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Multistatic radar system has a great potential for human detection and tracking for its fine localization precision, wide coverage and good observability. Traditional human detection and tracking are performed on the one dimensional (1-D) range profile. If targets are close or overlapped in range, it is difficult to distinguish these targets and obtain the measurements for target tracking. It can...
We consider the problem of tracking a maneuvering target in urban terrain with high clutter. Although multipath has been previously exploited to improve target tracking in complex urban environments, when the clutter is high, multipath returns can suffer from large losses in signal-to-noise ratio (SNR), reducing probability of detection (PD). Maneuvering, a common motion in urban terrain, can also...
We investigate the problem of tracking a moving target in urban terrain using a multiple-input and multiple-output (MIMO) radar system. Our proposed method aims to maximize the target information using an optimal configuration of MIMO widely-separated radar sensors while exploiting multipath returns from all the sensors. Furthermore, we adaptively configure the parameters of the transmit waveforms...
We investigate the target tracking problem of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors. Although the multi-modality framework allows for the integration of complementary information, there are many challenges to overcome, including targets with different energy returns, and information loss due to low signal-to-noise...
We integrate multipath exploitation with adaptive waveform design in order to increase the tracking performance of a vehicle moving in urban terrain. Mitigation of both clutter and strong multipath returns can result in increased target detection. However, exploiting multiple bounces from obstacles such as buildings can be shown to increase radar coverage and scene visibility, especially in the absence...
Multiple-input, multiple-output (MIMO) radar systems have gained significant attention as they can enhance target detection, identification and parameter estimation performance. In this paper, we consider the problem of optimizing the target tracking performance of a widely-separated MIMO radar system by scheduling the transmitter sensors and adaptively designing their waveforms. Specifically, for...
In this paper, we propose a unified Bayesian decision theory model to integrate various components of a sensor network. We identify the key aspects of the Bayesian decision theory model, the functionalities of each network component, and the nature of interaction of the various network components in the proposed Bayesian framework. We also highlight some of the research avenues that need to be investigated...
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