Detection of Weak Targets with Wavelet and Neural Network

Low-observable target detection in sea clutter based on the adaptive 3D-IFS algorithm

In recent years, small and weak target detection technology is one of the hotspots in information processing technology. However, the detection precision and speed of weak targets still have yet to be improved. <br/> As a branch of machine learning, deep learning has become more and more widely used in various fields. Therefore, this paper improves the deep convolutional networks for the characteristics of weak target detection, including the following three aspects: <br/> Firstly, a dataset dedicated to small and weak target detection is established. The data is sufficient and representative, which is beneficial to improve the quality of the network model. Each image in the dataset has a corresponding label that indicates the name of the image, and the coordinates and width of the target circumscribed rectangle. <br/> Secondly, the image is dilated many times so that the target having only a few pixels is covered by a lot of pixels. The highlighted portion of the image is dilated, and the result image has a larger highlighted area than the original image. <br/> Thirdly, the Faster R CNN algorithm is improved. In this paper, by adjusting the learning rates, a suitable one is determined to get the best network model. <br/> The results show that the average precision on the dataset has improved. The method proposed in this paper is of great significance for the detection of small and weak targets. For the military field, the research on weak target detection has high military value for improving early warning capability and counterattack capability.

In order to solve the problem of detection and tracking of maneuvering weak targets by aerial sonobuoys, a track-before-detect method based on stochastic parabolic Hough transform was constructed. The azimuth measurement of the target was cross-located by the adaptive correlation detection buoy group, and the corresponding position information in the target measurement was obtained. The stochastic parabolic Hough transform method was used to convert the target expansion measurement to the parameter space, and the target's maneuvering track was accumulated in the parameter space. The traditional random parabolic Hough transform method was improved by means of azimuth transformation to reduce the computational complexity of the algorithm. The problem of passive detection of maneuvering weak targets was transformed into a track detection problem based on random parabolic Hough transform under the track-before-detect model, which reduces the missed track detection of weak targets with low signal-to-noise ratio. The simulation results show that the detection probability exceeds 70 % when the signal-to-noise ratio is greater than 5 dB.

Autonomous Underwater Vehicle (AUV) is an automated navigation device, which can independently complete the underwater investigation and detection without supervision. However, the marine environment is getting increasingly severe, and the garbage in the ocean and fishnets cast by fishermen have a great negative influence on the work of AUV. To solve the problems of low precision and slow speed in the state-of-the-art network detection of weak and small targets in underwater sonar images, Yolo5 was improved by building the multi-branch shuttle neural network The dataset is collected by multi-beam forward-looking sonar Gemini720i, and includes “fishnet” and two representative types of knitted and plastic garbage, i.e., “cloth” and “plasticbag”. The original dataset is enhanced and balanced, and the effect of dataset distribution on the model performance is studied. Utilizing pre-training, the effects of Yolo5 family, i.e., Yolo5s, Yolo5m, Yolo5l and Yolo5x are compared using the balanced dataset, to explore the impacts on detecting weak and small targets of the deeper and wider networks of this family. In addition, the combinations of Yolo5s with lightweight networks, i.e., MobileNet3 and ShuffleNet2 are experimented, which further illustrates the effectiveness of the proposed network, and can satisfy the high accuracy and real-time requirements of AUV.

Infrared imaging plays a crucial role in the detection of weak targets (e.g., low-altitude vehicles) against complex backgrounds. However, challenges such as low contrast, high noise and uneven illumination often degrade image quality and hinder reliable target detection. In this paper, In order to address these problems, we systematically investigate six image enhancement algorithms: grey-scale transformation, histogram equalisation (HE), contrast-constrained adaptive histogram equalisation (CLAHE), Laplace filtering, single-scale Retinex (SSR), and multi-scale Retinex with colour reduction (MSRCR). We use objective metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) to evaluate the enhancement performance. In addition, we refine the evaluation by analysing PSNR and SSIM for the entire image as well as for regions of interest (ROIs) containing weak targets. This regional evaluation allows us to gain a more targeted understanding of the impact of enhancement on the detection of critical regions. Experimental results show that the Retinex-based method with Raschmann filtering significantly improves target visibility while suppressing background noise. This work provides a solution for infrared image enhancement and evaluation that has practical implications for aerospace sensing systems.

