Iet Radar Sonar and Navigation最新文献

The authors consider a quantum radar which operates on the quantum illumination principle. The authors’ attention is focused on its function of target detection in a noisy environment. The role of the optical parametric amplifier (OPA) in detection is first examined by the authors, and a dual-OPA design for more flexible combination of optimised gains is proposed, resulting in a detector substantially improved in its performance from the normally used 1-OPA design. Then, the use of the entanglement information in the covariance matrix (CM) between the returned signal and idler beams for detection is considered, and a technique to extract such information is proposed. By employing some statistical relationships between positive definite matrices, the authors come up with a new target detection method. Numerical experiments confirm the superior detection performance of the CM detectors compared to that of the OPA detectors.

The persuasive idea behind noise radar technology (NRT) states that the usage of random and non-periodic radar signals, in principle, eliminates all kinds of ambiguities that for many other radars are a driving design factor. However, practical aspects of NRT need to carefully evaluate the actual degree of randomness in their transmission, and the computational load the radar signal processing requires.

The performance of noise radars has evolved in accordance with the advance of signal processing hardware and algorithms. From the first implementations of noise radars which used analogue delay lines, for the observation of a limited range swath, towards modern and complex Field Programmable Gate Array-based real-time implementations, it took several decades of intense research. During the evolution of NRT, other advantageous characteristics of noise radars have been identified, particularly in the aspect of electronic warfare (EW). The latter, being seen as the counterpart of radar sensing, may have several goals such as the interception and location of radar emitters, the identification of the radar and or its platform, an estimation of the task of the radar, an assessment of the threat that is represented by the radar's task in a particular situation, and the engagement of counter-actions either by jamming, spoofing or a hard-kill. The modern and more general term EMSO (electromagnetic spectrum operations) draws an even wider picture around EW and includes cyber aspects as well. The latter, thus, introduces an interesting aspect for use-cases in which NRT is considered for joint communication and radar sensing applications.

The dear reader may be glad to see that this special issue on the advancements and future trends in noise radar contains contributions on anti-intercept features, security aspects, modern signal processing technology, such as programmable digital circuits and artificial intelligence.

The article ‘Implementation of a Coherent Real-Time Noise Radar System’ by Martin Ankel, Mats Tholén, Thomas Bryllert, Lars Ullander and Per Delsing focuses on the implementation aspects of a basic range-Doppler processing method. That algorithm is enhanced by a motion compensation approach that aims to overcome the cell migration in the range-Doppler plane caused by the high time-bandwith product of the selected parameters. This paper presents the implementation of a demonstrator system on a very detailed level. It not only reasons the authors' selection of particular Simulink® and Xilinx IP-cores but also discusses the requirements, limitations and effects that the selected RFSoC Hardware and its peripherals have on the implementation results. Finally, the paper reports the set up and results of field trials that illustrate the limitations of the demonstrator in accordance with what was expected from the theoretical assessment of the power budget, the waveform particularities and the hardware limitations. Interestin

Radar systems are a topic of great interest, especially due to their extensive range of applications and ability to operate in all weather conditions. Modern radars have high requirements such as its resolution, accuracy and robustness, depending on the application. Noise Radar Technology (NRT) has the upper hand when compared to conventional radar technology in several characteristics. Its robustness to jamming, low Mutual Interference and low probability of intercept are good examples of these advantages. However, its signal processing is more complex than that associated to a conventional radar. Artificial Intelligence (AI)-based signal processing is getting increasing attention from the research community. However, there is yet not much research on these methods for noise radar signal processing. The aim of the authors is to provide general information regarding the research performed on radar systems using AI and draw conclusions about the future of AI in noise radar. The authors introduce the use of AI-based algorithms for NRT and provide results for its use.

The application of the hybrid extended Kalman filter (HEKF), hybrid unscented Kalman filter (HUKF), hybrid particle filter (HPF), and hybrid extended Kalman particle filter (HEKPF) is discussed for hybrid non-linear filter problems, when prediction equations are continuous-time and the update equations are discrete-time, and also the discrete extended Kalman filter (DEKF), discrete unscented Kalman filter (DUKF), discrete particle filter (DPF), and discrete extended Kalman particle filter (DEKPF) for discrete-time non-linear filter problems, when prediction equations and update equations are discrete-time. In order to assess the performance of the filters, the authors consider the non-linear dynamics for a re-entry vehicle. The filters are used in two hybrid and discrete states to estimate the position, velocity, and drag parameter associated with the re-entry vehicle. Theoretical topics concerning estimating the drag parameter of a vehicle in re-entry phase have been dealt with. Drag parameter estimation is carried out using a combination of hybrid filters and discrete filters as an effective estimator and fixed value, forgetting factor, and Robbins-Monro stochastic approximation methods as the noise covariance matrix adjuster of the parameter.

