Multiple Coherent Processing Intervals Research Articles

Tensor structure-based ground clutter suppression approaches for pulse doppler radar

ABSTRACT For pulse doppler (PD) radar, two tensor structure-based methods are proposed to improve the clutter suppression performance. One is tensor singular value decomposition (TSVD) and the other is tensor robust principal component analysis (TRPCA). Two algorithms both stack the range-Doppler matrices of multiple coherent processing intervals (CPI) into a tensor structure, which helps make the most of the temporal information across CPIs. TSVD utilizes the strong temporal correlation of ground clutter data to estimate the clutter subspace. Meanwhile, TRPCA exactly separates both low-rank clutter component and sparse target components from the time series range-Doppler tensor structure. Experimental results indicate that compared with conventional matrix-based schemes such as singular value decomposition (SVD) or singular value decomposition (RPCA), the proposed methods have significant advantages in keeping the target echo while suppressing the clutter. In addition, the computational complexity of each algorithm is analysed in detail.

Target Localization Based on High Resolution Mode of MIMO Radar with Widely Separated Antennas

Coherent processing of multiple-input multiple-output (MIMO) radar with widely separated antennas has high resolution capability, but it also brings ambiguity in target localization. In view of the ambiguity problem, different from other signal processing sub-directions such as array configuration optimization or continuity of phase in space/time, this paper analyzes it from the information level, that is, the tracking method is adopted. First, by using the state equation and measurement equation, the echo data of multiple coherent processing intervals (CPI) are collected to improve the target localization accuracy as much as possible. Second, the non-coherent joint probability data association filter (JPDAF) is used to achieve stable tracking of spatial cross targets without ambiguity measurements. Third, based on the tracking results of the non-coherent JPDAF, the ambiguity of coherent measurement is resolved, that is, the coherent JPDAF is realized. By means of non-coherent and coherent alternating JPDAF (NCCAF) algorithms, high accuracy localization of multiple targets is achieved. Finally, numerical simulations are carried out to validate the effectiveness of the proposed NCCAF algorithm.

Mismatched Signal Rejection Performance of the Persymmetric GLRT Detector

We analytically evaluate the mismatched signal rejection performance of a persymmetric generalized likelihood ratio test (PGLRT) detector in a mismatched case where the nominal target steering vector is not aligned with the true one. This PGLRT detector can be applied to target detection in Gaussian noise with unknown covariance matrix for several cases where the data under test are collected from multiple range cells (i.e., for a distributed target), multiple bands, or multiple coherent processing intervals. We derive analytical expressions for the detection probability of the PGLRT detector in the presence of target steering vector mismatch. All the theoretical results are verified with Monte Carlo simulations. Numerical examples are provided to show that the PGLRT detector is sensitive to the steering vector mismatches.

Polarization-Angle-Frequency Estimation With Linear Nested Vector Sensors

This paper considers the problem of multiple dimensional parameter estimation of radar signals using a linear nested vector sensor array. We propose a computationally efficient polarization-angle-frequency estimation algorithm based on spatial-temporal nested sampling. Radar cross-sections diversity in multiple coherent processing intervals is exploited to construct a virtual polarization-spatial-temporal manifold with extended degrees of freedom. Then, a computational efficient method without eigen-decomposition is derived to estimate Khatri-Rao signal subspace. Automatically paired polarization, azimuth-elevation angles, and doppler frequency estimates are finally obtained by exploiting the idea of the estimation of signal parameters via rotational invariance techniques algorithm. The effectiveness of the proposed method is verified through numerical examples.

Joint angle–frequency estimation with spatiotemporal nested sampling

In this study, the authors propose a new method for joint estimation of angle and frequency parameters in pulsed Doppler radar systems. Unlike many existing methods, in which uniform sampling with Nyquist rate are used for spatiotemporal data acquisition, the presented method is based on the non-uniform nested sampling. The basic idea of is to use the radar cross-sections diversity in multiple coherent processing intervals to construct a virtual spatial–temporal manifold with extended degree of freedom. Through theoretical analysis and computer simulations, they show that the proposed method can offer better identifiability performance and higher parameter estimation accuracy, as compared with existing methods.

Sensitivity Analysis of Likelihood Ratio Test in <formula formulatype="inline"><tex Notation="TeX">$K$</tex> </formula> Distributed and/or Gaussian Noise

In a recent letter we addressed the problem of detecting a fluctuating target in $K$ distributed noise using multiple coherent processing intervals. It was shown through simulations that the performance of the likelihood ratio test is dominated by the snapshot which corresponds to the minimal value of the texture. However, for this particular snapshot the clutter to thermal noise ratio is not large and hence thermal noise cannot be neglected. In the present letter, we continue our investigation with a view to consider detection in a mixture of $K$ distributed and Gaussian noise. Towards this end we study the sensitivity of our previously derived detectors. First, we provide stochastic representations that allow to evaluate their performance in $K$ distributed noise only or Gaussian noise only. Then, their robustness to a mixture is assessed.

Analysis of Steering Vector Mismatch on Adaptive Noncoherent Integration

Amplitude and phase errors can cause mismatches between the hypothesized signal vector and the steering vector of an antenna array. A generalized likelihood ratio test (GLRT) for signal detection with multiple coherent processing intervals (CPIs) in unknown interference is derived. The detection performance of the resulting adaptive noncoherent integration (NCI) algorithm is analyzed along with the effect of steering vector mismatch on detection performance. Detection performance and the effect of steering vector mismatch are illustrated with results for both a nonfluctuating signal model and a CPI-to-CPI fluctuating signal model. It is shown that a signal-to-interference-plus-noise ratio (SINR) loss factor inherent to the algorithm in effect limits the performance gain due to NCI of mismatched signals.

Sea Clutter Texture Estimation: Exploiting Decorrelation and Cyclostationarity

In recent work sea clutter has been modeled as the product of two components. The first one, referred to as speckle, is modeled as a stationary Gaussian process. It is characterized by a short correlation time. The second component, referred to as texture, is modeled as a stationary or cyclostationary process. It is characterized by a long correlation time. Two approaches for the estimation of the texture are introduced here. The first approach involves the correlation properties of texture and speckle. The texture spectrum is modeled as an autoregressive (AR) process, while the texture range profile within one pulse repetition interval (PRI) is identified by combining the AR estimation of the sea clutter sample auto-covariance with the empirical orthogonal functions (EOF) analysis. The second approach involves a mixed l2-l1 norm minimization criterion to account for the sparse harmonic structure of the texture, considered herein as a cyclostationary process within multiple coherent processing intervals (CPI).