Infrared small weak target is difficult to detect since it often take up one or two pixel(s) in image. Nonsubsampled contourlet transform based detection algorithms work well in this case, but suffer from expensive time cost. In this paper, a novel infrared weak small target detection algorithm using optimized nonsubsampled contourlet transform is presented. We adopt an optimized nonsubsampled contourlet transform in which marking feature is faster than the original transform. First, raw images are decomposed into low frequency and high frequency coefficients by the optimized nonsubsampled contourlet, then the adaptive mean filter and the Gabor wavelet are used to suppress the background information respectively, in order to eliminate a small quantity of high light points from background, region entropy is used to distinguish background points, considering as false target alarms if not detected and true targets. Experimental results demonstrate that the presented algorithm can detect the infrared weak small targets effectively.

Estimation accuracy of sea clutter plays an important role in target detection and location. In this paper, a generalized likelihood ratio test (GLRT)-entropy joint location approach for low radar cross section (RCS) target in heavy sea clutter is proposed, which takes use of both estimated probability and localized entropy of the range image. After performing detection of target based on GLRT, the proposed approach identifies the target range bin by comparing localized entropies, which are obtained before and after clutter suppression respectively. Simulation results demonstrate performance advantages of the proposed approach over the one where only probability or entropy is used.

To solve the problem of target detection in heavy sea clutter, we make simulation study on a subspace-based clutter suppression method to improve signal to clutter ratio in the predicted target location, and thus to improve the detection performance. With the compound Gaussian model of the sea clutter, we first estimate the statistics of sea clutter by Expectation-Maximization (EM) algorithm, then exploit a subspace-based approach to further mitigate sea clutter. With the algorithm, the computational complexity is effectively reduced. Nonetheless the algorithm exhibits good performance of clutter suppression. Numerical results show that the algorithm is effective in sea clutter suppression.

The polarization information involved in radar echoes could be used to effectively distinguish targets in clutter backgrounds. In this paper, a detection method is proposed based on the characteristics of polarization scattering matrix (PSM) for detecting of weak moving targets in sea clutter. The orientation angle and the amplitude of the maximum return are obtain by the eigenvalue decomposition of the PSM to separate target echo and sea clutter. Meanwhile, the fluctuation of the polarization scattering characteristics between adjacent pulses is also considered with the help of the CP decomposition of the tensor constructed by the PSMs. Based on the feature space which has effectively distinguished target echo and sea clutter, the support vector machine (SVM) is utilized to detect target within sea clutter. Experiments based on real sea clutter data in IPIX database show that the proposed detector could effectively improves the performance for weak targets detection in sea clutter.

Infrared weak small target detection is one of the key technologies in the infrared early warning system, infrared imaging guidance system and wide-field view surveillance system etc. In the complex and low signal-to-noise ratio background environment, the target has only a few pixels. There is no shape and texture information to use. All of the above issues bring great difficulties to infrared dim-small target detection. In this paper, in order to deal with the shortcoming of large pixel tracking in moving target detection algorithm based on time domain filtering, apply the dim target detection based on Laplace operator to the subject of this paper, the false alarm points obviously to be less, and the target point is usually in the form of a single pixel, lay a good foundation for reducing the complexity of the algorithm.

: Sea clutter has a non-Rayleigh probability density when examined with a radar that has high resolution. This report discusses the properties of non- Rayleigh sea clutter and the detection of targets in non-Rayleigh sea clutter. The spatially varying clutter model is presented and is used to explain the non- Rayleigh nature of sea clutter. The variation of clutter distributions with various radar parameters such as frequency, pulse width, and polarization was determined, and the correlation properties of clutter were established. A way was developed of constructing a realistic realization of the sea surface on a computer, and the surface was used to predict non-Rayleigh clutter densities for various conditions and to indicate some finer points associated with the detection of small targets on the ocean's surface. Finally, various detectors were compared and the problems of pulse-to-pulse and scan-to-scan processing were analyzed.

