Pub Date : 2024-11-01DOI: 10.1016/j.sigpro.2024.109745
Jingxi Shi , Xueqi Yao , Zhihang Wang , Ziyang Cheng , Lei Xie
Compared with the uniform arrays, the conformal array can effectively reduce the radar cross section and improve the utilization of the limited space in the aircraft. However, the special array structure aggravates the non-stationarity of the clutter, and the typical blocking matrix construction method in the space–time adaptive processing (STAP) is not appropriate any more. To address these issues, a reduced dimension STAP algorithm based on penalty sequential convex programming in the generalized sidelobe cancellation structure is proposed. The blocking matrix, channel selection vector and STAP weights can be optimized simultaneously in the algorithm framework. To tackle the resultant nonconvex problem, we formulate the original optimization function as a quasi-convex form and solve these parameters alternately within an iterative framework. Numerical simulations are provided to validate the proposed method and demonstrate its high performance.
{"title":"Reduced-dimension STAP method for conformal array based on sequential convex programming","authors":"Jingxi Shi , Xueqi Yao , Zhihang Wang , Ziyang Cheng , Lei Xie","doi":"10.1016/j.sigpro.2024.109745","DOIUrl":"10.1016/j.sigpro.2024.109745","url":null,"abstract":"<div><div>Compared with the uniform arrays, the conformal array can effectively reduce the radar cross section and improve the utilization of the limited space in the aircraft. However, the special array structure aggravates the non-stationarity of the clutter, and the typical blocking matrix construction method in the space–time adaptive processing (STAP) is not appropriate any more. To address these issues, a reduced dimension STAP algorithm based on penalty sequential convex programming in the generalized sidelobe cancellation structure is proposed. The blocking matrix, channel selection vector and STAP weights can be optimized simultaneously in the algorithm framework. To tackle the resultant nonconvex problem, we formulate the original optimization function as a quasi-convex form and solve these parameters alternately within an iterative framework. Numerical simulations are provided to validate the proposed method and demonstrate its high performance.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"228 ","pages":"Article 109745"},"PeriodicalIF":3.4,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-31DOI: 10.1016/j.sigpro.2024.109752
Hongtao Li , Zhoupeng Ding , Shengyao Chen, Qi Feng, Longyao Ran, Zhong Liu
Hybrid analog–digital (HAD) architecture is a promising means to realize large-scale arrays owing to the judicious trade-off between system performance and hardware complexity. This paper investigates the beampattern shaping of HAD arrays through minimizing the beampattern matching error between desired and actual patterns. Due to the nonconvex constraints on analog and digital weights and the nonconvex nonsmooth objective function, the resultant codesign is NP-hard. To address this issue, we create an efficient algorithm by leveraging block coordinate descent (BCD) and Riemannian manifold optimization. We first equivalently represent the objective function as a smooth bi-quadratic function by introducing a uni-modulus auxiliary variable. Subsequently, we alternatingly optimize the scaling factor, digital weights, analog weights and auxiliary variable under the BCD framework, where the simultaneous update of analog weights and auxiliary variable is recast as uni-modular constrained quadratic programming which is efficiently solved by Riemannian Newton method, and the digital weights have a global optimal solution via Lagrangian multiplier method. We also derive an explicit convergence condition of this algorithm. Numerical results demonstrate that the proposed algorithm has superior performance and faster convergence speed than alternative algorithms, and produces nearly the same mainlobe gain as fully digital arrays with significantly fewer radio-frequency chains.
