Abstract
We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.
Highlights
The importance of radar target identification is widely recognized and it has become one of the major concerns in radar surveillance and homeland security applications [1]
We address the problem of extended target recognition in cognitive radar networks whose constitution was described by Haykin [18]
We demonstrate the benefits of a cognitive radar network for extended target recognition by comparing it to one without a feedback structure
Summary
The importance of radar target identification is widely recognized and it has become one of the major concerns in radar surveillance and homeland security applications [1]. Haykin [9] suggested that such a cognitive radar system can be represented using a Bayesian formulation whereby many different hypotheses are given a probabilistic rating Based on this idea, Goodman [10] proposed the integration of waveform design techniques [8] with a sequential-hypothesis testing (SHT) framework [11] that controls when hard decisions may be made with adequate confidence [12]. Goodman [10] proposed the integration of waveform design techniques [8] with a sequential-hypothesis testing (SHT) framework [11] that controls when hard decisions may be made with adequate confidence [12] He compared two different waveform design techniques for use with active sensors operating in a target recognition application. The target hypotheses were further extended to statistical characterization by power spectral densities in [15] where waveforms are matched to the target class rather than to individual target realizations
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