Iet Radar Sonar and Navigation最新文献

Mulitstatic SONAR networks (MSNs) have the potential to improve the imaging quality of underwater areas due to the increased degree of freedom. The additional signal processing required for such a network of distributed SONAR nodes compared to monostatic SONAR systems is presented. An equalisation technique for bistatic processing of coherent and incoherent setups as well as elliptical linear interpolation techniques are described to provide adequate imaging. Methods to merge the gathered data before detection and tracking algorithms are listed. A real-time modular SONAR imaging system is equipped with the presented algorithms. Simulations as well as measurements in a real harbour environment are performed, and the results show the capabilities as well as the advantages and drawbacks of such MSNs in contrast to conventional SONAR systems.

Interrupted sampling repeater jamming (ISRJ), as a typical intra-pulse coherent interference, seriously endangers the performance of radar. To effectively suppress ISRJ, the paper proposes an ISRJ suppression algorithm combines grey entropy with multidimensional filtering. Firstly, the grey entropy algorithm from image segmentation is introduced to extract interference-free segments enhancing the accuracy of intrapulse interference-free segments extraction. Secondly, a fractional-order domain filter is constructed using the interference-free segments to conduct the primary suppression of interference in the fractional-order domain. Then, a time-frequency filter is constructed based on dechirped interference-free segments to suppress the interference again in the frequency domain. Finally, leveraging the linear relationship between frequency and distance of target in dechirped radar received signals, and the pulse compression results after interference suppression are obtained. Simulation experiments validate the effectiveness and robustness of the proposed algorithm. Compared with the same type of algorithms, the proposed algorithm demonstrates improved interference suppression performance under low signal-to-noise ratio (SNR) or high jamming-to-signal ratio (JSR) conditions.

In the context of electronic warfare, cross-eye jamming is a powerful technique designed to induce angular error in radar systems. This study explores the potential of phase-conjugating (PC) arrays as novel and effective alternative to the traditional Van Atta (VA) arrays. An analytical framework for PC arrays is developed, extending the previous theoretical foundation of cross-eye jamming techniques, and is validated through simulations. A tolerance analysis of the system is provided by deriving analytical expressions for the acceptable phase and amplitude mismatches as a function of the angle deception capability measured by the angle factor. The impact of the skin return is examined with a refined metric based on the 95th percentile, which allows to assess the minimum JSR requirement for a reliable countermeasure. This shows again a superior performance for the PC implementation compared to the VA.

In this paper, we propose a three-element nonuniform linear array (NULA) design strategy for an OFDM-based radar system installed on a moving platform. The need to suppress Doppler-spread clutter induced by platform motion requires the use of space–time cancellation approaches, such as displaced phase centre antenna (DPCA) or space–time adaptive processing (STAP). In particular, the recently introduced thresholded-DPCA reciprocal filter (T-DPCA-RF) was demonstrated to be especially effective in detecting moving targets within a stationary scene using OFDM waveforms on transmit. This is mostly due to the T-DPCA-RF capability to process the received signals in batches of arbitrary length, independent of the OFDM framing structure. The three-element receiving NULA design strategy for multi-channel DPCA processing proposed in this work builds upon this feature of the T-DPCA-RF. Although previous research studies have demonstrated the advantages of NULA in multi-channel DPCA, the antenna positions were chosen based on a heuristic criterion. The NULA design strategy proposed in this paper provides guidance for antenna positioning by enforcing a constraint on the DPCA filter response that prevents blind velocity effects within a specified target velocity range of interest. The effectiveness of the multi-channel DPCA detector using the NULA designed with our strategy is validated through experimental data, extending the scope of previous studies in this area.

This paper focuses on the problem of mainlobe interference suppression in radar networks tracking multi-target scenarios with multiple jammers. A beam allocation method is proposed based on stepwise suppression of mainlobe and sidelobe interference. Although a single radar can effectively suppress sidelobe interference, mainlobe interference necessitates collaborative countermeasures among multiple radar nodes, indicating that the beam allocation method significantly impacts interference suppression performance. We derive the mathematical relationship between target tracking accuracy and beam allocation strategy to address this. Subsequently, we establish a beam allocation model, with the beam allocation matrix as the optimisation variable and target tracking accuracy as the optimisation objective. One intelligent algorithm is employed to solve this model. Simulation results demonstrate that the proposed method optimises the allocation of anti-jamming resources in the radar network, leading to efficient interference suppression and stable target tracking.