模拟数字混合（HAD）架构在系统性能和硬件复杂性之间进行了明智的权衡，是实现大规模阵列的一种有前途的方法。本文通过最小化期望模式与实际模式之间的信号匹配误差，研究了 HAD 阵列的信号振型。由于模拟和数字权重的非凸约束以及非凸非光滑目标函数，由此产生的编码设计是 NP-困难的。为了解决这个问题，我们利用块坐标下降（BCD）和黎曼流形优化创建了一种高效算法。首先，我们通过引入一个单模辅助变量，将目标函数等效为一个平滑的二次函数。随后，我们在 BCD 框架下交替优化缩放因子、数字权重、模拟权重和辅助变量，其中模拟权重和辅助变量的同步更新被重铸成单模态受限二次方程规划，并通过黎曼牛顿法有效求解，而数字权重则通过拉格朗日乘法得到全局最优解。我们还推导出了该算法的明确收敛条件。数值结果表明，与其他算法相比，所提出的算法性能更优越，收敛速度更快，并能以显著减少的射频链产生与全数字阵列几乎相同的主波增益。
{"title":"Shaped pattern synthesis for hybrid analog–digital arrays via manifold optimization-enabled block coordinate descent","authors":"Hongtao Li , Zhoupeng Ding , Shengyao Chen, Qi Feng, Longyao Ran, Zhong Liu","doi":"10.1016/j.sigpro.2024.109752","DOIUrl":"10.1016/j.sigpro.2024.109752","url":null,"abstract":"<div><div>Hybrid analog–digital (HAD) architecture is a promising means to realize large-scale arrays owing to the judicious trade-off between system performance and hardware complexity. This paper investigates the beampattern shaping of HAD arrays through minimizing the beampattern matching error between desired and actual patterns. Due to the nonconvex constraints on analog and digital weights and the nonconvex nonsmooth objective function, the resultant codesign is NP-hard. To address this issue, we create an efficient algorithm by leveraging block coordinate descent (BCD) and Riemannian manifold optimization. We first equivalently represent the objective function as a smooth bi-quadratic function by introducing a uni-modulus auxiliary variable. Subsequently, we alternatingly optimize the scaling factor, digital weights, analog weights and auxiliary variable under the BCD framework, where the simultaneous update of analog weights and auxiliary variable is recast as uni-modular constrained quadratic programming which is efficiently solved by Riemannian Newton method, and the digital weights have a global optimal solution via Lagrangian multiplier method. We also derive an explicit convergence condition of this algorithm. Numerical results demonstrate that the proposed algorithm has superior performance and faster convergence speed than alternative algorithms, and produces nearly the same mainlobe gain as fully digital arrays with significantly fewer radio-frequency chains.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"228 ","pages":"Article 109752"},"PeriodicalIF":3.4,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The increased use of multimedia applications to share digital images has raised concerns about their security during transmission and storage as well. Thus, the need for integrating the chaotic system with compressive sensing became an important and potentially successful method for enhancing the image security. However, in the integration of a single One-dimensional (1-D) chaotic system with compressive sensing, there is a significant drawback that is the limited chaotic behaviour and key space, which makes it vulnerable against brute force and statistical attacks. Hence, enlarging the key space to improve security by using a single One-Dimensional (1-D) chaotic system and making it resilient against brute force attacks still needs to be addressed.
In this paper, we propose an encryption method that makes use of the notion of a companion matrix and a single One-Dimensional (1-D) chaotic system to enlarge the key space. This method converts the grayscale image into a sparse representation. Thereafter, this sparse matrix is shuffled by applying the Arnold Cat Map, where the parameters for this map are generated through the usage of a One-Dimensional (1-D) Piecewise Linear Chaotic Map. Furthermore, we construct the key matrix by computing the eigenvalues of the companion matrix, and then we diffuse the cipher image to improve the security against statistical attacks.
Experimental results demonstrate that the proposed method balances the security and image reconstruction quality effectively. The advantage of the proposed method is that even by using a single One-Dimensional (1-D) chaotic system (i.e., faster in implementation), by using the concept of companion matrix, it achieves a significantly larger key space of that is larger than the several existing state-of-the-art methods that use hyperchaotic systems.
