Herein, the problem of target tracking in wireless sensor networks (WSNs) is investigated in the presence of Byzantine attacks. More specifically, we analyze the impact of Byzantine attacks on the performance of a tracking system. First, under the condition of jointly estimating the target state and the attack parameters, the posterior Cramer-Rao lower bound (PCRLB) is calculated. Then, from the perspective of attackers, we define the optimal Byzantine attack and theoretically find a way to achieve such an attack with minimal cost. When the attacked nodes are correctly identified by the fusion center (FC), we further define the suboptimal Byzantine attack and also find a way to realize such an attack. Finally, in order to alleviate the negative impact of attackers on the system performance, a modified sampling importance resampling (SIR) filter is proposed. Simulation results show that the tracking results of the modified SIR filter can be close to the true trajectory of the moving target. In addition, when the quantization level increases, both the security performance and the estimation performance of the tracking system are improved.

针对目前煤矿顶板压力检测手段存在工作量大、效率低的问题,设计了一种基于STM32的无线数据采集仪.重点介绍了仪器的构成、硬件设计及软件设计.该数据采集仪通过无线通信方式进行数据采集,有效的减少了线缆铺设及维护的工作量,极大的提高了工作效率.

The work aims to solve the problems that the positioning error of industrial robots is large, and the precision of the traditional multidimensional scale (MDS) model is affected by the environmental noise which cannot meet the requirement of positioning accuracy.A solution to attenuate noise effects to MDS by combining MDS with a Kalman filter was proposed.A model was built to predict the noise distribution with regard to additive noises to the distance measurements following the Gaussian distribution.From that, a linear tracking system was developed.The experimental data showed that, under the different noise level and sensor network node numbers, the location error based on MDS method is close to20％, and changes in the 7.46％～13.13％based on EKL, and always lower than8％based on multidimensional scaling coupled Kalman filtering.Compared with the current locating algorithms (MDS and EKL), the proposed algorithm had a higher positioning accuracy under different noise level and node number.It had a good potential to solve the positioning of mobile sensors.Besides, the linear filter is simplified, therefore it suits small and embedded sensors equipped with limited power, memory, and computational capacities well, which has certain reference value in the field of robot positioning.

underwater wireless sensor networks (UWSNs) can provide a promising solution to underwater target tracking. However, due to the limited energy and bandwidth resources of UWSNs, only a small part of nodes are allowed to track underwater target at each time step. As far as we know, almost all the existing node selection schemes assume the communication channel between a node and the fusion center is perfect. However, such assumption is hard to satisfy due to the complex underwater environment. Based on the above consideration, this paper study the effect of the imperfect channel on target tracking based on UWSNs. To select the best nodes to track the underwater target, the Posterior Cramer-Rao Lower Bound (PCRLB) considering the imperfect channel is derived. Then a new target tracking scheme considering the imperfect channel is also designed. Finally, a sea experiment is conducted and collected data can verify the validity of our algorithm

As for the problem of combinatorial explosion in node selection for target tracking based on underwater wireless sensor networks,low-complexity node selection problem is studied in this paper.At first,the PCRLB under the condition of the quantized measurements is derived,which can provide the criterion for node selection.Then,a low-complexity node selection scheme is designed by combining the GBFOS algorithm,the greedy algorithm,and the derived PCRLB.Simulation results show that GBFOS and greedy algorithm can greatly decrease the computational complexity while keeping good tracking performance and thus two algorithms are well suited to solving the node selection problem in dense network.Furthermore,the GBFOS is also applied to the imperfect channel case.Simulation results show that tracking performance can be improved by considering the effect of the imperfect communication channel.

