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Auditory motion perception is one crucial capability to decode and discriminate the spatiotemporal information for neuromorphic auditory systems. Doppler frequency‐shift feature and interaural time difference (ITD) are two fundamental cues of auditory information processing. In this work, the functions of azimuth detection and velocity detection, as the typical auditory motion perception, are demonstrated in a WOx‐based memristive synapse. The WOx memristor presents both the volatile mode (M1) and semi‐nonvolatile mode (M2), which are capable of implementing the high‐pass filtering and processing the spike trains with a relative timing and frequency shift. In particular, the Doppler frequency‐shift information processing for velocity detection is emulated in the WOx memristor based auditory system for the first time, which relies on a scheme of triplet spike‐timing‐dependent‐plasticity in the memristor. These results provide new opportunities for the mimicry of auditory motion perception and enable the auditory sensory system to be applied in future neuromorphic sensing. A auditory sensory system with motion perception is demonstrated by using a WOx‐based memristive synapse. Due to the coexistence of the volatile mode and semi‐nonvolatile mode in the Ar‐plasma‐treated (APT) WOx memristor, the functions of azimuth detection and velocity detection are realized via implementing the high‐pass filtering and processing the spike trains with frequency shift in both modes, respectively.

In recent years, autonomous robots have been practically used outdoors for transportation, delivery, and other applications. To ensure safety and reliability, it is necessary to measure and evaluate the accuracy of a robot’s movement from the outside. In this study, we develop a device to externally measure the coordinates and azimuth angle of a robot by combining image processing and distance measurement. In this study, an azimuth angle detection method that combines coarse-angle detection using AKAZE feature detection and small-angle detection using optical flow is proposed. The experimental results show that the azimuth angle can be detected with a standard deviation of σ=0.70° indoors and σ=0.99° outdoors.

The detection of weak moving targets in heterogeneous clutter backgrounds is a significant challenge in radar systems. In this paper, we propose a track-before-detect (TBD) method based on information geometry (IG) theory applied to range-azimuth measurements, which extends the IG detectors to multi-frame detection through inter-frame information integration. The approach capitalizes on the distinctive benefits of the information geometry detection framework in scenarios with strong clutter, while enhancing the integration of information across multiple frames within the TBD approach. Specifically, target and clutter trajectories in multi-frame range-azimuth measurements are modeled on the Hermitian positive definite (HPD) and power spectrum (PS) manifolds. A scoring function based on information geometry, which uses Kullback–Leibler (KL) divergence as a geometric metric, is then devised to assess these motion trajectories. Moreover, this study devises a solution framework employing dynamic programming (DP) with constraints on state transitions, culminating in an integrated merit function. This algorithm identifies target trajectories by maximizing the integrated merit function. Experimental validation using real-recorded sea clutter datasets showcases the effectiveness of the proposed algorithm, yielding a minimum 3 dB enhancement in signal-to-clutter ratio (SCR) compared to traditional approaches.

The angle of attack (AOA) is a crucial parameter for describing the flight state of a projectile and is one of the key elements in external ballistic testing. In light of the inherent difficulty and substantial cost associated with the direct detection of the AOA during the flight of guided projectiles, this study introduces an estimation method for the AOA that is predicated on a neuro-behavioural cognitive architecture (NBCA) neural network fusion model. This approach leverages data from geomagnetic sensors to calculate the geomagnetic azimuth angle related to the AOA. Subsequently, by fitting the geomagnetic azimuth angle and angle of attack-related quantities into the fusion model and training the neural network, the angle of attack is estimated. Research results indicate that the root mean square error of the angle of attack estimation is 0.0149°. This method achieves optimal estimation of the projectile’s angle of attack without the need for extensive detection equipment, providing a novel perspective for the practical engineering application of projectile angle of attack detection.

Radar target detection (RTD) is one of the most significant techniques in radar systems, which has been widely used in the field of military and civilian. Although radar signal processing has been revolutionized since the introduction of deep learning, applying deep learning in RTD is considered as a novel concept. In this paper, we propose a model for multitask target detection based on convolutional neural network (CNN), which works directly with radar echo data and eliminates the need for time-consuming radar signal processing. The proposed detection method exploits time and frequency information simultaneously; therefore, the target can be detected and located in multi-dimensional space of range, velocity, azimuth and elevation. Due to the lack of labeled radar complex data, we construct a radar echo dataset with multiple signal-to-noise ratio (SNR) for RTD. Then, the CNN-based model is evaluated on the dataset. The experimental results demonstrated that the CNN-based detector has better detection performance and measuring accuracy in range, velocity, azimuth and elevation and more robust to noise in comparison with traditional radar signal processing approaches and other state-of-the-art methods.

In geosynchronous synthetic aperture radar (GEO SAR), the azimuth FM rate drastically falls and comes close to the variation rate of the Doppler centroid, due to the higher altitude. The effect of the variation of the Doppler parameters becomes significant and affects the azimuth resolution results. A concise azimuth resolution expression is deduced from the azimuth ambiguity function which involves the azimuth variance property and third-order range equation. It is more accurate for GEO SAR than the conventional expression. Meanwhile, the phenomenon where the traditional frequency algorithm becomes invalid is found. However, the time domain algorithm is still applicable when the azimuth FM rate approaches zero. Finally, the simulation results of the point targets at five typical areas verify the analysis.

