Yu Wang

In this paper, we propose a parameter estimation method named improved Lv distribution (ImLVD) for linear frequency modulated (LFM) signals. In this method, based on the scaling principle, we present a two-scale estimation strategy to acquire low computational cost. The two-scale estimation strategy includes coarse estimation operation and fine estimation operation. To implement the strategy, improved scaled Fourier transform (ISFT) and Chirp-Z transform (CZT) operation are used, where the ISFT can be implemented by fast Fourier transform (FFT) and complex multiplications. By changing the frequency searching range of ISFT, the ISFT is used in different frequency ranges. To improve the anti-noise, we present an improved parameter selection criterion which can reduce the noise correlation effectively. In this paper, the implementation, anti-noise performance, and computational cost are analyzed. Through simulations and analyses, we demonstrate that the ImLVD outperforms the compared algorithms.

This paper considers the problems of synchronous orthogonal frequency-hopping (FH) signals separation and direction of arrival (DOA) estimation in the underdetermined situation. First of all, in order to estimate the mixing matrix of the FH signals, we stack the two-dimensional covariance matrices of the received FH signals into a third-order tensor, and then, an estimation approach based on the tensor decomposition method is developed to calculate this mixing matrix. Next, to separate these FH signals, a modified source recovery method is proposed. Finally, the DOAs of the FH signals are also obtained. Numerical results demonstrate that compared to the existing algorithms, the proposed algorithm not only presents superior performance but also requires less sparsity limitation and cost time.

This paper addresses the problem of estimating frequency hopping (FH) signals parameters with the single array. The existing optimization algorithms are with unrealistic computational burden. In order to reduce the computation burden, a novel method, based on orthogonal matching pursuit and sparse linear regression (OSLR), is proposed in this paper. The OSLR method consists of two steps. First, by segmenting the received signals into several measurements, orthogonal matching pursuit is used to detect whether the segments include hop timings or not. Second, sparse linear regression is used to estimate the spectrogram of FH signals for the segmentations with hop timings. Numerical simulations demonstrate that the OSLR method can achieve superior performance, and the OSLR method can be exploited to deal with underdetermined blind source separation problem of FH signals.

Polynomial phase signals (PPSs) have numerous applications in many fields including radar, sonar, geophysics, and radio communication systems. Therefore, estimation of PPS coefficients is very important. In this paper, a novel approach for PPS parameters estimation based on adaptive short-time Fourier transform (ASTFT), called the PPS-ASTFT estimator, is proposed. Using the PPS-ASTFT estimator, both one-dimensional and multi-dimensional searches and error propagation problems, which widely exist in PPSs field, are avoided. In the proposed algorithm, the instantaneous frequency (IF) is estimated by S-transform (ST), which can preserve information on signal phase and provide a variable resolution similar to the wavelet transform (WT). The width of the ASTFT analysis window is equal to the local stationary length, which is measured by the instantaneous frequency gradient (IFG). The IFG is calculated by the principal component analysis (PCA), which is robust to the noise. Moreover, to improve estimation accuracy, a refinement strategy is presented to estimate signal parameters. Since the PPS-ASTFT avoids parameter search, the proposed algorithm can be computed in a reasonable amount of time. The estimation performance, computational cost, and implementation of the PPS-ASTFT are also analyzed. The conducted numerical simulations support our theoretical results and demonstrate an excellent statistical performance of the proposed algorithm.

This paper considers the complex-valued mixing matrix estimation and direction-of-arrival (DOA) estimation of synchronous orthogonal frequency hopping (FH) signals in the underdetermined blind source separation (UBSS). A novel mixing matrix estimation algorithm is proposed by detecting single source points (SSPs) where only one source contributes its power. Firstly, the proposed algorithm distinguishes the SSPs by the comparison of the normalized coefficients of time frequency (TF) points, which is more effective than existing detection algorithms. Then, mixing matrix of FH signals can be estimated by the hierarchical clustering method. To sort synchronous orthogonal FH signals, a modified subspace projection method is presented to obtain the DOAs of FH. One superiority of this paper is that the estimation accuracy of the mixing matrix can be significantly improved by the proposed SSPs detection criteria. Another superiority of this paper is that synchronous orthogonal FH signals can be sorted in underdetermined condition. The experimental results demonstrate the efficiency of the two proposed algorithms.

For targets with complex motion, such as ships fluctuating with oceanic waves and high maneuvering airplanes, azimuth echo signals can be modeled as multicomponent quadratic frequency modulation (QFM) signals after migration compensation and phase adjustment. For the QFM signal model, the chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities, which need to be estimated. For multicomponent QFM signals, the cross terms create a challenge for detection, which needs to be addressed. In this paper, by employing a novel multi-scale parametric symmetric self-correlation function (PSSF) and modified scaled Fourier transform (mSFT), an effective parameter estimation algorithm is proposed—referred to as the Two-Dimensional product modified Lv’s distribution (2D-PMLVD)—for QFM signals. The 2D-PMLVD is simple and can be easily implemented by using fast Fourier transform (FFT) and complex multiplication. These measures are analyzed in the paper, including the principle, the cross term, anti-noise performance, and computational complexity. Compared to the other three representative methods, the 2D-PMLVD can achieve better anti-noise performance. The 2D-PMLVD, which is free of searching and has no identifiability problems, is more suitable for multicomponent situations. Through several simulations and analyses, the effectiveness of the proposed estimation algorithm is verified.

In this paper, we address the problem of detecting and tracking targets with a low signal-to-noise ratio (SNR) by exploiting hybrid differential evolution (HDE) in the particle filter track-before-detect (PF-TBD) context. Firstly, we introduce the Bayesian PF-TBD method and its weaknesses. Secondly, the HDE algorithm is regarded as a novel particle updating strategy, which is proposed to optimize the performance of the PF-TBD algorithm. Thirdly, we combine the systematic resampling approach to enhance the performance of the proposed algorithm. Then, an improved PF-TBD algorithm based on the HDE method is proposed. Experiment results indicate that the proposed method has better performance in detecting and tracking than previous algorithms when the targets have a low SNR.