Learning-Based Transmit Resource Management Scheme for Multiple Target Tracking With Active Jamming Mitigation

Energy-efficiency is an issue that needs to be considered while designing a scheme for target tracking. The purpose of this paper is to propose a scheme for target tracking that mitigates the power consumption. The proposed scheme consists of a dynamic leadership delegation based activation algorithm for efficient target tracking. A dynamic activation range for awaking sensors in the neighborhood of the target is used depending on its speed. The effectiveness of the proposed scheme is analysed through extensive simulations. The performance of the proposed approach is observed to be better than the existing approaches in terms of the accuracy of tracking and the amount of power consumption.

Two-tier network structure is usually used to lighten the energy consumption in Wireless Multimedia Sensor Networks (WMSNs) for large scale surveillance application. Previous methods for target tracking in two-tier WMSNs mostly focused on reducing energy cost to prolong lifetime of the networks without considering the effect of visual target tracking. This paper presents a collaborative multi-tier nodes strategy for visual target tracking in WMSNs. In the proposed method, the target detection and tracking are carried out by the cooperation of lower-tier scalar sensor nodes and upper-tier camera nodes. Each activated camera node uses an adaptive Gaussian mixture model to extract moving target and Mean Shift algorithm for target tracking. A novel function measuring the value of utility of tracking as well as energy cost is used to select optimal camera node. Experimental results show that the method can effectively achieve tradeoff between performance and energy consumption in real scenes.

For target tracking applications, small wireless sensors provide accurate information since they can be deployed and operated near the phenomenon. These sensing devices have the opportunity of collaboration amongst themselves to improve the target localization and tracking accuracies. Distributed data fusion architecture provides a collaborative tracking framework. Due to the energy constraints of these small, sensing and wireless communicating devices, a common trend is to put some of them into a dormant state. In addition to a selective sensor activation strategy based on the maximum mutual information metric, in this paper, we devise the Information-Controlled Transmission Power (ICTP) adjustment in order to improve the energy savings. The essence of the proposed ICTP scheme for collaborative target tracking lies behind the idea that the sensors with more information use higher transmission powers than the sensors with less information in order to share their target state information with the neighboring sensors.

Considering tracking a target equipped with an intercept receiver in the electronic warfare environment, how to balance the system resource to achieve the pre-defined tracking requirement and enhance the systems survival capability is significant for the moving airborne radar systems(ARS) network. This paper proposed a novel low probability of intercept(LPI) based resource allocation (LPI-RA) strategy for target tracking in the moving airborne radar network. Firstly, the target tracking requirement is introduced according to the posterior Cramer-Rao lower bound (PCRLB) for the moving platform and the pre-set threshold. Then, the intercept probability of the intercept receiver is calculated for each moving node in the network, which indicates the LPI performance of the single radar station. Based on these two aspects of system requirements, a novel weighted objective function is established and a non-convex problem containing both continuous and binary variables is proposed and solved by a two-step fast solution algorithm. Correspondingly, the LPI-RA strategy is formulated by adjusting the radar nodes and transmitting power in the moving network to achieve the predetermined target tracking accuracy and reduce the intercept probability of the network system. Finally, the simulations are given to validate the superiority of the proposed strategy over the traditional allocation strategies.

A general visual-servoing control strategy for active non-cooperative target tracking of space manipulators

Multiple radar systems have shown significant advances in target tracking. Reasonable power allocation strategy can sufficiently utilize the limited power resources and in turn, improve the system tracking performance. However, towards the existing power allocation strategies, the system configuration is only restricted to the centralized architecture, and practical communication requirements and system robustness have not been considered. To tackle these problems, we propose a joint selection and power allocation (JSPA) strategy for target tracking in multiple radar systems with decentralized configuration. The optimal fusion is presented to obtain the global posterior distribution in terms of the local filtering densities. Then the corresponding weights for the fusion estimation can be updated according to distributed particle filter (DPF). Finally, the decentralized posterior Cramer-Rao lower bound (PCRLB) is derived based on the optimal fusion, and consequently, employed as an optimization metric for JSPA strategy. Numerical results demonstrate the superior performance of the proposed strategy.

