Joint Target Detection Research Articles

Joint Target Detection and Channel Estimation for Distributed Massive MIMO ISAC Systems

Joint Target Detection and Channel Estimation for Distributed Massive MIMO ISAC Systems

A Learning Bayesian MAP Framework for Joint SAR Imaging and Target Detection

A Learning Bayesian MAP Framework for Joint SAR Imaging and Target Detection

Scene-aware refinement network for unsupervised monocular depth estimation in ultra-low altitude oblique photography of UAV

Using depth estimation joint target detection networks to locate targets in the UAV field of view is a novel application in the depth estimation research field. The presence of more depth variations and low-texture regions in the ultra-low altitude oblique photographic images make them trickier to train for an excellent depth estimation network compared to autonomous driving scenarios. This presents a challenge in achieving optimal training. This study investigates the problem of unsupervised monocular depth estimation for ultra-low altitude oblique photography images. It aims to make subsequent advanced vision tasks better benefit from excellent depth estimation results in terms of overcoming complex scenes. The lack of effective back-projection directionality in training using adjacent frames is attributed to the extensive low-textured areas contained in the training data for complex ultra-low altitude oblique photography. We propose a self-supervised scene-aware refinement learning architecture from the perspective of enhancing feature perception to deal with such problems. The architecture consists of a multi-resolution feature fusion depth network and a perceptual refinement network (PRNet), together with a pose network to enhance regional differences in complex environments from a refined feature context perspective to obtain higher quality depth maps. We rethink the problem of depth information recovery and design the edge information aggregation (EIA) module, which is configured in the decoder section to refine the local region depth detail representation. We design several loss terms to constrain the training of the network in order to improve the quality of depth estimation. Our method is compared with six state-of-the-art self-supervised monocular depth estimation methods on three datasets (UAVid 2020, WildUAV, UAV ula). The experimental results demonstrate that our model achieves the best performance in most scenarios. The code and the private dataset (UAV ula) can be publicly available at https://github.com/takisu0916/MRFEDepth.

Beam-Space MIMO Radar for Joint Communication and Sensing With OTFS Modulation

Motivated by automotive applications, we consider joint radar sensing and data communication for a system operating at millimeter wave (mmWave) frequency bands, where a Base Station (BS) is equipped with a co-located radar receiver and sends data using the Orthogonal Time Frequency Space (OTFS) modulation format. We consider two distinct modes of operation. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Discovery</i> mode, a single common data stream is broadcast over a wide angular sector. The radar receiver must detect the presence of not yet acquired targets and perform coarse estimation of their parameters (angle of arrival, range, and velocity). In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Tracking</i> mode, the BS transmits multiple individual data streams to already acquired users via beamforming, while the radar receiver performs accurate estimation of the aforementioned parameters. Due to hardware complexity and power consumption constraints, we consider a hybrid digital-analog architecture where the number of RF chains and A/D converters is significantly smaller than the number of antenna array elements. In this case, a direct application of the conventional MIMO radar approach is not possible. Consequently, we advocate a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">beam-space approach</i> where the vector observation at the radar receiver is obtained through a RF-domain beamforming matrix operating the dimensionality reduction from antennas to RF chains. Under this setup, we propose a likelihood function-based scheme to perform joint target detection and parameter estimation in Discovery, and high-resolution parameter estimation in Tracking mode, respectively. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while closely approaching the Cramér-Rao Lower Bound (CRLB) of the corresponding parameter estimation problem.

A Space-ground Joint Target Detection System Based on Artificial Intelligence

Due to the low efficiency and poor adaptability of template matching in target detection, this paper represents a space-ground joint detection system based on artificial intelligence, which takes advantage of the high computation power of the ground system to train neural networks and utilize the measurement and control channel to update and synchronize the on-orbit system. Based on YOLOv5, an oriented bounding box is used for labeling and training, the angle is considered as a class and the loss for the angle is added to the code. After training the network with the ground system, the weight will be sent to the satellite. This process would repeat once there are new training data that worse the update. Based on the new routine for target detection, the accuracy and robustness are significantly higher than before.