{"title":"A companion matrix-based efficient image encryption method","authors":"Rohit , Shailendra Kumar Tripathi , Bhupendra Gupta , Subir Singh Lamba","doi":"10.1016/j.sigpro.2024.109753","DOIUrl":"10.1016/j.sigpro.2024.109753","url":null,"abstract":"<div><div>The increased use of multimedia applications to share digital images has raised concerns about their security during transmission and storage as well. Thus, the need for integrating the chaotic system with compressive sensing became an important and potentially successful method for enhancing the image security. However, in the integration of a single One-dimensional (1-D) chaotic system with compressive sensing, there is a significant drawback that is the limited chaotic behaviour and key space, which makes it vulnerable against brute force and statistical attacks. Hence, enlarging the key space to improve security by using a single One-Dimensional (1-D) chaotic system and making it resilient against brute force attacks still needs to be addressed.</div><div>In this paper, we propose an encryption method that makes use of the notion of a companion matrix and a single One-Dimensional (1-D) chaotic system to enlarge the key space. This method converts the grayscale image into a sparse representation. Thereafter, this sparse matrix is shuffled by applying the Arnold Cat Map, where the parameters for this map are generated through the usage of a One-Dimensional (1-D) Piecewise Linear Chaotic Map. Furthermore, we construct the key matrix by computing the eigenvalues of the companion matrix, and then we diffuse the cipher image to improve the security against statistical attacks.</div><div>Experimental results demonstrate that the proposed method balances the security and image reconstruction quality effectively. The advantage of the proposed method is that even by using a single One-Dimensional (1-D) chaotic system (i.e., faster in implementation), by using the concept of companion matrix, it achieves a significantly larger key space of <span><math><msup><mrow><mn>2</mn></mrow><mrow><mn>400</mn></mrow></msup></math></span> that is larger than the several existing state-of-the-art methods that use hyperchaotic systems.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"228 ","pages":"Article 109753"},"PeriodicalIF":3.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.sigpro.2024.109755
Qinglei Kong , Jian Liu , Xiaodong Qu , Bo Chen , Haiyong Bao , Lexi Xu
The worldwide deployment of satellite constellations has received considerable attention in recent years. In this overview paper, we intensively study the security issues in typical data-driven on-orbit applications. First, we identify the security threats in the Global Navigation Satellite System (GNSS) and review a series of anomaly detection and auto-correlation-based methods to resist typical attacks. Second, we show the security issues in satellite communications, which suffer from the dynamic topology and frequent change of access points; meanwhile, we propose a secure mobility management framework concerning handover and location management. Third, we discuss the satellite remote sensing application’s secure on-orbit data processing paradigm. This paradigm achieves secure coordination between multiple tasks with heterogeneous on-orbit resources and secure on-orbit joint computation. As the study of securing satellite applications, especially in securing on-orbit data computation, is limited, our overview paper provides an early overview of this area. It also shows future security research trends in typical applications.
{"title":"Security in data-driven satellite applications: An overview and new perspectives","authors":"Qinglei Kong , Jian Liu , Xiaodong Qu , Bo Chen , Haiyong Bao , Lexi Xu","doi":"10.1016/j.sigpro.2024.109755","DOIUrl":"10.1016/j.sigpro.2024.109755","url":null,"abstract":"<div><div>The worldwide deployment of satellite constellations has received considerable attention in recent years. In this overview paper, we intensively study the security issues in typical data-driven on-orbit applications. First, we identify the security threats in the Global Navigation Satellite System (GNSS) and review a series of anomaly detection and auto-correlation-based methods to resist typical attacks. Second, we show the security issues in satellite communications, which suffer from the dynamic topology and frequent change of access points; meanwhile, we propose a secure mobility management framework concerning handover and location management. Third, we discuss the satellite remote sensing application’s secure on-orbit data processing paradigm. This paradigm achieves secure coordination between multiple tasks with heterogeneous on-orbit resources and secure on-orbit joint computation. As the study of securing satellite applications, especially in securing on-orbit data computation, is limited, our overview paper provides an early overview of this area. It also shows future security research trends in typical applications.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"228 ","pages":"Article 109755"},"PeriodicalIF":3.4,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-29DOI: 10.1016/j.sigpro.2024.109763
Xiaohua Xia , Dianbin Yang , Shaobo Huo , Jianhong Sun , Huatao Xiang
In the fields of multi-focus image fusion and shape from focus, accurate registration of multi-focus images is a crucial prerequisite. Due to the difficulty of feature detection in defocused regions and the limitations of global registration methods, traditional multi-focus image registration methods have low accuracy. To solve this problem, a novel multi-focus image registration method based on optical flow tracking and Delaunay triangulation is proposed. The innovation includes two aspects. The first is that optical flow tracking is utilized to extract and match the non-salient features of multi-focus images. It greatly increases the number of matching features in the defocused regions of multi-focus images. The second is that Delaunay triangulation is adopted for local registration. It makes the matching features strictly aligned. The results of the experiments show that the proposed method is superior to the traditional methods in terms of image registration accuracy and image fusion quality.
