The performance of conventional methods for detecting magnetic anomalies has been limited by a low signal-to-noise ratio (SNR) and the presence of complex noise environments, particularly in the context of Gaussian-colored noise. In addition, the data obtained from fluxgate magnetometers is in the form of time series, with traditional approaches often neglecting time-domain features in favor of frequency-domain features. Despite significant advancements in time series models in recent years, their potential application to magnetic anomaly detection has been largely disregarded. In response to these challenges, this paper introduces a magnetic anomaly detection approach based on the Attention-Bi-directional Long Short-Term Memory (ATT-Bi) network. To address these issues, the proposed method employs a preprocessing method involving wavelet decomposition and filtering to extract low-frequency features, thereby enhancing time-domain features and concurrently improving the SNR. Thereafter, an ATT-Bi network is utilized to extract time-domain features and capture the data correlation between time sequences for the detection of magnetic anomaly signals. The performance of the ATT-Bi network is evaluated through simulation and field testing, with comparisons made against other methods. The simulation results demonstrate that, in low SNR and Gaussian-colored noise environments, the ATT-Bi network achieves the highest detection accuracy. Moreover, the field test results consistently exhibit a detection accuracy of over 95% for ATT-Bi, highlighting the superior performance of this detection method and confirming the viability of prioritizing time-domain features.

A two-stage design approach is proposed to address the sparse antenna array design for multiple-input multiple-output radar. In the first stage, the cyclic algorithm (CA) is used to establish a covariance matrix that satisfies the beam pattern approximation for a full array. In the second stage, a sparse antenna array with a beam pattern is designed to approximate the desired beam pattern. This paper focuses on the second stage. The optimization problem for the sparse antenna array design aimed at beam pattern synthesis is formulated, where the peak side lobe (PSL) is weakly constrained by the mean squared error. To solve this optimization problem, the differential evolution (DE) algorithm with multistrategy is introduced and PSL suppression is treated as an inequality constraint. However, in doing so, a new multiobjective optimization problem is created. To address this new problem, a multiobjective differential evolution algorithm based on Pareto technique is proposed. Numerical examples are provided to demonstrate the advantages of the proposed approach over state-of-the-art methods, including DE and genetic algorithm.

Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and resource-constrained remote sensing edge devices. To tackle this problem, this study proposes a lightweight remote sensing detection network suitable for edge devices and an energy-efficient CNN accelerator based on field-programmable gate arrays (FPGAs). First, a series of network weight reduction and optimization methods are proposed to reduce the size of the network and the difficulty of hardware deployment. Second, a high-energy-efficiency CNN accelerator is developed. The accelerator employs a reconfigurable and efficient convolutional processing engine to perform CNN computations, and hardware optimization was performed for the proposed network structure. The experimental results obtained with the Xilinx ZYNQ Z7020 show that the network achieved higher accuracy with a smaller size, and the CNN accelerator for the proposed network exhibited a throughput of 29.53 GOPS and power consumption of only 2.98 W while consuming only 113 DSPs. In comparison with relevant work, DSP efficiency at an identical level of energy consumption was increased by 1.1–2.5 times, confirming the superiority of the proposed solution and its potential for deployment with remote sensing edge devices.

The accuracy of X-ray pulsar-based navigation is greatly affected by the Doppler effect caused by the spacecraft orbital motion and the systematic biases introduced by the pulsar directional error, spacecraft-borne clock error, etc. In this paper, an innovative navigation method simultaneously employing the pulse phase (PP), the difference of two neighbor PPs (DPP) and the Doppler frequency (DF) of X-ray pulsars as measurements is proposed to solve this problem. With the aid of the spacecraft orbital dynamics, a single pair of PP and DF relative to the spacecraft’s state estimation error can be estimated by using the joint probability density function of the arrival photon timestamps as the likelihood function. The systematic biases involved to the PP is proved to be nearly invariant over two adjacent navigation periods and the major part of it is eliminated in the DPP; therefore, the DPP is also exploited as additional navigation measurement to weaken the impact of systematic biases on navigation accuracy. Results of photon-level simulations show that the navigation accuracy of the proposed method is remarkably better than that of the method only using PP, the method using both PP and DF and the method using both PP and DPP for Earth orbit.

This paper addresses the issue of the reference signal being contaminated by target echoes due to factors such as wide beamwidths, beam pointing errors, or site errors in passive bistatic radar (PBR). Such contamination can lead to reference signal impurity and subsequently degrade the performance of traditional object detection methods. To overcome this challenge, a novel target detection method centered around the reference signal’s purification is presented. The proposed method involves the modeling, analysis, localization, and purification of the impure reference signal. First, a new signal model incorporating the impure reference signal is established, followed by a comprehensive analysis of its impact on target detection. Then, leveraging the valuable information hidden in the impure reference signal, target echoes within the impure reference signal are localized and reconstructed. Finally, the reconstructed target echoes are subtracted from the original impure reference signal to purify the reference signal. Based on the purified reference signal, traditional target detection is performed to detect the expected targets. The core advantage of this method lies in its efficient reconstruction and removal of the impure terms within the reference signal. The effectiveness of the proposed method is demonstrated through a series of simulation results.

The direct position determination (DPD) algorithm in wireless sensor array networks(WSANs) has high localization accuracy under low signal-to-noise ratios (SNRs). However, with the increasing of arrays, the DPD algorithm faces challenges in tracking of mobile sources in time-varying environments due to the growing communication pressure and computational complexity. In this study, a novel low-complexity DPD algorithm is considered in WSANs. First, an adaptive-filtering-based DPD model is proposed to reduce computational complexity, and the position of the target is estimated using the gradient descent method. Second, the proposed DPD method is extended to the distributed framework based on the diffusion strategy to distribute the communication pressure and computational load across the WSANs. As indicated by the simulation results, the proposed model outperforms the existing model exploiting. Furthermore, a negligible difference exists in localization performance and tracking performance between the proposed DPD method based on the diffusion strategy and the corresponding DPD method.