The authors propose a novel dual coprime frequency diverse array (FDA) multiple input multiple output (DCFDA-MIMO) radar network design, empowered by cognitive capabilities, aimed at target discrimination and mitigation of interference present in the standalone radar systems. That is, the proposed DCFDA-MIMO design capitalises on the complementary advantages of FDAs for target discrimination and coprime arrays for enhanced resolution, resulting in superior performance. Additionally, the proposed DCFDA-MIMO network employs a 2D multiple signal classification algorithm to achieve high-resolution target localisation. By incorporating cognitive techniques based on the action-perception cycle, the proposed approach demonstrates notable improvements in multiple target detection and tracking accuracy with fewer number of antenna elements as compared to existing techniques. Furthermore, it enhances individual radar beamforming performance for interference suppression and true target detection without prior information.

Conventional active SONAR systems often use beamformers and matched filters separately to extract bearing and range information from the received signal and offer a straightforward way of creating a two-dimensional map of the environment. In SONAR systems the minimum-variance-distortionless-response beamformer (MVDR beamformer) is a commonly used type of beamformer, which will reconstruct the receive signal from a certain direction optimally. In terms of detecting the transmit signal, the most used method is the conventional matched filter. Both algorithms are simple to implement and perform well under various noise scenarios. The proposed method combines the beamformer and matched filter by introducing an extended channel model that allows the derivation of a multichannel Wiener filter to solve for the unknown reflection coefficients of the complete two-dimensional environment. This results in adaptively calculated filter weights that will drastically improve the performance compared to a separate MVDR beamformer and matched filter. In addition, a parameter is introduced with which one can arbitrarily adjust the focus between angular and temporal resolution depending on the application. After the derivation, the performance is demonstrated with simulations and measurements.

A high precision estimation algorithm for ground moving targets in multi-channel wide-area surveillance ground moving target indication systems is proposed based on maximum likelihood method. The main concept of this novel algorithm is to estimate the azimuth angle of the detected targets using maximum likelihood method with the space steering vector formed by the estimated interferometric phase extracted from the mainlobe clutter region of the real data. Through this novel algorithm, the effect of channel errors among the multi-channels can be well reduced. Simulation experiments demonstrate the effectiveness of the proposed algorithm.

In a distributed multi-station system, the observations received by local radar nodes for a single target will have a large signal-to-noise ratio (SNR) bias due to inconsistent radar cross-sections from distinct angles, different distances from the target, various local interference such as harsh weather, and dissimilar background noise. Integrating heterogeneous information in dynamic and uncertain environments can be challenging for the fusion centre. Moreover, the particles in the basic particle filter (PF) may degrade after many iterations, making it difficult to achieve accurate target state estimation in the local tracking process. To address these issues, the authors propose a novel method named DS-UPF based on the Dempster–Shafer (DS) theory and the unscented particle filter (UPF). By updating the important density function, the UPF efficiently suppresses particle degradation. The weighted Basic Probability Assignments (BPAs) are proposed and integrated under the new synthesis formula. The weight-modified DS method restrains the impact of significant local estimation errors on weighted BPAs fusion result, improving robustness without local interference prior knowledge. The experimental results demonstrate that the DS-UPF outperforms the unscented Kalman filter, PF, and UPF in tracking tasks under various local interference. This indicates that the proposed algorithm can improve estimation precision in dynamic and uncertain environments.

To achieve broad observation areas and high resolution, synthetic aperture radars adopt wavelet-transformed observation areas that contain information on position and velocity. The observation area adopts pseudosignals with scattering information about the position and velocity in three dimensions. The wavelet transform (WT) is applied to micromoving targets to obtain a pseudosignal, and each micromoving target is defined by an Identifier (ID) of parameter scale a and parameter shift b. Because the interval of each micromoving target is minimised by the WT, the array of all micromoving targets becomes a continuum that can be represented by straight or curved lines. Every micromoving target can be identified by an ID as long as the micromoving targets do not overlap. Every moving signal in a three-dimensional space can be identified by the abovementioned ID. The results demonstrated that the observation area can be broadened by employing the minimum number of units with micromoving targets. In addition, micromoving targets in the observation area can be obtained at a high resolution (3 cm), and the position of the ID does not change owing to noise. The developments presented can contribute to the fast detection of earthquakes.

The spaceborne Inverse Synthetic Aperture Radar (ISAR) has garnered significant attention due to its extensive observation range and robust anti-attack capabilities. Consequently, the ISAR imaging research of air targets based on a spaceborne platform has crucial application value. However, unlike the traditional ground-based radar system, the spaceborne platform moves along its own orbit while observing the air target, and the received signal energy is weakened due to the extended observation distance. Therefore, it is important to optimise the existing ISAR imaging geometry models and motion compensation algorithms. The authors first construct a geometric model of spaceborne ISAR imaging for air targets. Aiming at the problem of low signal-to-noise ratio (SNR), a novel translational motion compensation algorithm based on motion parameter estimation is proposed. The algorithm compensates for both distance migration and Doppler migration caused by the first-order and second-order motion components of relative motion, respectively. Finally, simulation and semi-physical simulation results validate the effectiveness and superiority of the proposed algorithm under different SNR and motion conditions.