Target Detection for Distributed MIMO Radar With Non-Orthogonal Waveforms in Cluttered Environments

Orthogonal radar waveforms originating from spatially distributed transmitters (TXs) usually arrive at a receiver (RX) in non-orthogonal forms, as they propagate through different paths with distinct delays and Doppler frequencies in distributed multi-input multi-output (MIMO) radar. Non-orthogonal waveforms complicate the composition of the target and clutter returns, making it more challenging to separate one from the other. In this paper, we consider joint target detection and clutter mitigation in distributed MIMO radar. We first present a general signal model for distributed MIMO radar in cluttered environments. Next, we propose three families of detection solutions, including non-coherent detectors that require no phase estimation and are relatively simple to implement, coherent detectors that offer enhanced detection performance given accurate phase information, and hybrid detectors that are a compromise of the former two, requiring only local phase coherence but no explicit phase estimation. In addition, approximate solutions are proposed in each category with further complexity reduction, using high clutter-to-noise (CNR) approximation or convex relaxation. Simulation results are presented to demonstrate the performance of the proposed detectors, which outperform their earlier counterparts that neglect the presence of clutter.

Saliency-Based SAR Target Detection via Convolutional Sparse Feature Enhancement and Bayesian Inference

Traditional synthetic aperture radar (SAR) target detection methods use matched filtered SAR images as input, and the detection performance is restricted due to the high sidelobes and speckle noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing sidelobes, noise, and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this article, to improve the target detection performance using sparse SAR images as input, we proposed a convolutional sparse feature enhancement method to meet the needs of Bayesian saliency detection. The proposed Bayesian saliency joint target detection method comprised the following three steps: first, to obtain sparse SAR images with continuous contours and fewer holes in the target area, we proposed a convolutional L1 sparse regularization method. Second, a regularization parameter optimization method was derived to quickly obtain optimal regularization parameters for saliency detection. Finally, target detection results were obtained through a superpixel-based Bayesian saliency joint detector. Extensive experiments verified that the proposed method could improve the SAR target detection accuracy in complex backgrounds.

From target tracking to targeting track: A data-driven yet analytical approach to joint target detection and tracking

This paper addresses the problem of real-time detection and tracking of a non-cooperative target in the challenging scenario with almost no a-priori information about target birth, death, dynamics and detection probability. Furthermore, there are false and missing data at an unknown yet low rate in the measurements. The only information given in advance is about the target-measurement model and the constraint that there is no more than one target in the scenario. To solve these challenges, we model the movement of the target by using a polynomial trajectory function of time (T-FoT), which aims to estimate the continuous-time trajectory of the target rather than a series of discrete-time point estimates as is done in most existing filters/trackers. Data-driven T-FoT initiation and termination strategies are proposed for identifying the (re-)appearance and disappearance of the target. During the existence of the target, real target measurements are distinguished from clutter if the target indeed exists and is detected, in order to update the T-FoT at each scan for which we design a least-squares estimator. Overall, our approach is Markov-free, data-driven yet analytical. Simulations using either linear or nonlinear systems are conducted to demonstrate the effectiveness of our approach in comparison with the Bayes optimal Bernoulli filters. The results show that our approach is comparable to the perfectly-modeled filters, even outperforms them in some cases while requiring much less a-priori information and computing much faster.

Joint Pilot Optimization, Target Detection and Channel Estimation for Integrated Sensing and Communication Systems

Radar sensing will be integrated into the 6G communication system to support various applications. In this integrated sensing and communication system, a radar target may also be a communication channel scatterer. In this case, the radar and communication channels exhibit certain joint burst sparsity. We propose a two-stage joint pilot optimization, target detection and channel estimation scheme to exploit such joint burst sparsity and pilot beamforming gain to enhance detection/estimation performance. In Stage 1, the base station (BS) sends downlink pilots (DP) for initial target search, and the user sends uplink pilots (UP) for channel estimation. Then the BS performs joint target detection and channel estimation. In Stage 2, the BS exploits the prior information obtained in Stage 1 to optimize the DP signal to further refine the performance. A Turbo Sparse Bayesian inference algorithm is proposed for joint target detection and channel estimation in both stages. The pilot optimization problem in Stage 2 is a semi-definite programming with rank-1 constraints. By replacing the rank-1 constraint with a tight and smooth approximation, we propose an efficient pilot optimization algorithm based on the majorization-minimization (MM) method. Simulations verify the advantages of the proposed scheme.

