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Mobile cellular radio systems have become complex multi-layered systems with a mixed architecture of macro and micro cells; therefore, a dynamic method of triggering the handover algorithm in such systems is invaluable and required. In this paper the authors develop a simple handover mechanism to adapt for a fast moving mobile station requesting a handover, in which the authors utilise a Doppler frequency estimation in the downlink for adjustment and apply it to a long-term evolution (LTE) network. This method is beneficial for high speed mobiles in macro and/or micro cells, in which for the latter the cell radius is small and needs a dynamic algorithm to respond in a timely manner. The main objective of this study is to investigate the performance of the proposed algorithm; hence, a system layout is specifically designed for this study. Other factors, such as interference, which may affect the performance of the suggested system are not addressed here. The main concern is to study and compare the proposed algorithm to the standard handover algorithm currently implemented in LTE. Simulations at the system level show a marked decrease in the average number of handovers requested.

The article addresses distortions in synthetic aperture radar (SAR) images caused by the non-linear motion of the radar platform. Such motion, often due to navigation errors, environmental factors, or maneuvering, introduces Doppler frequency components into the received signal. These distortions lead to reduced image intensity and multiple displaced replicas of objects along the flight path. Analytical and simulation results show intensity can drop to 45% of the undistorted value, with object replicas appearing at regular intervals related to Doppler shift and system parameters. To counter this, the paper proposes a method that estimates Doppler components and applies a time-dependent phase correction via numerical integration. This correction is implemented before standard SAR processing and does not require precise knowledge of the platform’s trajectory. The study has a predominantly theoretical character, and the validation of the proposed method was carried out exclusively through numerical simulations without the use of real experimental data. Simulations show that with up to 20% estimation error, image intensity is restored to 85% and artifacts are suppressed. The method is efficient, practical, and compatible with existing SAR systems.

Accurate Doppler frequency estimation for Low Earth Orbit (LEO)-based Signals of Opportunity (SoOP) positioning faces significant challenges from extreme dynamics (±40 kHz Doppler shift, 0.4 Hz/ms fluctuation) and severe SNR fluctuations induced by multi-beam switching. Empirical analysis reveals that phased-array beamforming generates three-tiered SNR fluctuation patterns during unpredictable beam handovers, rendering conventional single-algorithm solutions fundamentally inadequate. To address this limitation, we propose KDFE (KNN-Driven Fusion Estimator)—an adaptive framework integrating the Rife–Vincent algorithm and MLE via intelligent switching. Global FFT processing extracts real-time Doppler-SNR parameter pairs, while a KNN-based arbiter dynamically selects the optimal estimator by: (1) Projecting parameter pairs into historical performance space, (2) Identifying the accuracy-optimal algorithm for current beam conditions, and (3) Executing real-time switching to balance accuracy and robustness. This decision model overcomes the accuracy-robustness trade-off by matching algorithmic strengths to beam-specific dynamics, ensuring optimal performance during abrupt SNR transitions and high Doppler rates. Both simulations and field tests demonstrate KDFE’s dual superiority: Doppler estimation errors were reduced by 26.3% (vs. Rife–Vincent) and 67.9% (vs. MLE), and 3D positioning accuracy improved by 13.6% (vs. Rife–Vincent) and 49.7% (vs. MLE). The study establishes a pioneering framework for adaptive LEO-SoOP positioning, delivering a methodological breakthrough for LEO navigation.

Synthetic aperture radar (SAR) imaging has important application potential in sea environments research, such as submarine detection. It has become one of the most significant research topics in the current SAR imaging field. In order to promote the development and application of SAR imaging technology, a MiniSAR experiment system is designed and developed, which provides a platform for related technology investigation and verification. A flight experiment is then conducted to detect the movement of an unmanned underwater vehicle (UUV) through the wake, which can be captured by SAR. This paper introduces the basic structure and the performance of the experimental system. The key technologies for Doppler frequency estimation and motion compensation, the implementation of the flight experiment, and the image data processing results are given. The imaging performances are evaluated, and the imaging capabilities of the system are verified. The system provides a good experimental verification platform to construct the follow-up SAR imaging dataset of UUV wake and investigate related digital signal processing algorithms.

A rigorous mathematical description of the signal reflected from a moving object for radar monitoring tasks using linear frequency modulated continuous wave (LFMCW) microwave radars is proposed. The mathematical model is based on the quasi-relativistic vector transformation of coordinates and Lorentz time. The spatio-temporal structure of the echo signal was obtained taking into account the transverse component of the radar target speed, which made it possible to expand the boundaries of the range of measuring the range and speed of vehicles using LFMCW radars. An algorithm for the simultaneous estimation of the range, radial and transverse components of the velocity vector of an object from the observation data of the time series during one frame of the probing signal is proposed. For an automobile 77 GHz microwave LFMCW radar, a computer experiment was carried out to measure the range and velocity vector of a radar target using the developed mathematical model of the echo signal and an algorithm for estimating the motion parameters. The boundaries of the range for measuring the range and speed of the target are determined. The results of the performed computer experiment are in good agreement with the results of theoretical analysis.

In this study, the authors discuss the topic of joint angle and Doppler frequency estimation in a monostatic multiple-input–multiple-output radar and a compressed sensing parallel factor (CS-PARAFAC) analysis-based algorithm is proposed. In this algorithm, the joint estimation problem is firstly linked to the compressed sensing trilinear model, then the estimated compressed matrix can be derived through trilinear alternating least square method and the angle and Doppler frequency are jointly estimated with sparsity from the compressed matrices. The proposed CS-PARAFAC algorithm, which can obtain automatically paired angle and Doppler frequency estimation, has very close estimation performance to the conventional parallel factor analysis algorithm. When compared to the conventional subspace-based algorithm, such as estimation of signal parameters via rotational invariance techniques, it can achieve much better joint angle and Doppler frequency estimation performance. As the compression, the proposed algorithm has much lower computational complexity and smaller memory capacity meanwhile. Numerical simulations verify the efficiency and illustrate performance improvement of the proposed algorithm.