{"title":"Multi-focus image registration based on optical flow tracking and Delaunay triangulation","authors":"Xiaohua Xia , Dianbin Yang , Shaobo Huo , Jianhong Sun , Huatao Xiang","doi":"10.1016/j.sigpro.2024.109763","DOIUrl":"10.1016/j.sigpro.2024.109763","url":null,"abstract":"<div><div>In the fields of multi-focus image fusion and shape from focus, accurate registration of multi-focus images is a crucial prerequisite. Due to the difficulty of feature detection in defocused regions and the limitations of global registration methods, traditional multi-focus image registration methods have low accuracy. To solve this problem, a novel multi-focus image registration method based on optical flow tracking and Delaunay triangulation is proposed. The innovation includes two aspects. The first is that optical flow tracking is utilized to extract and match the non-salient features of multi-focus images. It greatly increases the number of matching features in the defocused regions of multi-focus images. The second is that Delaunay triangulation is adopted for local registration. It makes the matching features strictly aligned. The results of the experiments show that the proposed method is superior to the traditional methods in terms of image registration accuracy and image fusion quality.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"228 ","pages":"Article 109763"},"PeriodicalIF":3.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article investigates the Bayesian detection problem for the distributed targets in the compound Gaussian (CG) sea clutter. The CG sea clutter is formulated as a product of lognormal texture and speckle component with an inverse Wishart distribution covariance matrix (CM). A generalized likelihood ratio test (GLRT) based Bayesian detector, which can operate without training data, is proposed by integrating the speckle CM and estimating the texture using the maximum a posteriori (MAP) criterion. Additionally, three other Bayesian detectors are designed for distributed targets by exploiting the two-step GLRT, the complex-valued Rao, and Wald tests. We first derive the test statistics assuming known texture and speckle CM. Then, by incorporating the MAP-estimated texture components and speckle CM into the test statistics, we present three Bayesian detectors for distributed targets. Finally, simulation experiments validate the detection performance of the proposed Bayesian detectors using both simulated and real sea clutter data.
本文研究了复合高斯（CG）海杂波中分布式目标的贝叶斯检测问题。复合高斯海杂波是对数正态纹理和斑点分量与逆 Wishart 分布协方差矩阵（CM）的乘积。研究人员提出了一种基于广义似然比检验（GLRT）的贝叶斯检测器，该检测器可以在没有训练数据的情况下工作，其方法是整合斑点 CM 并使用最大后验（MAP）准则估计纹理。此外，通过利用两步 GLRT、复值 Rao 和 Wald 检验，还为分布式目标设计了另外三种贝叶斯检测器。我们首先假设已知纹理和斑点 CM，得出检测统计量。然后，通过将 MAP 估算的纹理成分和斑点 CM 纳入测试统计，我们提出了三种针对分布式目标的贝叶斯检测器。最后，模拟实验利用模拟和真实海杂波数据验证了所提出的贝叶斯检测器的检测性能。
{"title":"Bayesian detection for distributed targets in compound Gaussian sea clutter with lognormal texture","authors":"Hongzhi Guo, Zhihang Wang, Haoqi Wu, Zishu He, Ziyang Cheng","doi":"10.1016/j.sigpro.2024.109751","DOIUrl":"10.1016/j.sigpro.2024.109751","url":null,"abstract":"<div><div>This article investigates the Bayesian detection problem for the distributed targets in the compound Gaussian (CG) sea clutter. The CG sea clutter is formulated as a product of lognormal texture and speckle component with an inverse Wishart distribution covariance matrix (CM). A generalized likelihood ratio test (GLRT) based Bayesian detector, which can operate without training data, is proposed by integrating the speckle CM and estimating the texture using the maximum <em>a posteriori</em> (MAP) criterion. Additionally, three other Bayesian detectors are designed for distributed targets by exploiting the two-step GLRT, the complex-valued Rao, and Wald tests. We first derive the test statistics assuming known texture and speckle CM. Then, by incorporating the MAP-estimated texture components and speckle CM into the test statistics, we present three Bayesian detectors for distributed targets. Finally, simulation experiments validate the detection performance of the proposed Bayesian detectors using both simulated and real sea clutter data.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109751"},"PeriodicalIF":3.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-29DOI: 10.1016/j.sigpro.2024.109754
M. Pourjoula, M. Karbasi, M.M. Nayebi, M.R. Bagheri
