Iet Radar Sonar and Navigation最新文献

In tracking scenarios involving groups with dense targets, achieving effective data association is challenging due to mutual occlusion and interference among targets. The complexity of the tracking problem is further exacerbated in low-observable environments by the increase in false alarm rates. The track-before-detect (TBD) is an advanced technology for detecting and tracking low-observable targets, effectively mitigating data association problems by integrating multi-frame echo data. However, the existing multi-target TBD algorithms typically assume that the targets are spatially separated and are not suitable for scenarios involving group targets. A group target maximum-likelihood probabilistic data association (GT-ML-PDA) algorithm, based on the concept of TBD, is proposed to track group targets effectively in low-observable environments. The proposed algorithm divides group target tracking into two stages: group centre trajectory estimation and individual target trajectory estimation. To enhance the performance of the proposed algorithm, two strategies are suggested: modifying the equivalent measurements and extracting independent measurement sets for individual targets. Simulation results demonstrate that the proposed algorithm is capable of effectively tracking numerous individual targets within a group, even in the presence of heavy clutter.

Over the past decade, there have been great advancements in radio frequency sensor technology for human–computer interaction applications, such as gesture recognition, and human activity recognition more broadly. While there is a significant amount of study on these topics, in most cases, experimental data are acquired in controlled settings by directing participants what motion to articulate. However, especially for communicative motions, such as sign language, such directed data sets do not accurately capture natural, in situ articulations. This results in a difference in the distribution of directed American Sign Language (ASL) versus natural ASL, which severely degrades natural sign language recognition in real-world scenarios. To overcome these challenges and acquire more representative data for training deep models, the authors develop an interactive gaming environment, ChessSIGN, which records video and radar data of participants as they play the game without any external direction. The authors investigate various ways of generating synthetic samples from directed ASL data, but show that ultimately such data does not offer much improvement over just initialising using imagery from ImageNet. In contrast, an interactive learning paradigm is proposed by the authors in which model training is shown to improve as more and more natural ASL samples are acquired and augmented via synthetic samples generated from a physics-aware generative adversarial network. The authors show that the proposed approach enables the recognition of natural ASL in a real-world setting, achieving an accuracy of 69% for 29 ASL signs—a 60% improvement over conventional training with directed ASL data.

Passive source detection is a challenging problem in the shadow zone, where the sound is contributed primarily by bottom-bounce rays. The conventional beamforming detector (CBFD), which utilises the sound energy from a single direction, suffers potential significant performance degradation in the multipath-signal scenario. The matched field detector (MFD) offers optimal performance by exploiting full-wave field characteristics but is limited due to its reliance on prior ocean environmental knowledge. The authors demonstrate that the incident sound on a near-surface vertical line array in the shadow zone can be approximated as a coherent sum of two plane waves that share a symmetric arrival angle about the horizontal. This leads to the signal subspace depending only on the arrival angle, which the authors call the bottom-bounce ray angle subspace (BRAS). With generalised likelihood ratio test theory, the authors further derive the BRAS detector (BRASD). It can utilise the full signal energy under the premise of weak environmental knowledge requirements and is generally superior to the CBFD. Simulation results in a typical deep-water channel under different source-position and array configuration conditions demonstrate the effectiveness of the BRASD and suggest that it can even offer a performance comparable to that of the MFD under specific conditions.

Traditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse-to-pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value.

Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi-realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre-Decluttering Dual-Stage UNet (D²UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D²UNet, the authors also design a multi-stage loss function to achieve progressive training. Extensive experimental results demonstrate that D²UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal-to-jamming ratio, 0.9952 structure similarity index measure) on the semi-realistic dataset. Its practical effect is further verified on measured data.

Precise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low-complexity and environment-adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed-form expression of the detection probability is derived. Simulations and on-board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal-to-noise ratio ≥3 dB and false alarm probability = 10⁻⁸. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals.

Robustness against Electronic Warfare/Electronic Defence attacks represents an important advantage of Noise Radar Technology (NRT). An evaluation of the related Low Probability of Detection (LPD) and of Intercept (LPI) is presented for Continuous Emission Noise Radar (CE-NR) waveforms with different operational parameters, that is, “tailored”, and with various “degrees of randomness”. In this frame, three different noise radar waveforms, a phase Noise (APCN) and two “tailored” noise waveforms (FMeth and COSPAR), are compared by time–frequency analysis. Using a correlator (i.e. a two antennas) receiver, assuming a complete knowledge of the band (B) and duration (T) of the coherent emission of these waveforms, it will be shown that the LPD features of a CE-NR do not significantly differ from those of any CE radar transmitting deterministic waveforms. However, in real operations, B and T are unknown; hence, assuming an instantaneous bandwidth estimation will show that the duration T can be estimated only for some specific “tailored” waveforms (of course, not to be operationally used). The effect of “tailoring” is analysed with prospects for future work. Finally, some limitations in the classification of these radar signals are analysed.

Suitable and effective matching area selection is crucial for gravity matching-aided navigation. In this paper, an all-field extended extremum algorithm based on an adaptive threshold (AT-AEE) is proposed for matching area selection in the Arctic Sea. The gradient data is obtained by using the convolution of gravity reference graph data and all-field extended extremum parameters. Then, the adaptive threshold method was employed to determine the optimal gradient threshold based on gravity anomaly data across various test areas. Data points with gradients exceeding the specified threshold are identified as local candidate points for matching areas. The test areas containing a certain proportion of local candidate points are designated as the suitable matching areas. Nine test areas in the Arctic Sea with different gravity change characteristics were chosen for simulation experiments to verify the performance of the proposed algorithm. Simulation experiments showed that superior navigation positioning results could be obtained in the matching areas selected by the AT-AEE algorithm. Compared to traditional algorithm, the matching areas derived from the AT-AEE algorithm performed with a better consistency in the gravity matching navigation results. In suitable matching areas with the proportion of local candidate points reaching 70%, the average positioning errors could be reduced to less than 1.5 n miles.

The authors propose an innovative solution to address challenges in terrain-referenced navigation (TRN). The suggested solution is the interactive multiple-model particle filter with a classification error minimisation strategy (IMM-CPF) based on decision theory. TRN is a technique that estimates position by comparing measured terrain altitude to the digital elevation model and critically depends on obtaining accurate altitude measurements. However, these measurements can be easily contaminated to not only from sensor errors but also from vegetation effects. The TRN measurement noise model is characterised as a multi-modal density, and it reveals an overlap between two density functions, with the mixture weight parameter varying based on surface environmental conditions. This variability can potentially degrade estimation accuracy. The proposed approach integrates truncated likelihoods into the mode estimation process to enhance mode estimation capability using a classification error minimisation strategy. The proposed strategy is based on decision theory and has been modified to be suited in the IMMPF form. The effectiveness of the proposed IMM-CPF method is verified through simulations conducted under diverse surface conditions, demonstrating significant improvements in estimation accuracy compared to conventional algorithms. Furthermore, the significance of this method is presented in terms of computational cost and robustness.