Haihong Tao

Axis rotation MTD algorithm for weak target detection

Weak target@?s range migration often happens for long time integration in radar detection. To compensate for linear range migration and detect weak targets effectively, a novel coherent integration detection algorithm, axis rotation moving target detection (AR-MTD), is proposed. The AR-MTD eliminates the linear range migration by rotating two-dimensional echoes data plane, and realizes coherent integration via moving target detection (MTD). As the target@?s resident time in a single range cell increases, the integration gain is improved clearly. Numerical experiments are presented to verify the reduction in the computational complexity of AR-MTD by selecting the velocity variation regions. Also, it is shown that the detection probability of AR-MTD is improved by nearly 20% when input signal-to-noise ratio (SNR) is -40 dB.

Passive Localization of Mixed Far-Field and Near-Field Sources without Estimating the Number of Sources

This paper presents a novel algorithm for the localization of mixed far-field sources (FFSs) and near-field sources (NFSs) without estimating the source number. Firstly, the algorithm decouples the direction-of-arrival (DOA) estimation from the range estimation by exploiting fourth-order spatial-temporal cumulants of the observed data. Based on the joint diagonalization structure of multiple spatial-temporal cumulant matrices, a new one-dimensional (1-D) spatial spectrum function is derived to generate the DOA estimates of both FFSs and NFSs. Then, the FFSs and NFSs are identified and the range parameters of NFSs are determined via beamforming technique. Compared with traditional mixed sources localization algorithms, the proposed algorithm avoids the performance deterioration induced by erroneous source number estimation. Furthermore, it has a higher resolution capability and improves the estimation accuracy. Computer simulations are implemented to verify the effectiveness of the proposed algorithm.

Detection of constant radial acceleration weak target via IAR-FRFT

A novel coherent integration algorithm, improved axis rotation fractional Fourier transform (IAR-FRFT), is proposed to detect the weak targets with a constant radial acceleration. IAR-FRFT could eliminate the linear range migration and alleviate the nonlinear range migration via an improved axis rotation transform and realize coherent integration via fractional Fourier transform. Numerical experiments verify the performance of IAR-FRFT on four aspects: coherent integration time, coherent integration gain, computational complexity, and multitarget detection.

Efficient Method of Passive Localization for Near-Field Noncircular Sources

In this letter, a novel near-field localization algorithm for noncircular sources is proposed using the uniform linear array (ULA). Firstly, an extended covariance matrix is constructed and decomposed to generate the corresponding extended signal subspace and noise subspace. Then, based on the symmetric property of the extended array manifold, the direction-of-arrival (DOA) estimates are obtained via generalized ESPRIT. Finally, the range spectrum function is derived, and the range parameters are consequently estimated through one-dimensional (1-D) search. The proposed algorithm is efficient in that it only requires second-order statistics (SOS) and 1-D spectral search. In addition, compared with the conventional SOS-based methods, the proposed algorithm can improve the estimation accuracy and resolve more sources. Simulation results are presented to validate the performance of the proposed algorithm.

Localization of mixed far-field and near-field sources under unknown mutual coupling

In this paper, a novel localization algorithm for mixed far-field and near-field sources is proposed in the presence of unknown mutual coupling. Based on the principle of rank reduction, direction-of-arrival (DOA) estimates of far-field sources are firstly decoupled under unknown mutual coupling. Then these estimates are employed to generate the mutual coupling coefficients. Finally, by the mutual coupling compensation and the far-field components elimination, near-field sources parameters (DOA and range) are obtained. The proposed algorithm is efficient in that it only requires second order statistics and one dimensional spectral search. Simulation results demonstrate that our algorithm is effective for the classification and localization of mixed sources under unknown mutual coupling.

Comments on “Near-Field Source Localization via Symmetric Subarrays”

In the aforementioned letter, the authors indicate that with a 2M + 1 sensor uniform linear array (ULA), up to 2M - 1 sources can actually be localized by the proposed algorithm. In this comment, however, we prove that the algorithm will no longer be valid if the number of sources exceeds M. A numerical simulation is performed to verify this conclusion.