Low-angle direction of arrival (DOA) estimation is a challenging issue in array processing systems. When the target’s elevation angle is extremely low, the direct-path signal (a.k.a. target) will combine with its corresponding reflection from the earth (a.k.a. ghost). Sensing systems usually have limited angle resolution capability, so the presence of two closely-spaced signals could lead to low DOA estimation accuracy. This paper aims to address this issue by utilizing a super-resolution method based on the emerging technology of multiple-input multiple-output (MIMO) radar, which offer more degrees of freedom than traditional phased array counterparts. The proposed method specifically focuses on the MIMO configuration of synthetic impulse and aperture radars (SIAR) and involves two key steps. First, the targets are resolved in range, Doppler frequency, and azimuth angle in the matched filtering process. Next, the elevation information of the filtered signal is obtained using compressed sensing (CS) approaches. Our simulation results indicate that the proposed method achieves a higher performance in distinguishing low-angle targets from their corresponding ghosts, compared with traditional methods in terms of root mean squared error (RMSE) criterion.
在阵列处理系统中，低角度到达方向（DOA）估计是一个具有挑战性的问题。当目标的仰角极低时，直达路径信号（又称目标）将与来自地球的相应反射信号（又称幽灵）结合在一起。传感系统的角度分辨率通常有限，因此两个间隔很近的信号可能会导致 DOA 估计精度较低。与传统的相控阵雷达相比，多输入多输出（MIMO）雷达具有更多的自由度，本文旨在利用基于这种新兴技术的超分辨率方法来解决这一问题。所提出的方法特别关注合成脉冲和孔径雷达（SIAR）的 MIMO 配置，包括两个关键步骤。首先，在匹配滤波过程中分辨目标的距离、多普勒频率和方位角。然后，利用压缩传感（CS）方法获取滤波信号的仰角信息。我们的模拟结果表明，与传统方法相比，就均方根误差（RMSE）标准而言，所提出的方法在区分低角度目标和相应的鬼影方面具有更高的性能。
{"title":"Resolving target and image in low altitude scenarios in synthetic impulse and aperture radars","authors":"M. Pourjoula, M. Karbasi, M.M. Nayebi, M.R. Bagheri","doi":"10.1016/j.sigpro.2024.109754","DOIUrl":"10.1016/j.sigpro.2024.109754","url":null,"abstract":"<div><div>Low-angle direction of arrival (DOA) estimation is a challenging issue in array processing systems. When the target’s elevation angle is extremely low, the direct-path signal (a.k.a. target) will combine with its corresponding reflection from the earth (a.k.a. ghost). Sensing systems usually have limited angle resolution capability, so the presence of two closely-spaced signals could lead to low DOA estimation accuracy. This paper aims to address this issue by utilizing a super-resolution method based on the emerging technology of multiple-input multiple-output (MIMO) radar, which offer more degrees of freedom than traditional phased array counterparts. The proposed method specifically focuses on the MIMO configuration of synthetic impulse and aperture radars (SIAR) and involves two key steps. First, the targets are resolved in range, Doppler frequency, and azimuth angle in the matched filtering process. Next, the elevation information of the filtered signal is obtained using compressed sensing (CS) approaches. Our simulation results indicate that the proposed method achieves a higher performance in distinguishing low-angle targets from their corresponding ghosts, compared with traditional methods in terms of root mean squared error (RMSE) criterion.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"228 ","pages":"Article 109754"},"PeriodicalIF":3.4,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-28DOI: 10.1016/j.sigpro.2024.109749
Wenlong Zhu , Xuexiao Chen , Linmei Jiang
Intelligent transportation systems are pivotal in modern urban development, aiming to enhance traffic management efficiency, safety, and sustainability. However, existing 3D Visual Scene Understanding methods often face challenges of robustness and high computational complexity in complex traffic environments. This paper proposes a Multi-Sensor Signal Fusion method based on PV-RCNN and LapDepth (PV-LaP) to improve 3D Visual Scene Understanding. By integrating camera and LiDAR data, the PV-LaP method enhances environmental perception accuracy. Evaluated on the KITTI and WHU-TLS datasets, the PV-LaP framework demonstrated superior performance. On the KITTI dataset, our method achieved an Absolute Relative Error (Abs Rel) of 0.079 and a Root Mean Squared Error (RMSE) of 3.014, outperforming state-of-the-art methods. On the WHU-TLS dataset, the method improved 3D reconstruction precision with a PSNR of 19.15 and an LPIPS of 0.299. Despite its high computational demands, PV-LaP offers significant improvements in accuracy and robustness, providing valuable insights for the future development of intelligent transportation systems.