A Novel Demixing Algorithm for Joint Target Detection and Impulsive Noise Suppression

This work considers a collocated radar scenario where a probing signal is emitted toward the targets of interest and records the received echoes. Estimating the relative delay-Doppler shifts of the targets allows determining their relative locations and velocities. However, the received radar measurements are often affected by impulsive non-Gaussian noise which makes a few measurements partially corrupted. While demixing radar signal and impulsive noise is challenging in general by traditional subspace-based methods, atomic norm minimization (ANM) has been recently developed to perform this task in a much more efficient manner. Nonetheless, the ANM cannot identify close delay-Doppler pairs and also requires many measurements. Here, we propose a smoothed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> atomic optimization problem encouraging both the sparse features of the targets and the impulsive noise. We design a majorization-minimization algorithm that converges to the solution of the proposed non-convex problem using alternating direction method of multipliers (ADMM). Simulations results verify the superior accuracy of our proposed algorithm even for very close delay-Doppler pairs in comparison to ANM with around 40 dB improvement.

Low-Light Image Enhancement and Target Detection Based on Deep Learning

Most computer vision applications demand input images to meet their specific requirements. To complete different vision tasks, e.g., object detection, object recognition, and object retrieval, low-light images must be enhanced by different methods to achieve different processing effects. The existing image enhancement methods, which are based on non-physical imaging models, and image generation methods, which are based on deep learning, are not ideal for low-light image processing. To solve the problem, this paper explores low-light image enhancement and target detection based on deep learning. Firstly, a simplified expression was constructed for the optical imaging model of low-light images, and a Haze-line was proposed for color correction of low-light images, which can effectively enhance low-light images based on the global background light and medium transmission rate of the optical imaging model of such images. Next, network framework adopted by the proposed low-light image enhancement model was introduced in detail: the framework includes two deep domain adaptation modules that realize domain transformation and image enhancement, respectively, and the loss functions of the model were presented. To detect targets based on the output enhanced image, a joint enhancement and target detection method was proposed for low-light images. The effectiveness of the constructed model was demonstrated through experiments.

Entanglement-Assisted Joint Monostatic-Bistatic Radars.

With the help of entanglement, we can build quantum sensors with sensitivity better than that of classical sensors. In this paper we propose an entanglement assisted (EA) joint monostatic-bistatic quantum radar scheme, which significantly outperforms corresponding conventional radars. The proposed joint monostatic-bistatic quantum radar is composed of two radars, one having both wideband entangled source and EA detector, and the second one with only an EA detector. The optical phase conjugation (OPC) is applied on the transmitter side, while classical coherent detection schemes are applied in both receivers. The joint monostatic-bistatic integrated EA transmitter is proposed suitable for implementation in LiNbO3 technology. The detection probability of the proposed EA joint target detection scheme outperforms significantly corresponding classical, coherent states-based quantum detection, and EA monostatic detection schemes. The proposed EA joint target detection scheme is evaluated by modelling the direct radar return and forward scattering channels as both lossy and noisy Bosonic channels, and assuming that the distribution of entanglement over idler channels is not perfect.

Location-Aware Beamforming for MIMO-Enabled UAV Communications: An Unknown Input Observer Approach

Numerous communications and networking challenges prevent deploying unmanned aerial vehicles (UAVs) in extreme environments where the existing wireless technologies are mainly ground-focused; and, as a consequence, the air-to-air channel for UAVs is not fully covered. In this paper, we address the crucial problem of beamforming in the UAV-based communications by utilizing a spatial estimation algorithm, which relies on the theory of observers in the control systems literature. The proposed spatial estimation algorithm relies on using a delay-tolerant observer-based predictor, which can accurately predict the positions of the target UAVs in the presence of uncertainties due to factors such as wind gust. The solution, which uses discrete-time unknown input observers (UIOs), reduces the joint target detection and communication complication notably by operating on the same device and performs reliably in the presence of channel blockage and interference. The effectiveness of the proposed approach is demonstrated using simulation results.

Distributed joint target detection, tracking and classification via Bernoulli filter

This paper aims to solve the problem of distributed joint detection, tracking and classification (D-JDTC) of a target on a peer-to-peer sensor network. The target can be present or not, can belong to different classes, and depending on its class can behave according to different kinematic modes. Accordingly, it is modelled as a suitably extended Bernoulli random finite set (RFS) uniquely characterized by existence, classification, class-conditioned mode and class & mode-conditioned state probability distributions. Existing algorithms have been devised to perform target JDTC based on a single sensor and can only be easily extended to multiple sensors in a centralized configuration, wherein a fusion centre gathers measurements from all sensors. In this paper, by designing a suitable rule for fusing local posteriors that convey information on target existence, class, mode and state from different sensor nodes, a novel scalable and fault-tolerant D-JDTC Bernoulli filter is proposed, and its performance is evaluated by means of simulation experiments.

