This paper investigates the problem of detecting a multichannel signal embedded in subspace interference and Gaussian noise. The interference lies in a known subspace but with unknown coordinates, while the noise, in the general sense, consisting of thermal noise and clutter, has an unknown covariance matrix. To estimate the covariance matrix, it is assumed that there are sufficient signal-free training data. Two cases are considered, namely, the homogeneous environment (HE) and partially homogenous environment (PHE). The test and training data in the HE have the same noise covariance matrix, while the test and training data in the PHE share the same noise covariance matrix up to an unknown scaling factor. We derive the corresponding Wald tests both in the HE and PHE. Remarkably, these two detectors reject the clutter by using the sample covariance matrix, and then reject interference and integrate signal simultaneously through the oblique projection. Numerical examples show that the two proposed detectors can provide better detection performance than existing detectors in some situations.

This paper presents a high-fidelity inhomogeneous ground clutter simulation method for airborne phased array Pulse Doppler (PD) radar aided by a digital elevation model (DEM) and digital land classification data (DLCD). The method starts by extracting the basic geographic information of the Earth’s surface scattering points from the DEM data, then reads the Earth’s surface classification codes of Earth’s surface scattering points according to the DLCD. After determining the landform types, different backscattering coefficient models are selected to calculate the backscattering coefficient of each Earth surface scattering point. Finally, the high-fidelity inhomogeneous ground clutter simulation of airborne phased array PD radar is realized based on the Ward model. The simulation results show that the classifications of landform types obtained by the proposed method are more abundant, and the ground clutter simulated by different backscattering coefficient models is more real and effective.

This article presents a simulation method for complex meteorological target echoes in airborne dual-polarization weather radar based on the invariant imbedding T-matrix (IITM) method. The approach integrates IITM with the weather research and forecasting (WRF) model. Initially, meteorological scenes are modeled and simulated using WRF. This is followed by the modeling of six meteorological targets: raindrops, snow, hail, graupel, ice crystals, and cloud droplets, taking into account their possible varying shapes and setting proportional distributions for these shapes in the simulation. Subsequently, the complex refractive indices of these meteorological targets are calculated using the Debye model, Polder-van Santern (P-S) equivalent medium model, and melting ratio model. These indices are then incorporated with IITM to compute the scattering amplitude matrices for targets of various shapes. The final stage involves calculating the reflectivity factors of the six types of meteorological targets under different shape ratios. These factors are then applied to the radar meteorological equation to determine the echo power. The covariance matrix, illustrating the pulse correlation within the echo signal, is formulated using meteorological target echo power and wind speed data. This matrix undergoes Cholesky decomposition to facilitate the simulation of echo signals from meteorological targets for airborne dual-polarization weather radar. The simulation results indicate that the method can accurately replicate meteorological features, with comparative analysis against actual measurement data further validating the method’s effectiveness and reliability.

In this article, we propose a method to estimate the synthetic aperture radar interferometry (InSAR) interferometric phase based on the model of correlation weight joint pixel by using the joint subspace projection technique. In the method, the correlation weight joint data vector is constructed and the data vector can make the noise subspace dimension of the corresponding weight covariance matrix which is not affected by the coregistration error, thus avoiding the trouble of calculating the noise subspace dimension before estimating the InSAR interferometric phase. The method takes advantage of the coherence information of neighboring pixel pairs to auto-coregister the SAR images and employs the projection of the joint signal subspace onto the corresponding joint noise subspace to estimate the terrain interferometric phase. The method can auto-coregister the SAR images and reduce the interferometric phase noise simultaneously. Theoretical analysis and computer simulation results show that the method can provide accurate estimate of the interferometric phase (interferogram) even when the coregistration error reaches one pixel. The effectiveness of the method is verified via simulated data and real data.

Conventional clutter suppression methods in the field of precipitation detection generally use fixed parameters, resulting in poor clutter suppression in complex weather conditions and regional environments. Here, a ground clutter suppression method for dual‐polarisation weather radar based on fuzzy neural network (FNN) is proposed. In the method, the FNN is trained with the ground clutter data acquired by the dual‐polarisation weather radar in the clear sky mode. The membership function parameters of ground clutter polarisation parameters are adaptively calculated. Then, the ground clutter in the radar echo under the precipitation mode is identified and suppressed by the trained FNN. Experimental results of measured data show that the proposed method can be adapted to the specific clutter environment so that the ground clutter can be effectively suppressed in complex weather conditions.

Conventional auto-correlation based detectors often require the knowledge of a covariance matrix, which is usually replaced with its corresponding sample covariance matrix by dividing the received signal vector into a number of sub-vectors. This can lead to performance loss due to the deviation of the sample covariance matrix from the population covariance matrix. In this work, the received signal is used as a single signal vector and the use of sample covariance matrices is avoided. Taking advantage of oversampling, we obtain a truncated approximate covariance matrix of primary signals, which leads to a new approximate log-likelihood-ratio-test (aLLRT) detector with low complexity. In addition, a noise power estimator is also proposed by exploiting oversampling, which is incorporated into the new detector for practical implementation. Theoretical analyses for the false-alarm and detection probabilities of the proposed detector are conducted, and their accurate expressions are obtained via performing a nonlinear transformation to the test-statistic of the proposed detector. Numerical results show that, compared to state-of-the-art detectors, the proposed detector improves the detection probability by at least 10%.