{"title":"PV-LaP: Multi-sensor fusion for 3D Scene Understanding in intelligent transportation systems","authors":"Wenlong Zhu , Xuexiao Chen , Linmei Jiang","doi":"10.1016/j.sigpro.2024.109749","DOIUrl":"10.1016/j.sigpro.2024.109749","url":null,"abstract":"<div><div>Intelligent transportation systems are pivotal in modern urban development, aiming to enhance traffic management efficiency, safety, and sustainability. However, existing 3D Visual Scene Understanding methods often face challenges of robustness and high computational complexity in complex traffic environments. This paper proposes a Multi-Sensor Signal Fusion method based on PV-RCNN and LapDepth (PV-LaP) to improve 3D Visual Scene Understanding. By integrating camera and LiDAR data, the PV-LaP method enhances environmental perception accuracy. Evaluated on the KITTI and WHU-TLS datasets, the PV-LaP framework demonstrated superior performance. On the KITTI dataset, our method achieved an Absolute Relative Error (Abs Rel) of 0.079 and a Root Mean Squared Error (RMSE) of 3.014, outperforming state-of-the-art methods. On the WHU-TLS dataset, the method improved 3D reconstruction precision with a PSNR of 19.15 and an LPIPS of 0.299. Despite its high computational demands, PV-LaP offers significant improvements in accuracy and robustness, providing valuable insights for the future development of intelligent transportation systems.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"227 ","pages":"Article 109749"},"PeriodicalIF":3.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-24DOI: 10.1016/j.sigpro.2024.109703
Fei Chen , Hoa Van Nguyen , Alex S. Leong , Sabita Panicker , Robin Baker , Damith C. Ranasinghe
We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider limited and unknown sensor field-of-views (FoVs), sensors with limited local computational resources and communication channel capacity. The resulting distributed multi-object tracking algorithm involves solving an NP-hard multidimensional assignment problem either optimally for small-size problems or sub-optimally for general practical problems. For general problems, we propose an efficient distributed multi-object tracking algorithm that performs track-to-track fusion using a clustering-based analysis of the state space transformed into a density space to mitigate the complexity of the assignment problem. The proposed algorithm can more efficiently group local track estimates for fusion than existing approaches. To ensure we achieve globally consistent identities for tracks across a network of nodes as objects move between FoVs, we develop a graph-based algorithm to achieve label consensus and minimise track segmentation. Numerical experiments with synthetic and real-world trajectory datasets demonstrate that our proposed method is significantly more computationally efficient than state-of-the-art solutions, achieving similar tracking accuracy and bandwidth requirements but with improved label consistency.
