Matched Filter Research Articles

L1- and F-norm joint edge preservation optimization algorithm for complex image sparse SAR reconstruction imaging model

ABSTRACT Synthetic aperture radar (SAR) is widely used for ground observations owing to its all-weather and all-time capabilities. Sparse SAR reconstruction imaging, which can enhance SAR imaging performance, has emerged as a significant research area. The traditional sparse SAR reconstruction imaging method was successfully applied to raw SAR data processing. However, the computational complexity makes sparse reconstruction imaging difficult for large scenes. Meanwhile, because raw data are usually kept confidential, only limited data are available. To address the issue of how to use publicly available complex image data for sparse reconstruction and effectively improve image quality, this study proposes a joint L1- and F-norm edge-preservation-based sparse SAR reconstruction algorithm for complex images. The algorithm uses the gradient descent (GD) technique to optimize the L1-norm regularization and reconstruct the overall image information; the iterative soft thresholding algorithm (ISTA) to optimize the F-norm regularization and reconstruct the high-frequency image information; and fuses the two sets of reconstructed information to obtain SAR reconstructed images with characteristics such as edge preservation, clear texture, and high quality. The results show that, compared with traditional matched filtering (MF) imaging results, the joint L1- and F-norm results obtained for complex images have lower sidelobes, higher signal-to-noise ratio, and better target resolution ability; compared with the F-norm-based sparse SAR reconstruction imaging results, the joint L1- and F-norm method provides images with better edge texture clarity.

A Deep Learning Model for Detecting the Arrival Time of Weak Underwater Signals in Fluvial Acoustic Tomography Systems

The fluvial acoustic tomography (FAT) system relies on the arrival time of the system signal to calculate the parameters of the region. The traditional method uses the matching filter method to calculate the peak position of the received acoustic signal after cross-correlation calculation within a certain time as the signal arrival time point, but this method is difficult to be effectively applied to the complex underwater environment, especially in the case of extremely low SNR. To solve this problem, a two-channel deep learning model (DCA-Net) is proposed to detect the arrival time of acoustic chromatographic signals. Firstly, an interactive module is designed to transmit the auxiliary information from the cross-correlation subnetwork to the original signal subnet to improve the feature information extraction capability of the network. In addition, an attention module is designed to enable the network to selectively focus on the important features of the received acoustic signals. Under the background of white Gaussian noise and real river environment noise, we use the received signals of the acoustic tomography system collected in the field to synthesize low SNR data of −10, −15, and −20 different decibels as datasets. The experimental results show that the proposed network model is superior to the traditional matching filtering method and some other deep neural networks in three low SNR datasets.

Analysis of 6G and B5G waveforms using hybrid MF-ED and ECG-ED spectrum sensing techniques

The rapid evolution of wireless communication has necessitated advanced waveform analysis for beyond-fifth-generation (B5G) and sixth-generation (6G) radio networks, focusing on efficient spectrum utilization. There is a need for greater spectrum allotment in data-intensive applications, and new technologies require faster data rates and reduced latency. This study explores hybrid spectrum sensing techniques, combining matched filter (MF) energy detection (ED) and an equal-gain combining-based energy detection Neyman-Pearson threshold estimation technique (EGC-ED-PTh) to enhance waveform detection accuracy in complex environments. The proposed method offers an enhanced signal-to-noise ratio (SNR) by optimizing the detection performance, particularly in low-SNR environments, thereby improving the signal reliability. The proposed algorithms are evaluated in comparison with traditional SS methods, including ED, MF, and cyclostationary feature detection (CFD). Additionally, characteristics including bit error rate (BER), power spectral density (PSD), probability of detection (pd), and probability of false alarm (pfa) were researched and evaluated for 500 and 1000 samples. The simulation findings show that the projected algorithms perform better than the traditional algorithms with minimum sidelobes of – 3024 and pfa effects and achieve a throughput gain of 5 and 4.7 dB compared with the conventional algorithms.