Unmanned aerial vehicle passive detection for Internet of Space Things

Unmanned aerial vehicle (UAV) passive detection is an essential part of intelligent surveillance in the Internet of Space Things (IoST). The satellite has the potential to play a vital role in many applications of IoST. This paper proposes a novel UAV passive detection using multiple satellites. Specifically, we consider target detection in the ground dual receiver system, which is divided into reference channel and monitoring channel. The reference channel receives the direct wave signals from multiple external emitters, and the monitoring channel receives the echo signals reflected by the UAV. We propose a multiple satellite joint target detection method based on a generalized likelihood ratio test (GLRT). This detection algorithm is robust to the reference channel noise. According to the known or unknown noise variance and interference targets, the conditions are divided into three types, where we use GLRT to design the detectors under different conditions. Extensive simulations are conducted to evaluate the performance of the algorithm under various parameter settings.

Multi-source image target detection technology based on salient feature fusion

The ground target can be easily affected by the complexity of ground objects and photoelectric interference, so the accuracy of target detection in complex background is not always satisfying. To this end, joint target detection of Infrared/Visible image, IR/VI, is adopted, and the feature representation is achieved by applying the method of deep image feature extraction. According to the different imaging characteristics of the target, the feature fusion is carried out to form a unified target feature representation. As a result, target detection based on deep learning can be achieved by the model established by such fused feature. Experiments show that the proposed method can effectively improve the accuracy of target detection.

Joint Power allocation and target detection in distributed MIMO radars

Joint Power allocation and target detection in distributed MIMO radars

Semantic segmentation for multiscale target based on object recognition using the improved Faster-RCNN model

Image semantic segmentation has received great attention in computer vision, whose aim is to segment different objects and provide them different semantic category labels so that the computer can fully obtain the semantic information of the scene. However, the current research mainly focuses on color image data as training, for outdoor scenes and single task semantic segmentation. This paper carries out multi-task semantic segmentation model in the complex indoor environment on joint target detection using RGB-D image information based on the improved Faster-RCNN algorithm, which can simultaneously realize the indoor scene semantic segmentation, target classification and detection multiple visual tasks. In which, in view of the influence of uneven lighting in the environment, the method of fusion of RGB images and depth images is improved. While enhancing the fusion image feature information, it also improves the efficiency of model training. Simultaneously, in order to meet the needs for operating on multi-scale target objects, the non-maximum value suppression algorithm is improved to improve the model’s performance. So as to realize the output of the model’s multi-task information, the loss function has also been redesigned and optimized. The indoor scene semantic segmentation model constructed in this paper not only has good performance and high efficiency, but also can segment the contours of different scale objects clearly and adapt to the indoor uneven lighting environment.

Target Detection for Multistatic Radar in the Presence of Deception Jamming

This study investigates the target detection problem of multistatic radar in the presence of deception jamming. A detection algorithm based on non-coherent integration is presented to determine whether a target exists in each probed spatial resolution cell (SRC). This algorithm can suppress the jamming to some extent because the spatial divergence of the jamming signal inhibits the effective energy accumulation for detection. However, a jamming signal influences more SRCs than a target. Given that the power of deception jamming is usually larger than that of the target, the global statistics of the SRCs can still exceed the detection threshold with a certain probability, which leads to many false targets. Therefore, a discriminator is presented to distinguish the jamming signals from the target echoes and noises. Subsequently, a joint target detection and deception jamming suppression method that can simultaneously detect targets and suppress the jamming is derived to eliminate the interference in the relative SRCs. Simulation results show that the proposed algorithm can suppress the deception jamming while accurately detecting multiple targets.

Retracted: Jointly network image processing: multi‐task image semantic segmentation of indoor scene based on CNN

Image semantic segmentation has always been a research hotspot in the field of robots. Its purpose is to assign different semantic category labels to objects by segmenting different objects. However, in practical applications, in addition to knowing the semantic category information of objects, robots also need to know the position information of objects to complete more complex visual tasks. Aiming at a complex indoor environment, this study designs an image semantic segmentation network framework of joint target detection. Using the parallel operation of adding semantic segmentation branches to the target detection network, it innovatively implements multi-vision task combining object classification, detection and semantic segmentation. By designing a new loss function, adjusting the training using the idea of transfer learning, and finally verifying it on the self-built indoor scene data set, the experiment proves that the method in this study is feasible and effective, and has good robustness.