Target tracking is crucial to many applications in wireless sensor networks (WSNs). Existing tracking schemes used in WSNs can basically be classified two categories, clustering and predicting. Considering network clustering consumes much energy for limited-energy WSNs, a predicting target tracking scheme is proposed called MC-MPMC (measurement compensation-based mixture population Monte Carlo) which tracks the target based on predicted locations in this work. Adaptive mixture PMC model for generating proposals varying from each iteration is proposed to guarantee sampling diversity. And also, extra measurements or observations generating method is introduced to compensate missed prediction locations or false estimations, avoiding tracking behavior degradation. Firstly, samples drawn from the proposals of next iteration can be generated by a mixture method to avoid sample degeneracy. Secondly, sample weights are jointly computed based on adaptive fusion of compensation measurement and true measurements. Thirdly, HTC method is combined to MC-MPMC scheme to decrease energy consumption in WSNs. Then, the proposed method is verified through comprehensive experiments about tracking error, delay and consumption predictions. Moreover, performance comparisons of MC-MPMC with other tracking schemes are also proposed.

In this study, a communication-awareness adaptive resource scheduling (CARS) strategy for multiple target tracking in a multiple radar system (MRS) is proposed. The CARS strategy aims to maximise the tracking performance of MRS, whilst minimising the interference from MRS to communication systems (CSs), whose mechanism is to simultaneously control the revisit frame interval of each target and determine the activation of radar nodes, the radar-target assignment and the allocation of transmitted power. Mathematically, the CARS strategy is formulated as an optimization problem, which contains both the continuous variable and the discrete (integer) variable. To tackle the resultant mixed-integer, non-convex, and non-linear problem efficiently, incorporating with the proposed hybrid particle swarm optimization algorithm based on the Kullback–Leibler divergence, a two-stage solution technique is developed to obtain the near-optimal solution. Numerical simulation results are provided to validate the proposed CARS strategy and demonstrate its superiority over the traditional scheduling strategies.

Recent innovations in modern radar for designing transmitted waveforms, coupled with new algorithms for adaptively selecting the waveform parameters at each time step, have resulted in improvements in tracking performance. Of particular interest are waveforms that can be mathematically designed to have reduced ambiguity function sidelobes, as their use can lead to an increase in the target state estimation accuracy. Moreover, adaptively positioning the sidelobes can reveal weak target returns by reducing interference from stronger targets. The manuscript provides an overview of recent advances in the design of multicarrier phase-coded waveforms based on Bjorck constant-amplitude zero-autocorrelation (CAZAC) sequences for use in an adaptive waveform selection scheme for mutliple target tracking. The adaptive waveform design is formulated using sequential Monte Carlo techniques that need to be matched to the high resolution measurements. The work will be of interest to both practitioners and researchers in radar as well as to researchers in other applications where high resolution measurements can have significant benefits. Table of Contents: Introduction / Radar Waveform Design / Target Tracking with a Particle Filter / Single Target tracking with LFM and CAZAC Sequences / Multiple Target Tracking / Conclusions

In this paper, we consider an adaptive node and power simultaneous scheduling (ANPSS) strategy for target tracking in distributed multiple radar systems. For all of the available nodes, with full resources allocation, minimizing estimation mean-square error (MSE) may exceed the predetermined system tracking performance goal and cause unnecessary resources consumption. Therefore, tracking performance driven resource allocation schemes for multiple radar systems are proposed. For a predefined estimation MSE threshold, the total transmitted energy is minimized by optimally scheduling node and power resources with the required tracking accuracy. For a given total power budget, the attainable tracking MSE is minimized by optimizing node and power allocation among the transmit radars. The Bayesian Cramer-Rao lower bound (BCRLB) is used as a performance metric. The resulting optimization problems are solved through Zoutendijk method of feasible directions (ZMFD). Numerical results demonstrate that significant resource savings could be obtained through the proposed schemes.

Target tracking is useful in many robotics applications, such as assembly, autonomous navigation, etc. Conventional position based tracking strategies usually define the dynamics models for either motion control on tracker or position prediction on target, making the system inflexible and difficult to implement. This paper presents a new target tracking strategy, in which the dynamics model parameters are online updated according to the limited observation of the target and tracker relation. The grey theory is used in the position prediction of the target, which predicts the future trend based on the previous sensory measurements. We also use the look-ahead fuzzy logic for motion control of the tracker. Experimental results for tracking different moving targets are accomplished by the Chung Cheng-1 mobile robot used in our laboratory. The results demonstrate the robustness and flexibility of the system.