Method of undetermined coefficients for circuits and filters using Legendre functions

This article presents a new way to implement matching networks and filters using the method of undetermined coefficients. A method is proposed for approximating the transmission coefficient of the synthesized filter, taking into account the required amplitude-frequency characteristics. To synthesize the filter, an approximating function (AF) was used using orthogonal Legendre polynomials, which is a mathematical description using a system of equations. Filter properties whose implementation is based on modified Legendre approximating functions usually depend on the interval on which they are defined and have the property that they are orthogonal on this interval. An example of seventh order filter synthesis using modified Legendre approximating functions is given. The filter circuit is implemented, the elements of the filter circuit are calculated based on the selected approximating modified function. The criteria used were minimization of the unevenness of the group delay time (GDT) and minimization of the complex approximation error for given values of the AF parameters. As a result, the number of filter elements, the group delay value and the complex approximation error are significantly reduced.

On the Solution of Linearized Inverse Scattering Problems in Near-Field Microwave Imaging by Operator Inversion and Matched Filtering

On the Solution of Linearized Inverse Scattering Problems in Near-Field Microwave Imaging by Operator Inversion and Matched Filtering

Experiences with techniques and sensors for smartphone positioning

Abstract In this paper, current alternative techniques and sensors embedded in smart mobile devices for localization are reviewed. Firstly, the evolution of smartphone development since the first IBM phone in 1992 and the introduction of the term ‘smart-phone’ in 2000 until present is discussed. The main part of the paper deals then with the sensors and positioning techniques where an overview about the topologies and positioning methods and their applicability for smartphone positioning is provided. The most important sensors and techniques are then elaborated and discussed in detail. They include MEMS-based sensors and wireless positioning options. The wireless options include Bluetooth, Wi-Fi and UWB which may be employed together with MEMS-based inertial sensors. Also the use of the smartphone camera and LiDAR is briefly discussed. Moreover, the most applicable sensor fusion approaches including Kalman filtering, particle filter and map matching are also analyzed. In concluding the paper, new ways in using smartphones for science based on different application examples are showcased.

Remote sensing analysis and geodynamic setting of magmatic spessartine-almandine-bearing leucogranites, Um Addebaa area, southeastern Desert, Egypt: Bulk rock and mineral chemistry

This study aims to incorporate optical multispectral remote sensing, petrography, geochemical, and field investigations in producing mapping and surface abundance of garnet mineralization within Um Addebaa area, southeastern Desert of Egypt. Datasets of Landsat-8 OLI/TIRS, ASTER and Sentinel-2A (S2A) were handled and scaled enhanced to discriminate the units of lithologic rock and garnet mineralization. The spectral mapping techniques of constrained energy minimization (CEM) and matched filtering (MF) are used to detect the garnet-rich zones which confined to the leucogranites masses. These granites are spessartine-almandine-rich, certainly along the interaction with the ophiolitic mélange. Notably, they have high total alkalis, SiO2, and Al2O3, Rb, 104Ga/Al, Y, Zr, Pb, Rb/Sr, Al2O3/TiO2, and FeO/MgO, and strong Ti, Sr, and Ba depletion as well as low CaO/Na2O ratio (<0.3). These geochemical parameters reflecting calc-alkaline, and strongly peraluminous magma of S-type. They are derived by two episodes of partial melting clay-rich metapelite crustal rocks followed by extreme Fe-Ti oxides and feldspar fractionation. These leucogranites formed within extension tectonic regime during post-collisional episode, resulting raising of asthenosphere and partial melting of the crustal metasedimentary rocks. Garnet occurs as, aggregates and/or vein like-shape, subhedral to rounded grains, homogenous and rarely reveal weak zonation. It’s cracked and free of inclusions. It contains appreciated concentrations of FeO and MnO and minor amounts of CaO and MgO, dominantly spessartine-almandine end-member (Alm52-59Sps 31-36 Grs 8-12 Prp0.5-0.8 Adr0). Garnet chemical composition reflect it is magmatic origin with MnO- FeO-rich magma that is produced from in situ nucleation from strongly peraluminous magmas during post- collision setting.

