Search Results (9)

This paper explores effective methods for taming rubidium atomic clocks with longwave timing signals. In an in-depth analysis of the time-difference data between the 1PPS timing signal output from the ground-wave signal received by a long-wave receiver and the 1PPS signal from UTC, we observe that the time-difference data has significant short-term jitter and long-term periodicity effects. To meet this challenge, we adopt several innovative strategies. First, we use the Fourier transform algorithm to analyse the time-frequency characteristics of the time-difference data in detail and accordingly propose a de-jittering correction algorithm for the long-wave timing data, which is aimed at improving the stability of the long-wave timing signals. Secondly, the time difference model of the rubidium clock is innovatively modified, and a quadratic polynomial superimposed with a periodic fluctuation term is constructed, which can accurately solve and eliminate the periodic components and obtain smoother time difference data. Finally, the parameters of the rubidium clock are accurately estimated by the least-squares method using the corrected smoother time difference data, and the output frequency of the rubidium clock is adjusted accordingly so that the rubidium clock is tamed effectively by the long-wave timing signal successfully. The experimental results show that the long-term stability of the tamed rubidium clock is significantly improved to 3.52 × 10⁻¹³/100,000 s; meanwhile, the phase deviation of the output 1PPS from the UTC of the tamed rubidium clock after entering the stabilisation period is kept within 25 ns. Full article

Translational motion compensation constitutes a pivotal and essential procedure in inverse synthetic aperture radar (ISAR) imaging. Many researchers have previously proposed their methods to address this requirement. However, conventional methods may struggle to produce satisfactory results when dealing with non-stationary moving targets or operating under conditions of low signal-to-noise ratios (SNR). Aiming at this challenge, this article proposes a parametric non-search method that contains two main stages. The radar echoes can be modeled as polynomial phase signals (PPS). In the initial stage, the energy of the received two-dimensional signal is coherently integrated into a peak point by leveraging phase difference (PD) and Lv’s distribution (LVD), from which the high-order polynomial coefficients can be obtained accurately. The estimation of the first-order coefficients is conducted during the second stage. The auto-cross-correlation function for range profiles is introduced to enhance the accuracy and robustness of estimation. Subsequently, a novel mathematical model for velocity estimation is proposed, and its least squares solution is derived. Through this model, a sub-resolution solution can be obtained without requiring interpolation. By employing all the estimated polynomial coefficients, the non-stationary motion of the target can be fully compensated, yielding the acquisition of a finely focused image. Finally, the experimental findings validate the superiority and robustness of the proposed method in comparison to state-of-the-art approaches. Full article

To increase the global convergence and processing efficiency of particle swarm optimization (PSO) applied in the adaptive joint time-frequency, in this study an improved PSO is proposed to refocus the high-resolution SAR images of complex moving vessels in high sea states. According to the characteristics of the high-order multi-component polynomial phase signal, this algorithm provides parallel processing and co-evolution methods by setting the different permissions of the sub-population and sharing its search information. As a result, the multiple components can be extracted simultaneously. Experiments were conducted using the simulation data and Gaofen-3 (GF-3) SAR data. Results showed the processing speed increased by more than 40% and the global convergence was significantly improved. The imaging results verify the efficiency and robustness of this co-evolutionary PSO. Full article

Conventional differential synthetic aperture radar tomography (D-TomoSAR) can only capture the scatterers’ one-dimensional (1-D) deformation information along the line of sight (LOS) of the synthetic aperture radar (SAR), which means that it cannot retrieve the three-dimensional (3-D) movements of the ground surface. To retrieve the 3-D deformation displacements, several methods have been proposed; the performance is limited due to the insufficient sensitivity for retrieving the North-South motion component. In this paper, an improved D-TomoSAR model is established by introducing the scatterers’ 3-D deformation parameters in slant range, azimuth, and elevation directions into the traditional D-TomoSAR model. The improved D-TomoSAR can be regarded as a multi-component two-dimensional (2-D) polynomial phase signal (PPS). Then, an effective algorithm is proposed to retrieve the 3-D deformation parameters of the ground surface by the 2-D product high-order ambiguity function (PHAF) with the relax (RELAX) algorithm. The estimation performance is investigated and compared with the traditional algorithm. Simulations and experimental results with semi-real data verify the effectiveness of the proposed signal model and algorithm. Full article