Detection and tracking of marine weak targets can be effectively solved by track-before-detect (TBD) algorithms based on particle filtering. However, these algorithms are susceptible to influence from neighboring targets, leading to potential issues like misassociation and tracking failure. In this paper, an auxiliary particle flow track-before-detect algorithm designed for marine neighboring weak targets is proposed which can effectively track marine neighboring weak targets under long-tail sea clutter. Firstly, marine neighboring targets are modeled by the generalized Pareto model, and an offline lookup table is utilized to obtain a non-closed solution, decreasing calculation cost. Subsequently, prediction is employed to classify targets, and measurement information is iteratively used to determine the sequence of target updates, effectively suppressing influence from neighboring targets. Finally, particles with higher measurement energy are chosen, and the Geodesic particle flow is employed to guide the particles toward better importance distribution, which enhances the accuracy of target trajectory estimation. Simulation experiments indicate that compared with track-before-detect algorithms based on parallel partition (PP) and auxiliary parallel partition (APP), the proposed algorithm shows an increase of 43.1% and 25.8% in detection probability at 6 dB, and a reduction of 76.6% and 66.2% in Root Mean Square Error (RMSE). Detection ability and trajectory estimation performance are effectively improved in the simulation, and excellent tracking performance is also confirmed in real clutter experiments.

The increasing number of space targets has a serious impact on the space environment and spacecraft safety. In this paper, aiming at the space-based optical monitoring platform, the problem of weak and small target detection under complex background star images is studied, and the effective separation method of space targets under star points is discussed. Based on the relatively constant topological relationship of stars between sequence frames, a space target detection algorithm based on invariant star topology information is proposed, which quantifies the invariant information into iso-interior angle descriptions, and designs a target and star separation strategy. The experimental results show that the method can make full use of image information, realize fast real-time detection of multiple targets, and reduce the false alarm rate to 16.3% of the original method, which provides a new method for space-based optical target detection.

An elliptical Hough transform (EHT) algorithm is proposed in the framework of track-before-detect (TBD) for joint detection and tracking of weak exoatmospheric targets. The new approach exploits the fact that when restricted to a two-body problem, the exoatmospheric target often follows an elliptical orbit, and thus the Hough transform integrated with orbital geometry information would have better detection performance. The relationship between the original radar measurements in data space and the elliptical parameters in parameter space is explicitly derived with multiple steps of coordinate transformation. It is found that the data points mapping into the parameter space essentially represent a quartic curve. An EHT-based algorithm is then designed, and orbit planarity is also taken into account to reduce the effect of noise accumulation. The influences of primary and secondary thresholds and the signal-to-noise ratio (SNR) on the detection performance are compared by simulations. Additionally, a real radar tracking dataset from a scientific satellite on 28 May 2017 is used to investigate the efficiency of the method. By adding some imaginary clutter to the raw orbit, the results indicate that it is very effective in detecting the real satellite trajectory in a low signal-to-noise ratio (SNR) environment. The advantage of the new method lies in it can not only simultaneously detect and track weak exoatmospheric targets but also can predict the trajectory by using these available detected parameters.

Bistatic radar target detection is often complicated by a direct-path signal from the bistatic transmitter to the bistatic receiver. This direct-path signal can be much stronger than the signal scattered by the target of interest. In order to detect targets in the presence of the direct-path signal, it must be attenuated or cancelled. In this paper, we determine the detectability of weak targets in the presence of a direct-path in the presence of additive white Gaussian noise (AWGN) in the clairvoyant case, in which the target amplitude, direct-path amplitude, and the AWGN variance is known. This results in a detection problem in which the likelihood ratio is not in general a monotonic function of the matched filter output. This means that the most powerful test with a fixed false-alarm rate is not a threshold test on the matched filter output. We analyze the ability of a radar with phased array and delay-Doppler matched filter processor to exploit differences in the direct target direction, delay, and Doppler to attenuate the direct-path and optimally detect the target. This performance analysis is useful as a benchmark in developing direct-path cancellation algorithms.