Weak-signal and high-dynamics are of two primary concerns of space navigation using GNSS (Global Navigation Satellite System) in the space service volume (SSV). The paper firstly defines a reference assumption third-order phase-locked loop (PLL) as the baseline of an onboard GNSS receiver, and proves the incompetence of this conventional architecture. Then an adaptive four-state Kalman filter (KF)-based algorithm is introduced to realize the optimization of loop noise bandwidth, which can adaptively regulate its filter gain according to the received signal power and line-of-sight (LOS) dynamics. To overcome the matter of losing lock in weak-signal and high-dynamic environments, an open loop tracking strategy aided by an inertial navigation system (INS) is recommended, and the traditional maximum likelihood estimation (MLE) method is modified in a non-coherent way by reconstructing the likelihood cost function. Furthermore, a typical mission with combined orbital maneuvering and non-maneuvering arcs is taken as a destination object to test the two proposed strategies. Finally, the experiment based on computer simulation identifies the effectiveness of an adaptive four-state KF-based strategy under non-maneuvering conditions and the virtue of INS-assisted methods under maneuvering conditions.

In this paper, a blind channel and Doppler frequency shift (DFS) estimation method based on cyclostationarity is proposed for orthogonal frequency division multiplexing (OFDM) transmission under impulse noise environment. The proposed method begins with a compressing transform (CT) applied to the received signal to mitigate the adverse effects of impulse noise on estimation performance. Through analysis, it is found that the energy distributions of the cyclic correlation functions of the OFDM signal and Gaussian white noise (AWGN) before and after CT remain unchanged. Furthermore, it is shown that the energy of the cyclic correlation function of the impulse noise after CT only exists at zero cyclic frequency and delay variables. Based on these properties, we simplify the cyclic spectral functions and construct Toeplitz matrices by carefully selecting cycle frequency and delay variables. This selection is guided by the distinct energy distributions of the cyclic correlation functions corresponding to the OFDM signal, impulse noise, and AWGN, respectively. Thus, a matrix equation based on cyclic spectral functions is further constructed. Based on these analyses, DFS and channel estimation values are derived by solving this equation. The simulation results show that the proposed method has lower mean square errors (MSE).

What are the main findings? * A novel high-resolution radial ocean surface current (OSC) velocity estimation method is proposed, utilizing Maximum A Posteriori (MAP) estimation based on signal modeling of the local Doppler power spectrum. * Compared with existing algorithms, the proposed method substantially improves retrieval accuracy while effectively resolving the azimuth doppler ambiguity problem. A novel high-resolution radial ocean surface current (OSC) velocity estimation method is proposed, utilizing Maximum A Posteriori (MAP) estimation based on signal modeling of the local Doppler power spectrum. Compared with existing algorithms, the proposed method substantially improves retrieval accuracy while effectively resolving the azimuth doppler ambiguity problem. What are the implications of the main findings? * Obtaining more precise and reliable radial OSC data from SAR is crucial for ocean current research. * The proposed method is beneficial for high-resolution operational ocean monitoring utilizing SAR image data. Obtaining more precise and reliable radial OSC data from SAR is crucial for ocean current research. The proposed method is beneficial for high-resolution operational ocean monitoring utilizing SAR image data. The retrieval of radial ocean surface current from Synthetic Aperture Radar (SAR) data is important for ocean current research and effective ocean remote sensing. Existing algorithms, primarily based on the Average Cross-Correlation Coefficient (ACCC) method, suffer from drawbacks, including low Doppler frequency-shift estimation accuracy and susceptibility to azimuth ambiguity, hindering accurate measurements. To address these limitations, this paper proposes a method for high-resolution radial current velocity estimation. This approach employs Maximum A Posteriori (MAP) estimation based on signal modeling of the local Doppler power spectrum. This method achieves better Doppler frequency shift estimation accuracy than ACCC and effectively mitigates the azimuth ambiguity, substantially enhancing the precision of radial ocean surface velocity estimation. The algorithm was validated using raw Sentinel-1 Strip-map mode real data and HYCOM data acquired over the Seychelles Islands on 23 April 2023, and the central Indian Ocean (south of the equator) on 20 May 2023. Compared with the Sentinel-1 Level 2 ocean Surface Radial Velocity (RVL) product, the method demonstrates the improvements in both spatial resolution and retrieval accuracy. Specifically, the quantitative comparison with HYCOM data showed a reduction in Root Mean Square Error (RMSE) of up to 34.3% and an improvement in Mean Absolute Error (MAE) of up to 32.1%. Moreover, its ability to suppress the azimuth Doppler ambiguity is demonstrated in the real-data experiment.

In this article, we develop a novel algorithm for Doppler acquisition in fast Fourier transform (FFT)-based Global Navigation Satellite System (GNSS) receivers. The Doppler estimation is carried out in FFT domain by finding the frequency shift which maximizes the energy of the correlation vector. Subsequently, energy detection is used for preliminary decision about the presence of the target code. Then, the final decision and code phase estimation are done in the time domain after taking the inverse fast Fourier transform (IFFT). It is shown that the proposed algorithm has the potential for reducing the average number of required IFFTs in the acquisition process. For improving the sensitivity of the proposed approach, time-domain block averaging and FFT-domain non-coherent integration are investigated as alternative methods. They exhibit rather similar performance improvement, but the non-coherent integration approach is found to be computationally more effective.