Iet Radar Sonar and Navigation最新文献

Modern radar systems often face various interference signals in complex and rapidly changing electronic environments. The task of suppressing this interference in the radar echo signal to extract vital information is challenging. A radar interference suppression method is proposed based on a generative adversarial network (GAN). This method effectively recovers the target signal from the echo signal, which contains interference and noise, by leveraging the powerful fitting ability of GAN. Specifically, this method was tested using coherent suppression interference, smart noise interference, and noise frequency modulation suppression interference. We compared the proposed GAN method with recurrent neural network, short-time Fourier transform time-varying filtering, short-time fractional Fourier transform time-varying filtering algorithms and RNN approach. The results show that the interference suppression algorithm based on GAN is superior to the other three algorithms.

The authors focus on the waveform design for Code Division Multiple Access Multiple Input Multiple Output (CDMA-MIMO) radar systems, with a specific emphasis on Compressed Sensing (CS) based target estimation. The selection of an appropriate waveform is a critical determinant in the effectiveness of estimation algorithms. Recent studies show the possibilities of optimising waveform parameters to improve the efficiency of CS based estimation. The authors introduce an optimisation framework designed to modify the phase components of code sequences used in CS-CDMA MIMO radar systems. The objective of this optimisation is to minimise the l_∞ norm of off-diagonal elements within the Gramian matrix of the underlying sensing matrix, focusing on phase modulation of the waveform. Solving this optimisation problem requires dealing with a non-convex, combinatorial and non-linear scenario. Simulated Annealing is employed as the solution technique. To assess the effectiveness of the proposed optimisation approach, the resulting optimised sequence is rigorously compared against well-established Hadamard and Gold sequences across various performance metrics. These metrics encompass correlation properties, ambiguity function behaviour, recovery percentage and recovery error. The study demonstrates that the generated poly-phase sequences outperform existing sequences, leading to significantly improved target reconstruction results in the context of CDMA-MIMO radar systems with CS-based estimation.

Beamforming is an effective way of resolving target direction and anti-jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters’ outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range-Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam.

The performance of Doppler velocity logs (DVLs) in terms of velocity estimate error is directly linked to the geometry of the beam and the pulse transmitted. Beyond a specific transmitted bandwidth, the phase-shift beamformer can introduce significant errors in velocity estimation. To delineate the operating mechanism of phase-shift errors within a phased array of acoustic DVLs, the correlation between bottom echo and velocity distribution, in conjunction with the power-weighted function, was initially examined predicated on spectral estimation theory. Subsequently, numerical and analytical models of the Gaussian-shaped Doppler spectrum were formulated. The models are employed to evaluate the velocity estimation inaccuracies attributed to phase shifts in extant DVLs, and the comparative results with field experiments corroborate the model's efficacy in forecasting errors. The theoretical findings evaluate the performance limitations of the current phased array transducer design and provide insights for developing new designs. Pool experimental results show that this design effectively reduces the velocity estimation error caused by phase shift under static conditions and in the presence of Doppler frequencies to a level of almost complete elimination of the error compared to conventional configurations.

Space situational awareness systems primarily focus on detecting and tracking space objects, providing crucial positional data. However, understanding the complex space domain requires characterising satellites, often involving estimation of bus and solar panel sizes. While inverse synthetic aperture radar allows satellite visualisation, developing deep learning models for substructure segmentation in inverse synthetic aperture radar images is challenging due to the high costs and hardware requirements. The authors present a framework addressing the scarcity of inverse synthetic aperture radar data through synthetic training data. The authors approach utilises a few-shot domain adaptation technique, leveraging thousands of rapidly simulated low-fidelity inverse synthetic aperture radar images and a small set of inverse synthetic aperture radar images from the target domain. The authors validate their framework by simulating a real-case scenario, fine-tuning a deep learning-based segmentation model using four inverse synthetic aperture radar images generated through the backprojection algorithm from simulated raw radar data (simulated at the analogue-to-digital converter level) as the target domain. The authors results demonstrate the effectiveness of the proposed framework, significantly improving inverse synthetic aperture radar image segmentation across diverse domains. This enhancement enables accurate characterisation of satellite bus and solar panel sizes as well as their orientation, even when the images are sourced from different domains.