Doppler Compensation Techniques for M-Ary Sequence Spread Spectrum Signals Based on Correlation Cost Factors in Mobile Underwater Acoustic Communication

Unlike terrestrial radio, the speed of sound in the ocean is relatively slow, which results in mobile underwater M-ary spread spectrum communication typically exhibiting significant and variable multipath effects along with strong Doppler effects, leading to rapid carrier phase shifts in the received signal that severely impact decoding accuracy. This study aims to address the issue of rapid carrier phase shifts caused by significant time-varying Doppler shifts during mobile underwater M-SS communication. This paper innovatively proposes a method for updating matched filters based on correlation cost factors. By calculating the correlation cost factors for each received symbol, the method guides the direction of Doppler estimation and updates the matched filters. After identifying the optimal match, the received symbols are shifted, correlated, and decoded. Simulation and sea trial results indicate that this method demonstrates higher computational efficiency and improved decoding accuracy compared to traditional Doppler estimation matched filters under low signal-to-noise ratio conditions, and exhibits greater robustness under complex motion conditions.

The AstroSat UV Deep Field South. I. Far- and Near-ultraviolet Source Catalog of the GOODS South Region

We present the AstroSat UV Deep Field south (AUDFs), an imaging survey using the wide-field Ultraviolet Imaging Telescope on board AstroSat. AUDFs cover ∼236 arcmin2 of the sky area, including the Great Observatories Origins Deep Survey (GOODS) South field in F154W and N242W filters. The deep and shallow parts of AUDFs have an exposure time ∼62,000 and ∼31,000 s, respectively, in the F154W filter, while in the N242W filter, they are ∼64,000 and ∼34,000 s. These observations reached a 3σ depth of 27.2 and 27.7 AB mag with a 50% completeness limit of 27 and 27.6 AB mag in the F154W and N242W filters, respectively. With the acquired depth, AUDFs is the deepest far- and near-UV imaging data covering the largest area known to date at 1.″2–1.″6 spatial resolution. Two primary catalogs were constructed for the F154W and N242W filters, each containing 13,495 and 19,374 sources brighter than the 3σ detection limit, respectively. Our galaxy counts of a power-law slope of ∼0.43 dex mag−1 in the N242W filter match well with Hubble Space Telescope/Wide-Field Camera 3/UVIS observations. A wide range of extragalactic science can be achieved with this unique data, such as providing a sample of galaxies emitting ionizing photons in the redshift range of z ∼1–3 and beyond, constraining the UV luminosity function, investigating the extended UV emission around star-forming galaxies and UV morphologies for z < 1. The UV catalog will enhance the legacy value of the existing optical/IR imaging and spectroscopic observations from ground- and space-based telescopes on the GOODS South field.

Mangrove Extraction Algorithm Based on Orthogonal Matching Filter-Weighted Least Squares.

High-precision extraction of mangrove areas is a crucial prerequisite for estimating mangrove area as well as for regional planning and ecological protection. However, mangroves typically grow in coastal and near-shore areas with complex water colors, where traditional mangrove extraction algorithms face challenges such as unclear region segmentation and insufficient accuracy. To address this issue, in this paper we propose a new algorithm for mangrove identification and extraction based on Orthogonal Matching Filter-Weighted Least Squares (OMF-WLS) target spectral information. This method first selects GF-6 remote sensing images with less cloud cover, then enhances mangrove feature information through preprocessing and band extension, combining whitened orthogonal subspace projection with the whitened matching filter algorithm. Notably, this paper innovatively introduces Weighted Least Squares (WLS) filtering technology. WLS filtering precisely processes high-frequency noise and edge details in images using an adaptive weighting matrix, significantly improving the edge clarity and overall quality of mangrove images. This innovative approach overcomes the bottleneck of traditional methods in effectively extracting edge information against complex water color backgrounds. Finally, Otsu's method is used for adaptive threshold segmentation of GF-6 remote sensing images to achieve target extraction of mangrove areas. Our experimental results show that OMF-WLS improves extraction accuracy compared to traditional methods, with overall precision increasing from 0.95702 to 0.99366 and the Kappa coefficient rising from 0.88436 to 0.98233. In addition, our proposed method provides significant improvements in other metrics, demonstrating better overall performance. These findings can provide more reliable technical support for the monitoring and protection of mangrove resources.

