometric diversity, track breaks due to temporary occlusion can be substantially reduced by correlating and fusing source-level track data into system-level tracks. Furthermore, when operating in a communication-constrained environment, it is preferable to transmit track data rather than either raw video data or detection measurements. To avoid statistical correlation due to common prior information, tracklets can be formed from the source tracks prior to transmission to a central command node, which is then responsible for system track maintenance via correlation and fusion. To maximize the operational benefit of the system-level track picture, it should be distributed in an efficient manner to all platforms, especially the local trackers at the sensors. In this paper, we describe a centralized architecture for multi-sensor video tracking that uses tracklet-based feedback to maintain an accurate and complete track picture at all platforms. We will also use challenging synthetic video data to demonstrate that our architecture improves track completeness, enhances track continuity (in the presence of occlusions), and reduces track initiation time at the local trackers.
The coordinated use of multiple distributed sensors by network communication has the potential to substantially improve track state estimates even in the presence of enemy countermeasures. In the modern electronic warfare environment, a network-centric tracking system must function in a variety of jamming scenarios. In some scenarios hostile electronic countermeasures (ECM) will endeavor to deny range and range rate information, leaving friendly sensors to depend on passive angle information for tracking. In these cases the detrimental effects of ECM can be at least partially ameliorated through the use of multiple networked sensors, due to the inability of the ECM to deny angle measurements and the geometric diversity provided by having sensors in distributed locations. Herein we demonstrate algorithms for initiating and maintaining tracks in such hostile operating environments with a focus on maximum likelihood estimators and provide Cramer-Rao bounds on the performance one can expect to achieve.
