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This paper proposes a robust vector tracking loop structure based on potential bias analysis. The influence of four kinds of biases on the existing two implementations of Vector Tracking Loops (VTLs) is illustrated by theoretical analysis and numerical simulations, and the following findings are obtained. Firstly, the initial user state bias leads to steady navigation solution bias in the relative VTL, while new measurements can eliminate it in the absolute VTL. Secondly, the initial code phase bias is transferred to the following navigation solutions in the relative VTL, while new measurements can eliminate it in the absolute VTL. Thirdly, the user state bias induced by erroneous navigation solution of VTLs can be eliminated by both of the two VTLs. Fourthly, the multipath/NLOS likely affects the two VTLs, and the induced tracking bias in the duration of the multipath/NLOS would decrease the performance of VTLs. Based on the above analysis, a robust VTL structure is proposed, where the absolute VTL is selected for its robustness to the two kinds of initialization biases; meanwhile, the instant bias detection and correction method is used to improve the performance of VTLs in the duration of the multipath/NLOS. Numerical simulations and experimental results verify the effectiveness of the proposed robust VTL structure.

Ground-penetrating radar (GPR) is widely used for detecting airport runway subsurface defects. The trailing interference in the GPR data disguises subsurface defect responses, which seriously affects the accuracy of subsurface defect detection. To tackle the challenge of subsurface defects detection under trailing interference in real scenarios, a new multimodal fusion network referred to as $\mathrm {M^{2}}$ FNet is proposed. Based on the premise that the trailing signal is highly similar across all adjacent A-scans, the model employs a transformer encoder to extract global features of the signal with long-distance correlation. In contrast, the subsurface defects only show echo characteristics in a few adjacent B-scans. The phase of the trailing and the target signal is opposite, which is easy to discover from the Top-scan view. Thus, a hybrid convolutional neural network structure is used to extract local features from GPR images of different views. This dual network structure extremely enhances the representation learning of GPR data. In order to investigate various subsurface defects and trailing interference collected by multiple GPR systems under different conditions, the first large-scale hybrid dataset called ASD-GPR is created. Transfer learning is employed to enhance the model’s ability to detect rare defects by fine-tuning it for real-world situations, differing from synthetic training data scenarios. The results of the experiments reveal that $\mathrm {M^{2}}$ FNet outperforms state-of-the-art object detection methods in various real-world scenarios, demonstrating superior performance in detecting subsurface defects.

Infrared (IR) image small target detection and identification is a massive challenge with vast applications in essential places such as military industries and airports. Homoplastically, the unclear outline of the target in IR images, the complex image background, and the tiny percentage of targets in IR images make it challenging to accurately acquire the targets’ features and other characteristics, all of which make the research of target detection and identification in IR images significantly challenging. In this article, we first contribute an open dataset of single-frame IR small target dataset dubbed CAUC-SIRST. After that, the similarity object enhancement (SOE) module is obtained based on the input feature maps by calculating the Wasserstein distance between the local target and the local background. Then a heterogeneous parallel backbone network structure is constructed, and the feature maps obtained from three different backbone network channels are fused and stitched together. As above, the general convolution channel adaptively extracts feature information; the simple attention module (SimAM) channel increases the attention of potential targets, and the SOE channel increases the weight of targets. Finally, fusing the feature maps of the three different channels makes it possible to increase the weight of potential targets while retaining the original basic information, and this method is named heterogeneous parallel networks with SOE (HPN-SOE). Embedding the algorithm into the camera can form a sensor for IR target detection and recognition. Extensive experimental results demonstrate our outstanding performance, which outperforms other existing methods by achieving a detection accuracy of 85.7% and a detection speed of 31.2 frames/s.

Abstract: Space-time adaptive processing (STAP) is an effective method for detecting moving target in airborne/spaceborne radar. However, the detection of fast air moving targets from high speed platform is challenged by the range walk of both the clutter and moving targets. It is well known that keystone formatting can be used to compensate for the range walk of multiple moving targets simultaneously without using the knowledge of the motion parameters. However, in the presence of serious Doppler ambiguity of fast air moving targets, distribution of the clutter will be affected by the keystone formatting matched to the ambiguity number of targets, and as a result the STAP performance will degrade. In this paper, a novel STAP method is proposed for the detection of fast air moving targets from high speed platform, which can deal with the range walk of both clutter and targets. Effectiveness of the new method is verified via simulation examples.

Abstract: In this paper, a novel adaptive interference suppression algorithm is proposed for high dynamic GPS (global positioning system) receiver. The new method incorporates derivative constraints on the interference DOA (direction of arrival) into power minimization approach, which could broaden the width of nulls in the interferences directions and adapt to the rapid change of the interference directions. Compared with the traditional wide nulling algorithm based on the derivative constraints, the new method does not have to know or estimate the DOAs of the desired signal and the interference. The new method is firstly discussed in the context of spatial processing, and then extended to space-time adaptive processing to mitigate wideband diffusive multipath interference. Effectiveness of the new method is verified via simulation examples.

In sonar and many other applications, time-delay estimation is an important problem. When bandpass probe signals are used, the correlation function between the received and the known transmitted signals oscillates near the carrier frequency of the transmitted signal. In this case, many existing time-delay estimation algorithms perform poorly due to converging to local optimum points. In this paper, two efficient algorithms, Hybrid-WRELAX and EXIP-WRELAX, are proposed to deal with the above problem. They are relaxation-based global minimizers of a highly oscillatory nonlinear least-squares cost function. Both algorithms are shown to achieve the Cramer-Rao bound and require only a sequence of weighted Fourier transforms.

A computationally efficient algorithm, referred to as DS-WRELAX, is proposed for the delay and Doppler scale estimation of multiple moving targets. DS-WRELAX can approach the corresponding Cramer-Rao bounds and exhibits much better convergence properties than existing algorithms.

It is demonstrated through simulations that a combination of the vector auto-regressive (VAR) filtering technique and a maximum likelihood (ML) parameter estimation method is an effective means for carrying out clutter suppression and parameter estimation, being more robust against system mismatches than conventional displaced-phase-centre-antenna (DPCA) processing.