Chengpeng Hao

Persymmetric Rao and Wald Tests for Partially Homogeneous Environment

This letter deals with the problem of adaptive detection in partially-homogeneous Gaussian disturbance with unknown but persymmetric structured covariance matrix. Since no uniformly most powerful test exists for the problem at hand, we devise and assess two detection strategies based on the Rao test and the Wald test design criteria. Remarkably, both detectors ensure the constant false alarm rate property with respect to both the structure of the covariance matrix as well as the power level. The preliminary performance assessment, conducted by resorting to simulated data, has confirmed the effectiveness of the newly proposed detectors.

Persymmetric adaptive detection of distributed targets in partially-homogeneous environment

In this paper we deal with the problem of detecting distributed targets in the presence of Gaussian noise with unknown but persymmetric structured covariance matrix. In particular, we consider the so-called partially-homogeneous environment, where the cells under test (primary data) and the training samples (secondary data), which are free of signal components, share the same structure of the interference covariance matrix but different power levels. Under the above assumptions, we derive the generalized likelihood ratio test (GLRT) and the so-called two-step GLRT. Remarkably, the new receivers ensure the constant false alarm rate property with respect to both the structure of the covariance matrix as well as the power level. The performance assessment, conducted by resorting to both simulated data and real recorded dataset, highlights that the proposed detectors can significantly outperform their unstructured counterparts, especially in a severely heterogeneous scenario where a very small number of secondary data is available.

Adaptive detection of distributed targets in partially homogeneous environment with Rao and Wald tests

This paper deals with the problem of detecting distributed targets in the presence of partially homogeneous Gaussian disturbance with unknown covariance matrix. Since no uniformly most powerful test exists for the problem at hand, we devise and assess two detection strategies based on the Rao test, and the Wald test respectively. Remarkably both tests ensure the constant false alarm rate (CFAR) property with respect to both the structure of the covariance matrix as well as the power level. A preliminary performance assessment, conducted by resorting to simulated data, also in comparison to previously proposed detectors, has confirmed the effectiveness of the newly proposed detection algorithms.

Adaptive Detection of Point-Like Targets in Spectrally Symmetric Interference

We address adaptive radar detection of targets embedded in ground clutter dominated environments characterized by a symmetrically structured power spectral density. At the design stage, we leverage on the spectrum symmetry for the interference to come up with decision schemes capable of capitalizing the a-priori information on the covariance structure. To this end, we prove that the detection problem at hand can be formulated in terms of real variables and, then, we apply design procedures relying on the GLRT, the Rao test, and the Wald test. Specifically, the estimates of the unknown parameters under the target presence hypothesis are obtained through an iterative optimization algorithm whose convergence and quality guarantee is thoroughly proved. The performance analysis, both on simulated and on real radar data, confirms the superiority of the considered architectures over their conventional counterparts which do not take advantage of the clutter spectral symmetry.

Parametric Rao test for multichannel adaptive detection of range-spread target in partially homogeneous environments

In this paper we deal with the problem of detecting a multi-channel signal of range-spread target in the presence of Gaussian disturbance with an unknown covariance matrix. In particular, we consider the so-called partially homogeneous environment, where the disturbances in both the cells under test (primary data) and the training samples (secondary data) share the same covariance matrix up to an unknown power scaling factor. To this end, we first model the disturbance as a multichannel autoregressive (AR) process, and then develop an adaptive detector resorting to the Rao test. Remarkably, the proposed detector attains asymptotically a constant false alarm rate (CFAR) independent of the disturbance covariance matrix as well as the power scaling factor. The performance assessment conducted by Monte Carlo simulation highlights that the new receiver significantly outperforms their traditional covariance matrix-based counterparts both in AR and non-AR modeled disturbance backgrounds. Meanwhile, it requires less secondary data and is computationally more efficient. HighlightsThe proposed detector requires less training data, and is more computational efficient.The proposed detector is scaling invariant to the power scaling factor.The proposed detector has an asymptotic CFAR property with respect to the disturbance structure.

An Autocalibration Algorithm for Uniform Circular Array With Unknown Mutual Coupling

A new subspace-based autocalibration algorithm for a uniform circular array with unknown mutual coupling is presented in this letter. In allusion to the existing ambiguity problems and the limitation of nonzero coupling coefficients in earlier work by Lin and Yang, a more generalized iterative method is proposed to jointly estimate the direction-of-arrival (DOA) and unknown mutual coupling. It suffers from no ambiguity problems and does not require the prior knowledge of the number of nonzero elements in the coupling vector. Simulation results show the robustness, effectiveness, and higher estimated accuracy of the proposed algorithm.

Adaptive Radar Detection and Range Estimation with Oversampled Data for Partially Homogeneous Environment

In the present letter we investigate the problem of adaptive detection and range estimation for point-like targets buried in partially homogeneous Gaussian disturbance with unknown covariance matrix. To this end, we jointly exploit the spillover of target energy to consecutive range samples and the oversampling of the received signal. In this context, we design a detector relying on the Generalized Likelihood Ratio Test (GLRT). Remarkably, the new decision scheme ensures the Constant False Alarm Rate (CFAR) property with respect to the unknown disturbance parameters. The performance analysis reveals that it can provide enhanced detection performance compared with its state-of-art counterpart while retaining accurate estimation capabilities of the target position.

Modified Rao Test for Multichannel Adaptive Signal Detection

The problem of detecting a subspace signal is studied in colored Gaussian noise with an unknown covariance matrix. In the subspace model, the target signal belongs to a known subspace, but with unknown coordinates. We first present a new derivation of the Rao test based on the subspace model, and then propose a modified Rao test (MRT) by introducing a tunable parameter. The MRT is more general, which includes the Rao test and the generalized likelihood ratio test as special cases. Moreover, closed-form expressions for the probabilities of false alarm and detection of the MRT are derived, which show that the MRT bears a constant false alarm rate property against the noise covariance matrix. Numerical results demonstrate that the MRT can offer the flexibility of being adjustable in the mismatched case where the target signal deviates from the presumed signal subspace. In particular, the MRT provides better mismatch rejection capacities as the tunable parameter increases.

An Adaptive Detector with Range Estimation Capabilities for Partially Homogeneous Environment

In this work, we devise an adaptive decision scheme with range estimation capabilities for point-like targets in partially homogeneous environments. To this end, we exploit the spillover of target energy to consecutive range samples and synthesize the Generalized Likelihood Ratio Test. The performance analysis, conducted resorting to both simulated data and real recorded datasets, highlights that the newly proposed architecture can guarantee superior detection performance with respect to its competitors while retaining accurate estimation capabilities of the target position.

Radar detection and range estimation using oversampled data

In this work, we propose two adaptive receivers achieving enhanced range estimation capability through a joint exploitation of the oversampling and the spillover of target energy in adjacent range samples. To this end, a proper discrete-time model for the received signal is introduced. Then, the generalized likelihood ratio test (GLRT) and the so-called two-step GLRT are derived and assessed. The performance analysis, conducted using both simulated data and real recorded datasets, is aimed at assessing the effectiveness of proposed solutions, also in comparison with existing detectors sharing range estimation capabilities. The illustrative examples highlight that better detection performance and increased range estimation accuracy can be achieved by exploiting the oversampling at the price of an additional processing cost.