Track-to-track association for intelligent vehicles by preserving local track geometry

Track-to-track association (T2TA) is a challenging task in situational awareness in intelligent vehicles and surveillance systems. In this paper, the problem of track-to-track association with sensor bias (T2TASB) is considered. Traditional T2TASB algorithms only consider a statistical distance cost between local tracks from different sensors, without exploiting the geometric relationship between one track and its neighboring ones from each sensor. However, the relative geometry among neighboring local tracks is usually stable, at least for a while, and thus helpful in improving the T2TASB. In this paper, we propose a probabilistic method, called the local track geometry preservation (LTGP) algorithm, which takes advantage of the geometry of tracks. Assuming that the local tracks of one sensor are represented by Gaussian mixture model (GMM) centroids, the corresponding local tracks of the other sensor are fitted to those of the first sensor. In this regard, a geometrical descriptor connectivity matrix is constructed to exploit the relative geometry of these tracks. The track association problem is formulated as a maximum likelihood estimation problem with a local track geometry constraint, and an expectation–maximization (EM) algorithm is developed to find the solution. Simulation results demonstrate that the proposed methods offer better performance than the state-of-the-art methods.The authors gratefully acknowledge the Autonomous Vision Group for providing the KITTI dataset. The authors also would like to thank the editors and referees for the valuable comments and suggestions.The Research Funds of Chongqing Science and Technology Commission, the National Natural Science Foundation of China, the Key Project of Crossing and Emerging Area of CQUPT, the Research Fund of young-backbone university teacher in Chongqing province, Chongqing Overseas Scholars Innovation Program, Wenfeng Talents of Chongqing University of Posts and Telecommunications, Innovation Team Project of Chongqing Education Committee, the National Key Research and Development Program, the Research and Innovation of Chongqing Postgraduate Project, the Lilong Innovation and Entrepreneurship Fund of Chongqing University of Posts and Telecommunications.http://www.mdpi.com/journal/sensorsam2021Electrical, Electronic and Computer Engineerin

Underwater source localization using time difference of arrival and frequency difference of arrival measurements based on an improved invasive weed optimization algorithm

Abstract In this study, we propose an underwater localization method based on an improved invasive weed optimization algorithm to accurately locate moving sources in underwater sensor networks. First, the Lévy flight model is introduced into the invasive weed optimization algorithm to enhance its global search ability and avoid falling into local optima. At the same time, under the condition that the observed noise of each observation is Gaussian noise and does not consider the influence of other error factors, the localization error is adopted as the objective function to obtain an initial estimate for the unknown source parameter. Then, the obtained initial estimates of the target position and velocity as well as the target parameter error are utilized to construct a new localization model. Finally, the precise position of the source and its velocity are obtained according to the weighted least square method. The performance of the algorithm is verified by comparing it with the Cramér–Rao Lower Bound (CRLB). Results from simulations indicate that the algorithm proposed in this paper has excellent localization accuracy compared to existing methods and achieves results close to the CRLB

Shared Aperture Multibeam Forming of Time-Modulated Linear Array

A novel technique is proposed in this paper for shared aperture multibeam forming in a complex time-modulated linear array. First, a uniform line array is interleaved randomly to form two sparse array subarrays. Subsequently, the theory of time modulation for linear arrays is applied in the constructed subarrays. In the meantime, the switch-on time sequences for each element of the two subarrays are optimized by an optimized differential evolution (DE) algorithm, i.e., the scaling factor of the sinusoidal iterative chaotic system and the adaptive crossover probability factor are used to enhance the diversity of the population. Lastly, the feasibility of the new technique is verified by the comparison between this technique and the basic multibeam algorithm in a shared aperture and the algorithm of iterative FFT. The results of simulations confirm that the proposed algorithm can form more desired beams, and it is superior to other similar approaches.Peer Reviewe

An Agile Privacy-Preservation Solution for IoT-Based Smart City Using Different Distributions

In today's world, everything is connected via the Internet. Smart cities are one application of the Internet of Things (IoT) that is aimed at making city management more efficient and effective. However, IoT devices within a smart city may collect sensitive information. Protecting sensitive information requires maintaining privacy. Existing smart city solutions have been shown not to offer effective privacy protection. We propose a novel continuous method called Differential Privacy-Preserving Smart City (DPSmartCity). When the IoT device produces sensitive data, it applies differential privacy techniques as a privacy-preserving method that uses Laplace distributions or exponential distributions. The controller receives the perturbed data and forwards it to the SDN. SDN controllers eventually send the data to the cloud for further analysis. Accordingly, if the data is not sensitive, it is directly uploaded to the cloud. In this way, DPSmartCity provides a dynamic environment from the point of view of privacy preservation. As a result, adversaries are unable to easily compromise the privacy of the devices. The solution incurs at most 10-18% overhead on IoT devices. Our solution can therefore be used for IoT devices that are capable of handling this overhead