Robust multicomponent LFM signals synthesis algorithm based on masked ambiguity function

Wigner distribution (WD) based multiple components linear frequency modulation (LFM) signal synthesis method (SSM) is often adversely affected by cross-terms. Masked WD (MWD) is one of the widely used cross-term suppression techniques in practice due to its simplicity and efficiency. However, the cross-terms are hardly masked out when auto-terms and cross-terms are overlapped (Case I) or the components are very close to each other in the time-frequency (TF) plane (Case II). To solve these problems, we present a robust ambiguity function (AF) based approach for multicomponent signals synthesis. This algorithm consists of two stages. First, a SSM from the AF is proposed according to matrix rearrangement and eigenvalue decomposition. However, the existence of cross-term makes the signal synthesis entirely erroneous. To settle this issue, we present a masked AF (MAF) algorithm based on Radon and its inverse transforms in the second stage. Applying the presented algorithm, multicomponent signals can be synthesized efficiently even in Case I and Case II. Simulation results demonstrate the effectiveness and feasibility of the proposed algorithm.

Imaging and Doppler parameter estimation for maneuvering target using axis mapping based coherently integrated cubic phase function

Abstract In this paper, we propose a novel imaging and Doppler parameter estimation algorithm for ground maneuvering targets. Since the cross-track acceleration will induce the quadratic chirp rate (third-order phase) in the phase history, it may cause the maneuvering target severely smeared in the Doppler domain. To obtain a well-focused target imaging result, the quadratic chirp rate must be estimated accurately. Though cubic phase function (CPF) is efficient in estimating the parameters of a single maneuvering target, it may suffer from the identifiability problem when dealing with multiple maneuvering targets. To address these issues, an axis mapping (AM) based coherently integrated cubic phase function (CICPF) algorithm is proposed. This algorithm consists of two stages. Firstly, the linear chirp rate migration (i.e. quadratic chirp rate) of target in the time and chirp-rate domain is corrected by AM. After that, a dechirping technique is utilized to coherently integrate the auto-terms, and suppress the cross-terms and spurious peaks. Compared with several existing quadratic chirp rate estimation approaches, AM based CICPF (AMCICPF) algorithm can acquire lower signal-to-noise ratio threshold and estimate the centroid frequency, chirp rate and quadratic chirp rate of maneuvering target simultaneously. By compensating the chirp rate and quadratic chirp rate, a finely focused maneuvering target imaging can be obtained. Both simulated and real data processing results show that the AMCICPF algorithm serves as a good candidate for maneuvering target Doppler parameter estimation and imaging.

Narrow-Band Interference Suppression via RPCA-Based Signal Separation in Time–Frequency Domain

Narrow-band interference (NBI) is a critical issue for synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To suppress NBI effectively, a novel interference suppression algorithm using robust principal component analysis (RPCA) based signal separation in time–frequency domain is proposed. The RPCA algorithm is introduced for signal separation in the time–frequency domain for the first time. The fundamental assumption of RPCA is that a matrix can be modeled as a combination of a low-rank matrix and a sparse counterpart. In terms of the SAR echo, the short time Fourier transformation (STFT) matrix of mixed signals (i.e., useful SAR signals and NBIs) well fits the assumption of RPCA. Based on this property, radar echoes are first transformed into the time–frequency domain by STFT to form an STFT matrix. Then, the RPCA algorithm is used to decompose the STFT matrix into a low-rank matrix (i.e., NBIs) and a sparse matrix (i.e., useful signals). Finally, the NBIs can be reconstructed and subtracted from the echoes to realize the interference suppression. The experimental results of simulated and measured data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.

Time-Varying SAR Interference Suppression Based on Delay-Doppler Iterative Decomposition Algorithm

Narrow-band interference (NBI) and Wide-band interference (WBI) are critical issues for synthetic aperture radar (SAR), which degrades the imaging quality severely. Since some complex signals can be modeled as linear frequency modulated (LFM) signals within a short time, LFM-WBI and NBI are mainly discussed in this paper. Due to its excellent energy concentration and useful properties (i.e., auto-terms pass through the origin of Delay-Doppler plane while cross-terms are away from it), a novel nonparametric interference suppression method using Delay-Doppler iterative decomposition algorithm is proposed. This algorithm consists of three stages. First, we present signal synthesis method (SSM) from ambiguity function (AF) and cross ambiguity function (CAF) based on the matrix rearrangement and eigenvalue decomposition. Compared with traditional SSM from Wigner distribution (WD), the proposed SSM can synthesize a signal faster and more accurately. Then, based on unique properties in Delay-Doppler domain, a mask algorithm is applied for interference identification and extraction using Radon and its inverse transformation. Finally, a signal iterative decomposition algorithm (IDA) is utilized to subtract the largest interference from the received signal one by one. After that, a well-focused SAR imagery is obtained by conventional imaging methods. The simulation and measured data results demonstrate that the proposed algorithm not only suppresses interference efficiently but also preserves the useful information as much as possible.