Since underwater nodes provide measurements for target tracking based on underwater wireless sensor networks (UWSNs), target-node geometry (T-NG) may affect the performance of target tracking. This paper studies T-NG effect on target tracking in UWSNs using quantized measurements. In order to evaluate the arbitrary T-NG, the relation between posterior Cramer-Rao lower bound (PCRLB) and node's position is derived. In general, an exhaustive search is required to find the optimal T-NG by minimizing PCRI.B, which is computationally prohibitive and not realistic for real-time online implementation. To decrease computational complexity while keeping proper tracking accuracy, this paper utilizes the generalized Brciman, Friedman, Olshen, and Stone (GBFOS) algorithm, the greedy search, and the random scheme to select the best T-NG. Their performance is compared with the exhaustive search. Although the random scheme is the fastest, its tracking error is too large. The greedy search and GBFOS can decrease computational load greatly while keeping almost the same tracking accuracy as that of the exhaustive search. Simulations are carried out in terms of the dense network and the sparse network, and the results can demonstrate the effectiveness of the greedy search and GBFOS, especially when the network is relatively dense.

As a result of multipath interference, tracking a low-altitude target always leads to an error which cannot be ignorable. Besides the direct reflection from the target, there are three alternative paths via the sea-surface. Under this circumstance, the conventional measurement of monopulse radar (using the real part of amplitude radio) will not represent the angle between the target and the boresight accurately. In this paper, C 2 algorithm is applied to process data from the sum and difference channels in order to measure the elevation angle. In some practical scenarios, C 2 algorithm may have an unsatisfied error. The reason of the error in C 2 algorithm and the method of reducing the error will be discussed in this paper. The results will show the improvement is feasible and effective in monopulse radar low-altitude measurement.

Two kinds of determined path (SCAN track and HILBERT track)were studied separately.It’s shown by the Enthought Canopy software simulation results that any definite beacon moving path which cov-ers the entire region has a distinct advantage over the random moving path.When the node network area is traversed by moves beacon with the communication range which is less than the resolution,the SCAN path has a lower localization error.When the track is greater than the resolution of communication range,the HILBERT trajectory provides a higher accuracy significantly.

Traditional sonar-array-based target tracking algorithms may be unsuitable for on-demand tracking missions, since they assume that the sonar arrays should be towed or mounted by a submarine or a ship. Alternatively, underwater wireless sensor networks can offer a promising solution approach. First, each underwater node is battery-powered, so saving energy expenditure is a critical issue. Instead of keeping all sensor nodes active, this paper provides a local node selection (LNS) scheme which increases energy efficiency by waking up only a small part of nodes at each time. Second, considering node's limited computing ability and the real-time requirement for the tracking algorithm, instead of employing the centralised fusion structure, we utilise the distributed Kalman filtering fusion with feedback in this paper. Finally, instead of assuming one sensor node can uniquely determine target's location, a more practical range-only measurement model is proposed. Then the LNS scheme and distributed fusion with feedback are extended to our range-only measurement model. The simulation results demonstrate the efficiency of our scheme.

For the locating problem of trench mortar,a locating method based onα-β-γfilter is presented. First,centroid moving equations are determined according to the exterior ballistics. Then the motion param-eters are solved by theα-β-γtracking filter with constant gain. Finally, the trench mortar position is de-duced by solving the centroid moving equations with the Runge-Kutta method. The simulation results show that the proposed method has good locating precision with much shorter time compared with conventional method,and it is valuable for engineering.

On one hand, due to the energy and bandwidth constraint of underwater wireless sensor networks (UWSNs), local data quantization/compression is not only a necessity, but also an integral part of the design of UWSNs; on the other hand, since underwater nodes provide measurements for target tracking based on UWSNs, node topology, which is made up of the underwater nodes, may affect the performance of target tracking. This paper studies the effect of node topology on the target tracking in UWSNs using quantized measurements. Firstly, by using the knowledge of geometry, the effects of four typical topologies on target tracking using quantized measurements are analyzed qualitatively. The four typical topologies include two nodes are close to each other, three nodes are close to each other, three nodes are co-linear, and three nodes form a regular triangle. Secondly, under the condition of quantized measurements, the relationship between the posterior Cramer-Rao lower bound (PCRLB) and node's position is derived to evaluate the arbitrary topology. Thirdly, our target tracking scheme consisting of the optimal topology selection scheme by minimizing PCRLB, the optimal fusion center selection scheme by minimizing energy consumption, and the multisensor particle filter with quantized measurements is designed. Last, simulation results show the effectiveness of the proposed scheme.