Signal detection of M-MIMO-orthogonal time frequency space modulation using hybrid algorithms: ZFE + MMSE and ZFE + MF

Orthogonal Time Frequency Space (OTFS) modulation, coupled with Massive Multiple Input Multiple Output (Massive-MIMO) technology, presents a promising avenue for enhancing the efficiency and reliability of fifth-generation (5G) and beyond fifth-generation (B5G) systems. OTFS modulation offers robust communication in high-mobility environments by converting signals into the delay-Doppler domain, ensuring better performance over fading channels. MIMO enhances wireless networks by using large antenna arrays to boost capacity, spectral efficiency, and reliability, making both technologies vital for next-generation radio systems. In this study, we explore the detection of (8 × 8, 16 × 16, 64 × 64, and 256 × 256) Massive-MIMO-OTFS signals utilizing two prominent detection algorithms: zero forcing equalization (ZFE) with matched filter (MF) known as (ZFE + MF) and Zero Forcing with minimum mean square error (MMSE) known as (ZFE + MMSE). The combination of Massive MIMO and OTFS offers improved spectral efficiency, robustness against fading, and enhanced spatial multiplexing capabilities. The parameters such as bit error rate (BER) and power spectral density (PSD) are analyzed and estimated for the proposed hybrid and conventional algorithms. The proposed algorithms obtained the SNR and PSD gain of 3.2 dB, 3.2 dB, 4.8 dB, and 6.1 dB gain, respectively, for 8 × 8, 16 × 16, 64 × 54, and 256 × 256 MIMO systems. Further, the PSD gain of -390 is obtained for the 256 × 256 system, resulting in high spectral efficiency.

Deep Learning-Based Approximated Observation Sparse SAR Imaging via Complex-Valued Convolutional Neural Network

Sparse synthetic aperture radar (SAR) imaging has demonstrated excellent potential in image quality improvement and data compression. However, conventional observation matrix-based methods suffer from high computational overhead, which is hard to use for real data processing. The approximated observation sparse SAR imaging method relieves the computation pressure, but it needs to manually set the parameters to solve the optimization problem. Thus, several deep learning (DL) SAR imaging methods have been used for scene recovery, but many of them employ dual-path networks. To better leverage the complex-valued characteristics of echo data, in this paper, we present a novel complex-valued convolutional neural network (CNN)-based approximated observation sparse SAR imaging method, which is a single-path DL network. Firstly, we present the approximated observation-based model via the chirp-scaling algorithm (CSA). Next, we map the process of the iterative soft thresholding (IST) algorithm into the deep network form, and design the symmetric complex-valued CNN block to achieve the sparse recovery of large-scale scenes. In comparison to matched filtering (MF), the approximated observation sparse imaging method, and the existing DL SAR imaging methods, our complex-valued network model shows excellent performance in image quality improvement especially when the used data are down-sampled.