This paper proposes a collaborative transmit resource scheduling and waveform selection (CTRSWS) strategy for target tracking in multistatic radar system which consists of one transmitter and an arbitrary number of receivers. The main mechanism of the proposed CTRSWS strategy is to exploit the optimisation technique to jointly optimise the illumination power, dwell time, waveform bandwidth, and pulse length under the constraints of several resource budgets and the predefined waveform library, aiming at improving the low probability of intercept (LPI) performance and target tracking accuracy of multistatic radar system simultaneously. The analytical expressions for the probability of intercept and the trace of the predicted error covariance matrix corresponding to the target state estimation are derived and adopted to evaluate the LPI performance and target tracking accuracy, respectively. Subsequently, the resulting non-convex and non-linear optimisation problem is resolved by an efficient and fast four-stage solution methodology. Several numerical results are provided to verify the effectiveness and superiority of the proposed CTRSWS scheme in terms of the achievable LPI performance and target tracking accuracy of multistatic radar system.

We propose a methodology for learning and using a multiple-goal probabilistic motion model within a particle filter-based target tracking on video streams. In a set of training video sequences, we first extract the locations (coined as “goals”) where the pedestrians either leave the scene or often change directions. Then, we learn one motion prior model per detected goal. Each of these models is learned statistically based on the local motion observed by the camera during the training phase. Given that the initial, empirical distribution may be incomplete or noisy, we regularize it in a second phase. These priors are then used in an Interactive Multiple Model (IMM) scheme for target tracking and goal estimation. We demonstrate the relevance of this methodology with tracking experiments and comparisons done on standard datasets.

The target tracking is an fundamental ability for many oceanic applications, such as ocean surveillance, underwater rescue and etc. Due to the good mobility of autonomous underwater vehicles (AUVs), they are the ideal devices to carry out the target tracking. However, their position accuracy severely affect the target tracking performance. Thus, in this paper, aiming at the problem of target tracking using multiple AUVs, we introduce two AUV cooperative localization and target tracking schemes. The two schemes are designed based on belief propagation and carry out the localization and tracking serially and in parallel, respectively. The features and differences of both schemes are compared and analyzed in detail. Moreover, their application prospects in underwater scenarios are discussed and further validated through simulations. As a result, two schemes have their own advantages in different situations and are suitable for multiple-AUV applications.

Energy-efficiency in target tracking applications has been extensively studied in the literature of Wireless Sensor Networks (WSN). However, there is little work which has been done to survey and summarize this effort. In this paper, we address the lack of these studies by giving an up-to-date State-of-the-Art of the most important energy-efficient target tracking schemes. We propose a novel classification of schemes that are based on the interaction between the communication subsystem and the sensing subsystem on a single sensor node. We are interested in collaborative target tracking instead of single-node tracking. In fact, WSNs are often of a dense nature, and redundant data that can be received from multiple sensors help at improving tracking accuracy and reducing energy consumption by using limited sensing and communication ranges. We show that energy-efficiency in a collaborative WSN-based target tracking scheme can be achieved via two classes of methods: sensing-related methods and communication-related methods. We illustrate both of them with several examples. We show also that these two classes can be related to each other via a prediction algorithm to optimize communication and sensing operations. By self-organizing the WSN in trees and/or clusters, and selecting for activation the most appropriate nodes that handle the tracking task, the tracking algorithm can reduce the energy consumption at the communication and the sensing layers. Thereby, network parameters (sampling rate, wakeup period, cluster size, tree depth, etc.) are adapted to the dynamic of the target (position, velocity, direction, etc.). In addition to this general classification, we discuss also a special classification of some protocols that put specific assumptions on the target nature and/or use a "non-standard" hardware to do sensing. At the end, we conduct a theoretic comparison between all these schemes in terms of objectives and mechanisms. Finally, we give some recommendations that help at designing a WSN-based energy efficient target tracking scheme.