{"title":"Distributed multi-object tracking under limited field of view heterogeneous sensors with density clustering","authors":"Fei Chen , Hoa Van Nguyen , Alex S. Leong , Sabita Panicker , Robin Baker , Damith C. Ranasinghe","doi":"10.1016/j.sigpro.2024.109703","DOIUrl":"10.1016/j.sigpro.2024.109703","url":null,"abstract":"<div><div>We consider the problem of tracking multiple, unknown, and time-varying numbers of objects using a distributed network of heterogeneous sensors. In an effort to derive a formulation for practical settings, we consider <em>limited</em> and <em>unknown</em> sensor field-of-views (FoVs), sensors with limited local <em>computational resources</em> and <em>communication channel capacity</em>. The resulting distributed multi-object tracking algorithm involves solving an NP-hard multidimensional assignment problem either optimally for small-size problems or sub-optimally for general practical problems. For general problems, we propose an efficient distributed multi-object tracking algorithm that performs track-to-track fusion using a clustering-based analysis of the state space transformed into a density space to mitigate the complexity of the assignment problem. The proposed algorithm can more efficiently group local track estimates for fusion than existing approaches. To ensure we achieve globally consistent identities for tracks across a network of nodes as objects move between FoVs, we develop a graph-based algorithm to achieve label consensus and minimise track segmentation. Numerical experiments with synthetic and real-world trajectory datasets demonstrate that our proposed method is significantly more computationally efficient than state-of-the-art solutions, achieving similar tracking accuracy and bandwidth requirements but with improved label consistency.</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"228 ","pages":"Article 109703"},"PeriodicalIF":3.4,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-23DOI: 10.1016/j.sigpro.2024.109746
Filippo Martinini , Mauro Mangia , Alex Marchioni , Gianluca Setti , Riccardo Rovatti
Accelerated MRI acquisition is widely adopted and basically consists in undersampling the current slice at the cost of a quality degradation. What samples to skip is determined by an encoder, while the quality loss is partially compensated by the use of a decoder. The hypothesis behind accelerated MRI acquisition is that to higher acceleration factors always correspond lower reconstruction qualities with an undersampling pattern that is usually fixed at design time, neglecting adaptability on the slice acquired at inference time. This paper proposes a novel accelerated MRI acquisition method that enables single-slice adaptation by dividing the acquisition into incremental batches and estimating the reconstruction quality at the end of each batch. The acquisition terminates as soon as the target quality is reached. We demonstrate the efficacy of our novel method using a state-of-the-art neural model capable of jointly optimizing the encoder and decoder. To estimate the current quality of the slice we reconstruct and propose a neural quality predictor. We demonstrate the advantages of our novel acquisition method compared to classic acquisition for two different datasets and for both line-constrained and unconstrained Cartesian sampling strategies (theoretically implementable via 2D and 3D imaging respectively).
{"title":"Incremental Undersampling MRI Acquisition With Neural Self Assessment","authors":"Filippo Martinini , Mauro Mangia , Alex Marchioni , Gianluca Setti , Riccardo Rovatti","doi":"10.1016/j.sigpro.2024.109746","DOIUrl":"10.1016/j.sigpro.2024.109746","url":null,"abstract":"<div><div>Accelerated MRI acquisition is widely adopted and basically consists in undersampling the current slice at the cost of a quality degradation. What samples to skip is determined by an encoder, while the quality loss is partially compensated by the use of a decoder. The hypothesis behind accelerated MRI acquisition is that to higher acceleration factors always correspond lower reconstruction qualities with an undersampling pattern that is usually fixed at design time, neglecting adaptability on the slice acquired at inference time. This paper proposes a novel accelerated MRI acquisition method that enables single-slice adaptation by dividing the acquisition into incremental batches and estimating the reconstruction quality at the end of each batch. The acquisition terminates as soon as the target quality is reached. We demonstrate the efficacy of our novel method using a state-of-the-art neural model capable of jointly optimizing the encoder and decoder. To estimate the current quality of the slice we reconstruct and propose a neural quality predictor. We demonstrate the advantages of our novel acquisition method compared to classic acquisition for two different datasets and for both line-constrained and unconstrained Cartesian sampling strategies (theoretically implementable via 2D and 3D imaging respectively).</div></div>","PeriodicalId":49523,"journal":{"name":"Signal Processing","volume":"228 ","pages":"Article 109746"},"PeriodicalIF":3.4,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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