The increasing number of space objects (SO), debris, and constellation of satellites in Low Earth Orbit poses a significant threat to the sustainability and safety of space operations, which must be carefully and efficiently addressed to avoid mutual collisions. The space situational awareness is currently addressed by an ensemble of radar and radio-telescopes that detect and track SO. However, a large part of space debris is composed of very small and tiny metallic objects, very difficult to detect. The authors demonstrate the benefits of using deep learning (DL) architectures for small space object detection by radar observations. TIRA radio telescope has been simulated to generate range-Doppler maps, then used as inputs for object detection exploiting You-Only-Look-Once (YOLO) frameworks. The results demonstrate that the object detection by using YOLO algorithms outperform conventional target detection approaches, thus indicating the potential benefits of using DL techniques for space surveillance applications.

Model-based acoustic localisation estimates the locations of underwater objects by comparing sensor measurements with model predictions. To obtain high quality predictions, propagation models need to be run for a large set of environmental parameters. However, real-time Model-based acoustic localisation estimations using onboard computational resources are often limited. To address this, the authors propose a Quantum annealing (QA) algorithm for enhancing underwater acoustic localisation. A restricted Boltzmann machine (RBM) is trained to predict the probability distribution of underwater targets. Advantage of this approach is that part of the computation is moved to offline-training. Moreover, the probability distribution can potentially be sampled efficiently using a quantum annealer possibly enabling real-time accurate target estimations being made onboard.The RBM is applied to a simplified multi-sensor horizontal localisation problem where a constant and linear acoustic propagation is assumed. Using simulated annealing the authors show that the RBM is able to learn probability distributions that resemble target locations. Preliminary results show that training and sampling the RBM can be done using QA hardware by D-Wave Systems.However, there remains room for improvement especially in ranging predictions. Further research into possible benefits of QA RBMs is needed to provide theoretical and practical results of a speed-up.

Forward-looking radar imaging is a top priority due to a variety of applications. An airborne forward-looking radar imaging approach via a modified propagator method (MPM) based on the planar phased array is proposed. It can obtain a better focusing effect without range migration correction. Through the MPM algorithm, the azimuth resolution can be enhanced greatly. Compared with the conventional synthetic aperture radar imaging algorithm, the left-right ambiguity process can be avoided by generating two-dimensional spatial spectra. Furthermore, the imaging results of different numbers of elements are given to provide an assessment of the imaging performance. Additionally, the authors proposed to apply the scanning planar phased array configuration to airborne forward-looking two-dimensional imaging for the first time. The validity of the method is proven by experiments. The image is more focused. Furthermore, the results of the experiments verify that the proposed method is fit for high-speed situation and aircraft height variation condition.

With the assumption of densely distributed vegetation in PolInSAR processing, the height parameters can be inversed by the conventional random volume over ground (RVoG) method. During the procedure of RVoG, the samples used to estimate the PolInSAR coherence are selected directly from the neighbouring areas. However, for sparsely distributed vegetation, the scattering statistics are different from those of densely distributed vegetation. Therefore, the inversion performance will be deteriorated if the samples are selected directly in the neighbouring areas. A new phase-based method is proposed to select samples, whose positions represent the volume scattering pixels, for sparsely distributed vegetation height inversion. By analysing the scattering characteristics of sparsely distributed vegetation, the processing data vector is formulated based on the amplitude-normalised interferograms. With the PolSAR classification, the generalised inner product (GIP) is iteratively used to select the non-local samples based on the formulated phase data vectors, which are utilised for sparsely distributed vegetation height inversion. For the selected sample sets, the vegetation distribution can be approximately regarded as “dense distribution”, and then the vegetation height parameters can be inversed by RVoG method. Compared to the height inversion performance based on different sample selection methods, the effectiveness of the proposed method is validated by the PolSARPro simulated data and the real airborne L-band PolInSAR data.