An Identical Code–Data Based Nonorthogonal Multiple Access

ABSTRACTNonorthogonal multiple access (NOMA) is a promising technique operating with limited resources. NOMA can be executed in both power and code domains. Cyclic interleaved NOMA strategies are debated in recent literature, combining power and code domains. Identical code cyclic shift (ICCS) transmission is viewed to be a bridge between code division access and NOMA. We propose identical code–data cyclic shift NOMA (ICDCS‐NOMA) in which the code and data are cyclically shifted in distinction to simple cyclic interleaved transmission introduced in our earlier work and the ICCS transmission. The cell‐edge user in a NOMA pair engages its signal transmission using ICDCS; in contrast, the cell‐center user prefers simple cyclic interleaved transmission. The spread data stream and its cyclic shifted version are blended in ICDCS, followed by cyclic interleaving. A distinct and extreme fading channel pair is assumed with the same number of paths for the edge and center users. Diversity combining multipath signals is embraced at the edge user terminal, and frequency domain equalization (FDE)/matched filter (MF) receiver is employed at the center user. The simulation indicates the dominance of the proposed ICDCS‐NOMA transmission in terms of the attained diversity gain over the conventional, ICCS, and simple cyclic interleaved transmissions.

Terminological features of the concept of signal-to-noise ratio when processing ultra-wideband signals

Currently, when processing various signals and studying various types of receivers, there are many definitions for one of the basic characteristics of radio systems – signal-to-noise ratio (SNR). At the same time, the terminological feature of the SNR concept is that any definition within the framework of a locally solvable signal processing problem is correct. However, difficulties arise when comparing the effectiveness of one algorithm with another, when studying different signal models, or when measuring the SNR in different parts of the receiving system. Some researchers ignore this fact due to the apparent absolute clarity of this issue, which leads to incorrect comparison of various processing algorithms or the formation of obviously unrealizable requirements for the designed equipment. In each locally solved problem, the determination of SNR will be individual, and comparison of efficiency at a fixed SNR must be carried out with mandatory scaling of signal energies depending on the formulation of the problem. This paper systematizes the terminological features of the SNR concept in order to ensure uniformity of its application by researchers and engineers. The purpose of the work is to systematize the definition of SNR for various signal models and conditions for measuring this characteristic, to specify the results in terms of terminological features of UWB signal processing and to propose a model for recalculating the SNR depending on the stage of the signal transmission and reception process. The paper examines the determination of SNR at the input and output of optimal and matched filters, at the output of optimal receivers operating under conditions of various a priori uncertainties, as well as when processing various classes of signals. By determining the SNR at the output of the matched filter and the SNR at the output of the maximum likelihood receiver for a rectangular video pulse, the discrepancy between the signal energies, all other things being equal, is clearly demonstrated. A connection has been established between the SNR when processing ultra-wideband signals at the output of the optimal receiver with similar devices for processing other types of signals, as well as with the SNR at the input of the receiving system. Systematization of the terminological features of the SNR concept will allow theoretical researchers to choose SNR definitions that are as close as possible to practical applications when analyzing the effectiveness of synthesized algorithms, and will allow practicing engineers to correctly interpret the results of theoretical calculations when developing or testing radio engineering systems.

Management of white spot enamel lesions with resin infiltration: potentials and future research directions.

