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Register a new userAdaptive Radar Detection and Classification Algorithms for Multiple Coherent Signals
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Added by
Pia Addabbo
Publication date
2020
fields
Electronic Engineering
and research's language is
English
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In this paper, we address the problem of target detection in the presence of coherent (or fully correlated) signals, which can be due to multipath propagation effects or electronic attacks by smart jammers. To this end, we formulate the problem at hand as a multiple-hypothesis test that, besides the conventional radar alternative hypothesis, contains additional hypotheses accounting for the presence of an unknown number of interfering signals. In this context and leveraging the classification capabilities of the Model Order Selection rules, we devise penalized likelihood-ratio-based detection architectures that can establish, as a byproduct, which hypothesis is in force. Moreover, we propose a suboptimum procedure to estimate the angles of arrival of multiple coherent signals ensuring (at least for the considered parameters) almost the same performance as the exhaustive search. Finally, the performance assessment, conducted over simulated data and in comparison with conventional radar detectors, highlights that the proposed architectures can provide satisfactory performance in terms of probability of detection and correct classification.
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In this paper, four adaptive radar architectures for target detection in heterogeneous Gaussian environments are devised. The first architecture relies on a cyclic optimization exploiting the Maximum Likelihood Approach in the original data domain, whereas the second detector is a function of transformed data which are normalized with respect to their energy and with the unknown parameters estimated through an Expectation-Maximization-based alternate procedure. The remaining two architectures are obtained by suitably combining the estimation procedures and the detector structures previously devised. Performance analysis, conducted on both simulated and measured data, highlights that the architecture working in the transformed domain guarantees the constant false alarm rate property with respect to the interference power variations and a limited detection loss with respect to the other detectors, whose detection thresholds nevertheless are very sensitive to the interference power.
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This paper proposes compressed domain signal processing (CSP) multiple input multiple output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain. First, compressive sensing is applied at the receive antennas, followed by a Capon beamformer which is designed to suppress clutter. Exploiting the sparse nature of the beamformer output, a second compression is applied to the filtered data. Target detection is subsequently conducted by formulating and solving a hypothesis testing problem at each grid point of the discretized angle space. The proposed approach enables an 8-fold reduction of the sample complexity in some settings as compared to a conventional compressed sensing (CS) MIMO radar thus enabling faster target detection. Receiver operating characteristic (ROC) curves of the proposed detector are provided. Simulation results show that the proposed approach outperforms recovery-based compressed sensing algorithms.
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