Building change detection is a critical field for monitoring artificial structures using high-resolution multitemporal images. However, relief displacement depending on the azimuth and elevation angles of the sensor causes numerous false alarms and misdetections of building changes. Therefore, this study proposes an effective object-based building change detection method that considers azimuth and elevation angles of sensors in high-resolution images. To this end, segmentation images were generated using a multiresolution technique from high-resolution images after which object-based building detection was performed. For detecting building candidates, we calculated feature information that could describe building objects, such as rectangular fit, gray-level co-occurrence matrix (GLCM) homogeneity, and area. Final building detection was then performed considering the location relationship between building objects and their shadows using the Sun’s azimuth angle. Subsequently, building change detection of final building objects was performed based on three methods considering the relationship of the building object properties between the images. First, only overlaying objects between images were considered to detect changes. Second, the size difference between objects according to the sensor’s elevation angle was considered to detect the building changes. Third, the direction between objects according to the sensor’s azimuth angle was analyzed to identify the building changes. To confirm the effectiveness of the proposed object-based building change detection performance, two building density areas were selected as study sites. Site 1 was constructed using a single sensor of KOMPSAT-3 bitemporal images, whereas Site 2 consisted of multi-sensor images of KOMPSAT-3 and unmanned aerial vehicle (UAV). The results from both sites revealed that considering additional shadow information showed more accurate building detection than using feature information only. Furthermore, the results of the three object-based change detections were compared and analyzed according to the characteristics of the study area and the sensors. Accuracy of the proposed object-based change detection results was achieved over the existing building detection methods.

Vortex electromagnetic (EM) waves can provide more target information, which is orbital angular momentum (OAM), than traditional plane waves in the field of radar detection. By emitting multiple different OAM modes of vortex EM waves to the same target, the azimuth information of the target can be obtained. The Linear Frequency Modulated (LFM) radar is often used in current OAM radar design. At present, researches on vortex EM wave imaging usually requires the emission of a large number of OAM modes to irradiate the target. Although many achievements have been made to significantly reduce this number, it is often accompanied by problems such as loss of azimuth imaging accuracy or poor real-time performance in practical applications. In response to this issue, this paper proposes a near-field target detection method based on modal baseline using vortex EM waves. By alternately emitting vortex EM waves with least OAM mode difference of 1, the method achieves range-velocity imaging while the OAM mode phase information is not blurred. The target detection task is carried out in the range-Doppler dimension, avoiding the problem of azimuth imaging. Through signal processing, our method achieves 3d point cloud detection of target velocity, range, and azimuth. Simulation results compared with traditional phase comparison azimuth measurement method proved the superiority of our method. The work and results provide suggestions to the development of vortex EM wave target detection.

Utilizing hydrophone arrays for detecting underwater acoustic communication (UWAC) signals leverages spatial information to enhance detection efficiency and expand the perceptual range. This study redefines the task of UWAC signal detection as an object detection problem within the frequency–azimuth (FRAZ) spectrum. Employing Faster R-CNN as a signal detector, the proposed method facilitates the joint prediction of UWAC signals, including estimates of the number of sources, modulation type, frequency band, and direction of arrival (DOA). The proposed method extracts precise frequency and DOA features of the signals without requiring prior knowledge of the number of signals or frequency bands. Instead, it extracts these features jointly during training and applies them to perform joint predictions during testing. Numerical studies demonstrate that the proposed method consistently outperforms existing techniques across all signal-to-noise ratios (SNRs), particularly excelling in low SNRs. It achieves a detection F1 score of 0.96 at an SNR of −15 dB. We further verified its performance under varying modulation types, numbers of sources, grating lobe interference, strong signal interference, and array structure parameters. Furthermore, the practicality and robustness of our approach were evaluated in lake-based UWAC experiments, and the model trained solely on simulated signals performed competitively in the trials.

Acoustic telemetry is a commonly applied method to investigate the ecology of marine animals and provides a scientific basis for management and conservation. Crucial insight in animal behaviour and ecosystem functioning and dynamics is gained through acoustic receiver networks that are established in many different environments around the globe. The main limitation to this technique is the ability of the receivers to detect the signals from tagged animals present in the nearby area. To interpret acoustic data correctly, understanding influencing factors on the detection probability is critical. Therefore, range test studies are an essential part of acoustic telemetry research. Here, we investigated whether specific environmental factors (i.e. wind, currents, waves, background noise, receiver tilt and azimuth) influence the receiver detection probability for a permanent acoustic receiver network in Belgium. Noise and wind speed in relation to distance, the interaction of receiver tilt and azimuth and current speed were the most influential variables affecting the detection probability in this environment. The study indicated that there is high detection probability up to a distance of circa 200 m. A new setup, making use of features that render valuable information for data analysis and interpretation, was tested and revealed general applicability.