The current study1 is a randomized controlled trial with two arm, multicenter and parallel group design. Study subjects were 38 orthodontic patients younger than 17 years who were being treated with metal braces. Patients with at least one white spot lesion (WSL) graded 1 to 2 according to the International Caries Detection and Assessment System (ICDAS) on the labial surface of permanent maxillary and mandibular canines and incisors were included for the trial. Teeth with carious lesions and restorations, anomalies of the enamel, and primary teeth were excluded. Patients were allocated by computer generated random sequence into resin infiltration and fluoride varnish intervention groups. Study subjects were blinded until the allocation, outcome assessors and statisticians remained blinded through the study, however the operators could not be blinded. Resin infiltration treatment involved removal of orthodontic wires and auxiliaries followed by cleaning the teeth with fluoride free prophylactic paste and completing the resin infiltration according to manufacturer's instructions. In the fluoride varnish group, a thin layer of the material was applied after isolating the teeth, and patients were asked not to eat or drink for 1 hour. This was continued twice a month for 6 months. Digital images of the teeth were obtained before, and 1 day (T1), 1 week (T2), 1 month (T3), 3 months (T4) and 6 months (T5) after treatment, using a DSLR camera and a matching polarization filter. The images were processed for calibration and color stability. Regions of interest representing WSL (white spot lesion) and SAE (sound adjacent enamel) were isolated in the images for comparison at different stages the images were captured. Statistical analysis was performed using SPSS version 28. Independent-samples t-test was utilized for comparison between the two groups, and paired-samples t-test for comparison within the groups. A statistical significance level of α = 0.05 was set. At T1, significant color difference was observed between white spot lesion and adjacent sound enamel in the resin infiltration group and it remained stable after 6 months. Whereas in the fluoride varnish group, there were no statistical differences from baseline to 6 months. A statistical difference of 3.27 CIELAB units (p < 0.001) was reported between the infiltration group and the fluoride group at T5. No significant changes were noted in SAE with respect to changes in lightness. Resin infiltration was found to be better at masking the demineralization produced by WSL and also enhanced the esthetic appearance of demineralized areas around the brackets. Resin infiltration did not produce any clinically visible effects in non-affected enamel. These changes remained stable for a period of 6 months.

An efficient hybrid spectrum sensing algorithm to enhance the performance of optical NOMA waveforms using 256-QAM

Abstract The ever-increasing demand for bandwidth in optical networks necessitates efficient spectrum utilization. To address this challenge, this paper proposes a novel hybrid spectrum sensing algorithm tailored explicitly for 256-QAM (Quadrature Amplitude Modulation) optical communication waveforms. The proposed algorithm combines the strengths of energy detection and cyclostationary feature detection to overcome the limitations of individual methods. Energy detection (ED) provides fast and low-complexity sensing, while cyclostationary feature detection offers higher accuracy and sensitivity. First, ED is employed for rapid initial spectrum assessment. Subsequently, matched filter (MF) detection is selectively applied only to frequency bands identified as potentially occupied by primary users based on the energy detection results. This selective approach significantly reduces computational complexity while maintaining high detection accuracy. The results demonstrate significant improvements in detection accuracy, sensitivity, and computational efficiency compared to existing methods. In particular, the hybrid algorithm performs better in scenarios where weak 256-QAM signals coexist with strong primary users, showcasing its effectiveness in dynamic spectrum-sharing applications. This work contributes significantly to optical spectrum sensing by offering an efficient and accurate solution for advanced radio systems. The proposed hybrid algorithm paves the way for improved spectrum utilization and facilitates the development of high-performance, next-generation optical networks. The projected method obtained a gain of −200 as compared with the existing methods.

Non-Intrusive Continuous Monitoring of Leaks for an In-Service Penstock.

In modern industries, pipelines play a crucial role, both as an essential element in energy transportation (water, gas and electricity) and also in the distribution of these resources. The large size of piping infrastructures, their age and unpredictable external factors are the main difficulties in monitoring the piping system. In this context, the detection and the localization of leaks are challenging but essential, as leaks lead to substantial economic losses. Current methods have many limitations, involving invasive procedures, working only with short pipes or requiring a system shutdown. This paper presents a non-intrusive method based on acoustic signal processing. Leak detection is performed using matched filters, while localization is performed based on the phase diagram representation method and diagram-based entropy computation. Our continuous monitoring system was used for two months and a full comparison with the video inspection-based technique was conducted. The results indicate that this method has a high accuracy, regardless of the length of the pipe.