The echo of maneuvering targets can be expressed as a multicomponent polynomial phase signal (mc-PPS), which should be processed by time frequency analysis methods, while, as a modified maximum likelihood (ML) method, the frequency domain extraction-based adaptive joint time frequency (FDE–AJTF) decomposition method is an effective tool. However, the key procedure in the FDE–AJTF method is searching for the optimal parameters in the solution space, which is essentially a multidimensional optimization problem with different extremal solutions. To solve the problem, a novel multicomponent particle swarm optimization (mc-PSO) algorithm is presented and applied in the FDE–AJTF decomposition with the new characteristic that can extract several components simultaneously based on the feature of the standard PSO, in which the population is divided into three groups and the neighborhood of the best particle in the optimal group is set as the forbidden area for the suboptimal group, and then two different independent components can be obtained and extracted in one extraction. To analyze its performance, three simulation tests are carried out and compared with a standard PSO, genetic algorithm, and differential evolution algorithm. According to the tests, it is verified that the mc-PSO has the best performance in that the convergence, accuracy, and stability are improved, while its searching times and computation are reduced. Full article

In high-resolution spaceborne synthetic aperture radar (SAR), imaging of moving ship targets is strongly influenced by ships’ complex three-axis motions, so that imaging results are fuzzy and unfocused. Yet scattered and moving information on ship targets is wholly contained in the complex image data. This paper proposes a novel SAR and inverse SAR (SAR–ISAR) hybrid imaging method to improve imaging effects, using this complex SAR image data on ship targets, and based on frequency-domain-extraction-based adaptive joint time frequency (FDE–AJTF) decomposition. First, complex SAR image data is transformed to the Doppler domain in the azimuth dimension, and the optimum azimuth data are selected. Next, the signal in each range cell is decomposed to its polynomial phase signal (PPS) components by FDE–AJTF. Finally, a two-dimensional image of the ship target at a given azimuth time is constructed directly. The feasibility and effectiveness of this proposed imaging method is verified through comparisons with conventional methods in simulation and experimental tests. Full article

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. Full article

The maneuvering target echo of high-resolution radar can be expressed as a multicomponent polynomial phase signal (mc-PPS). However, with improvements in radar resolution and increases in the synthetic period, classical time frequency analysis methods cannot satisfy the requirements of maneuvering target radar echo processing. In this paper, a novel frequency domain extraction-based adaptive joint time frequency (FDE-AJTF) decomposition method was proposed with three improvements. First, the maximum frequency spectrum of the phase compensation signal was taken as the fitness function, while the fitness comparison, component extraction, and residual updating were operated in the frequency domain; second, the time window was adopted on the basis function to fit the uncertain signal component time; and third, constant false alarm ratio (CFAR) detection was applied in the component extraction to reduce the ineffective components. Through these means, the stability and speed of phase parameters estimation increased with one domination ignored in the phase parameter estimation, and the accuracy and effectiveness of the signal component extraction performed better with less influence from the estimation errors, clutters, and noises. Finally, these advantages of the FDE-AJTF decomposition method were verified through a comparison with the classical method in simulation and experimental tests. Full article

Inverse synthetic aperture radar (ISAR) imaging of a maneuvering target is a challenging task in the field of radar signal processing. The azimuth echo can be characterized as a multi-component polynomial phase signal (PPS) after the translational compensation, and the high quality ISAR images can be obtained by the parameters estimation of it combined with the Range-Instantaneous-Doppler (RID) technique. In this paper, a novel parameters estimation algorithm of the multi-component PPS with order three (cubic phase signal-CPS) based on the modified discrete polynomial-phase transform (MDPT) is proposed, and the corresponding new ISAR imaging algorithm is presented consequently. This algorithm is efficient and accurate to generate a focused ISAR image, and the results of real data demonstrate the effectiveness of it. Full article