Network-Centric MFA Tracking Architectures

Lu, Suihua; Poore, Aubrey B.; Suchomel, Brian J.

doi:10.1007/978-1-4757-3758-5_10

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…7,8 For the MFA assignment solver we use four frames for track initiation and three frames for track maintenance (known as a 7/3 solver). In effect, we are able to hold assignment decisions in abeyance until we receive two frames of additional data.…”

Section: Simulation Results Using Residual Bias Mitigationmentioning

confidence: 99%

<title>Mitigation of biases using the Schmidt-Kalman filter</title>

et al. 2007

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Fusion of data from multiple sensors can be hindered by systematic bias errors. This may lead to severe degradation in data association and track quality and may result in a large growth of redundant and spurious tracks. Multi-sensor networks will generally attempt to estimate the relevant bias values (usually, during sensor registration), and use the estimates to debias the sensor measurements and correct the reference frame transformations. Unfortunately, the biases and navigation errors are stochastic, and the estimates of the means account only for the "deterministic" part of the biases. The remaining stochastic errors are termed "residual" biases and are typically modeled as a zero-mean random vector. Residual biases may cause inconsistent covariance estimates, misassociation, multiple track swaps, and redundant/spurious track generation; we therefore require some efficient mechanism for mitigating the effects of residual biases. We present here results based on the Schmidt-Kalman filter for mitigating the effects of residual biases. A key advantage of this approach is that it maintains the cross-correlation between the state and the bias errors, leading to a realistic covariance estimate. The current work expands on the work previously performed by Numerica through an increase in the number of bias terms used in a high fidelity simulator for air defense. The new biases considered revolve around the transformation from the global earth-centered-earth-fixed (ECEF) coordinate frame to the local east-north-up (ENU) coordinate frame. We examine not only the effect of bias mitigation for the full set of biases, but also analyze the interplay between the various bias components.

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Section: Simulation Results Using Residual Bias Mitigationmentioning

confidence: 99%

<title>Mitigation of biases using the Schmidt-Kalman filter</title>

et al. 2007

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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).…”

Section: Centralized Track Fusionmentioning

confidence: 99%

Distributed multiple-hypothesis correlation and feedback with applications to video data

2008

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A common problem in video-based tracking of urban targets is occlusion due to buildings and vehicles. Fortunately, 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.

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