Aerosol‐Calibrated Matched Filter Method for Retrievals of Methane Point Source Emissions Over the Los Angeles Basin

AbstractMethane, with a global warming potential roughly 86 times greater than carbon dioxide over a 20‐year timeframe, plays a crucial role in global warming. Remote sensing retrieval is a pivotal methodology for identifying methane emission sources, with accuracy influenced largely by surface and atmospheric properties, including aerosols. In this study, we propose an Aerosol‐Calibrated Matched Filter (ACMF) algorithm to improve the traditional Matched Filter (MF) method. Our new approach incorporates an aerosol scattering correction factor to reduce the aerosol‐induced bias on methane retrievals. Validating our algorithm through simulated spectra, we demonstrate that considering the aerosol scattering effect significantly reduces retrieval errors compared to MF methods by an average of approximately 90%. We apply our newly developed algorithm to hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer—Next Generation in the Los Angeles Basin and focus on 11 plumes identified through case studies. Our results reveal that ACMF estimates of emission rates and inversion uncertainties exhibit an average reduction of approximately 4% compared to corresponding MF results, with deviation increasing with aerosol optical depth (AOD).

Deep learning for ultrasound surface echo detection

Ultrasound array imaging techniques are often applied using a coupling medium to transmit waves from the transducer to the scanned component. In this type of tests, the echoes generated by the wave reflection at the component surface contain information that can be used to estimate the surface shape and its position relative to the probe, which are needed to compute the imaging focal laws. The most interesting feature to extract from surfaces echoes is the Time of Flight (TOF) for each reception channel. TOF measurement in acoustic signals is a problem that frequently arises not only in Non Destructive Testing (NDT), but also in medical imaging, geophysics and other fields. The surface echo in a signal is usually identified as the first high amplitude peak, and is traditionally detected with standard methods as threshold crossing, peak search or matched filters. All these methods are easily affected by Signal to Noise Ratio, which might be high in some channels, for example if the ray arrives at the corresponding element at a low sensibility angle. Moreover, spurious echoes from bubbles, the emission signal tail or echoes from other parts of the component can be incorrectly labelled as surface echoes. This kind of outliers are difficult to filter by conventional means and, thus, there is a need for more robust surface estimation methods. In this work, we study the application of a deep 3D Convolutional Neural Network (CNN) to detect surface echoes in Full Matrix Capture (FMC) ultrasound data. The CNN is trained using signals acquired with a matrix array and a reference component, using a robotic arm set-up to measure the probe position and orientation relative to the component. Thus, with knowledge of the set-up geometry, a model is used to compute the theoretical surface echo TOFs, which are used to label the data for training. The model was tested with a validation set, presenting good agreement with the ground truth. The results were compared with the threshold crossing method observing a significant reduction of outliers in the TOF estimation.

High-resolution wide-swath SAR imaging with multifrequency pulse diversity mode in azimuth multichannel system

ABSTRACT High-resolution wide-swath (HRWS) imaging has emerged as a focal point in synthetic aperture radar (SAR) research. However, conventional SAR systems face challenges in achieving both high azimuth resolution and wide swath simultaneously due to the minimum antenna area constraint. In this paper, we propose a HRWS imaging method based on multifrequency pulse diversity (MFPD) and azimuth multichannel (AMC) technique, capable of resolving range and Doppler ambiguities concurrently. In MFPD mode, range ambiguous echoes can be separated by matched filters in the range frequency domain, as multifrequency pulses are transmitted by a single channel in the transmitter. To further enhance the ambiguity resolution ability of the system and address azimuth incoherence, a novel scheme applying the MFPD to AMC system is proposed, categorized into two distinct scenarios. 1) Uniform Sampling: This scenario satisfies the displaced phase centre antenna condition. Under this condition, high range resolution imaging can be achieved through spectrum splicing in range frequency domain, simultaneously resolving Doppler ambiguity. 2) Non-uniform Sampling: In this scenario, the ambiguous echoes are processed by spatial digital beamforming and spectrum splicing in two-dimensional frequency domain. Finally, the HRWS imaging can be obtained by performing the traditional SAR algorithm. Furthermore, the proposed method can synthesize a wideband signal from multifrequency signals, thereby enhancing the feasibility of super-high-resolution imaging. Simulation results demonstrate the effectiveness of the proposed method.