Range migration (RM), Doppler frequency migration (DFM) and Doppler ambiguity, arising from high-speed manoeuvre of target, render moving target detection particularly difficult. In this paper, an efficient method based on reversal decoupling transform (RDT), scaled Fourier transform (SCFT) and keystone transform (KT), that is, RDT-SCFT-KT, is proposed for effective long-time coherent integration (LTCI) of an accelerated manoeuvring target with Doppler ambiguity. More specifically, an efficient RDT is performed to tackle range curvature and some other unnecessary terms. After that, the scaled inverse Fourier transform (SCIFT) and SCFT are successively introduced to handle the linear RM and DFM, thereby realising two-dimensional (2-D) energy integration. Besides, velocity and acceleration with respect to target could be acquired by the aid of peak detection. With the compensation associated with the obtained parameters, a linear and nonsearching approach involving the KT is developed to efficiently attain the final energy focusing and the remaining target parameters (i.e., range and unambiguous velocity). Given the above, the proposed RDT-SCFT-KT method could achieve LTCI for accelerated manoeuvring target and exhibits low computational complexity without requiring parameter searching process. Meanwhile, it remains effective for Doppler ambiguity (including velocity ambiguity and Doppler spectrum ambiguity) and offers complete motion parameters. Besides, the final energy integration is a linear transform process, which facilitates multi-target processing. The efficacy of the RDT-SCFT-KT integration approach is substantiated through numerical experiments.

In advanced defence and security systems, multi-sensor fusion is widely used to improve the overall observation capability, and heterogeneous sensors are a typical deployment in multi-sensor systems. Track-to-track association (T2TA) of heterogeneous sensors is the precondition and foundation of heterogeneous sensor track fusion. However, problems such as ubiquitous systematic and random errors, inconsistent update periods and features caused by two heterogeneous sensors bring significant challenges to T2TA and existing methods have not solved the above problems adequately. To address these problems, we propose conditional idempotent association generation for heterogeneous track-to-track association (CIAG). In CIAG, a track state mapping module (TSMM) is constructed to unify asynchronous and heterogeneous tracks from heterogeneous sensors. The TSMM can also mitigate the effects of systematic and random errors. An idempotent association generation module (IAGM) is constructed to model tracks and association matrices jointly, and generate association matrices directly and precisely. Moreover, CIAG realises an end-to-end generation from the track tensor to the association matrix that can avoid long time consumption caused by traversal calculations of tracks. Comprehensive experiments demonstrate that CIAG can achieve the best association performance and has better association efficiency.

The rotating corner reflector is widely used in passive jamming methods because of its micro-Doppler modulation effect on the echo signal of synthetic aperture radar. Nevertheless, the traditional methods based on this have problems of single jamming effect and limited jamming range, which are gradually unable to adapt to the increasingly complex electromagnetic countermeasure environment. Therefore, it is of great significance to optimise and improve this to achieve more abundant jamming effects. With the development of material technology, the electromagnetic meta-materials can realise the intra-pulse modulation of radar signals. Thus, a novel jamming method based on the uniform acceleration or deceleration rotating corner reflector and the periodic pulse modulation electromagnetic meta-material is proposed in this paper. Through changing the modulation parameters of proposed jamming model, four different types of jamming effects can be achieved and regulated flexibly. The imaging characteristics of proposed jamming model and the regulation effects of jamming parameters are analysed, respectively. Then, the simulation experiments are performed using the original echo data of airborne SAR system and the simulation results verify the correctness of theoretical analysis. Finally, the prototype design of proposed jamming model and the practical feasibility analysis are given, respectively, which can provide support for the subsequent engineering implementation.

In a distributed sensor fusion architecture, using standard Kalman filter (naive fusion) can lead to degraded results as track correlations are ignored and conservative fusion strategies are employed as a sub-optimal alternative to the problem. Since, Gaussian mixtures provide a flexible means of modelling any density, therefore fusion strategies suitable for use with Gaussian mixtures are needed. While the generalised covariance intersection (CI) provides a means to fuse Gaussian mixtures, the procedure is cumbersome and requires evaluating a non-integer power of the mixture density. In this paper, the authors develop a pooling-based fusion strategy using the harmonic mean density (HMD) interpolation of local densities and show that the proposed method can handle both Gaussian and mixture densities without much changes to the framework. Mathematical properties of the proposed fusion strategy are studied and simulated on two-dimensional (2D) and three-dimensional (3D) manoeuvering target tracking scenarios. The simulations suggest that the proposed HMD fusion performs better than other conservative strategies in terms of root-mean-squared error while being consistent.

Automatic object detection onboard drones is essential for facilitating autonomous operations. The advent of deep learning techniques has significantly enhanced the efficacy of object detection and recognition systems. However, the implementation of deep networks in real-world operational settings for autonomous decision-making presents several challenges. A primary concern is the lack of transparency in deep learning algorithms, which renders their behaviour unreliable to both practitioners and the general public. Additionally, deep networks often require substantial computational resources, which may not be feasible for many compact portable platforms. This paper aims to address these challenges and promote the integration of deep object detectors in drone applications. We present a novel interpretative framework, DetDSHAP, designed to elucidate the predictions generated by the YOLOv5 detector. Furthermore, we propose utilising the contribution scores derived from our explanatory model as an innovative pruning technique for the YOLOv5 network, thereby achieving enhanced performance while minimising computational demands. Lastly, we provide performance evaluations of our approach demonstrating its efficiency across various datasets, including real data collected from drone-mounted cameras and established public benchmark datasets.