In order to increase the localization coverage while keeping the localization error small in a unique network architecture in which there are not evenly distributed anchor nodes with great ability of communication or additional infrastructure, a Top-down Positioning Scheme (TPS) for underwater acoustic sensor networks is proposed. By defining node’s confidence reasonably, TPS insures the quality of the new reference nodes. TPS also refines the nodes which have just been positioned via the gradient method and helps non-localized nodes search for more reference nodes via the new scheme for 3D Euclidean distance estimation. By comparing the new scheme for 3D Euclidean distance estimation with the existing scheme, the new scheme is shown to have greater ability to estimate two-hop Euclidean distance in 3D space. Simulation results show that TPS which integrates node’s confidence defined reasonably, the gradient method, and the new scheme for 3D Euclidean distance estimation can increase the localization coverage ratio, while keeping the localization error small.

Since underwater nodes provide measurements for target tracking based on underwater wireless sensor networks (UWSNs), node topology, which is made up of the underwater nodes, may affect the performance of target tracking. But all the existing target tracking schemes do not consider this effect. This paper studies the effect of node topology on the target tracking in UWSNs. Firstly, by using the knowledge of geometry, the effects of four typical topologies on target tracking based on UWSNs are analyzed qualitatively. The four typical topologies include four nodes form a square, four nodes are in line, four nodes are close to each other, and four nodes form a regular tetrahedron. Secondly, to evaluate the arbitrary topology, the relationship between the posterior Cramer-Rao lower bound (PCRLB) and node's position is derived. Thirdly, our target tracking scheme consisting of the optimal topology selection scheme by minimizing PCRLB, the optimal fusion center selection scheme by minimizing energy consumption, and the multi-sensor particle filter (PF) is designed. Last, simulation results show the effectiveness of the proposed scheme.

A reverse localization scheme (RLS) assumes that node's communication ability is so powerful that it can communicate with surface anchor nodes directly. However, this assumption is hard to be satisfied when the monitoring area is deep. In order to overcome RLS's drawback, this paper proposes a multi-hop reverse localization scheme (MRLS) for underwater wireless sensor networks (UWSNs). For MRLS, the routing problem and the localization problem have been solved together. Simulation results show than MRLS achieves much larger localization coverage than RLS while keeping a proper level of localization error and the communication overhead in deep water environment and MRLS's performance is not worse than RLS in shallow water environment.

According the characteristic of wireless sensor net works (WSN), it can be used in warehouse monitoring. However, there are problems in localiza tion. Existing RSSI-based approaches rely on absolute RSSI values to estimate physical distances . However, RSSI has a lager variation because its fading, shadowing and reflections. By comparing RSSI values of the mobile beacon, we propose an improved RSSI-based approach which can achieve higher accuracy. In this paper, RSSI localization is introduced firstly. Second, we propose a localizati on algorithm based on the comparison of the mobile beacon node's RSSI. Third, we analyze the algorithm with simulation experiment. Finally, we draw conclusion

Almost all the existing range-based localization schemes for underwater wireless sensor networks (UWSNs) assume that clocks of different nodes are well synchronized. However, clock synchronization is very challenging in UWSNs. In this paper, we design a clock synchronization independent localization scheme (CSILS). Instead of trying to estimate distances between unknown nodes and anchor nodes, we design a scheme for obtaining the distance differences based on the local clocks. In order to convert the distance differences into node’s location, we propose distancedifference-based maximum likelihood estimation (D-D-BMLE). Finally, we give some suggestions on how to deploy anchor nodes Simulation results show that CSILS works well without clock synchronization when anchor nodes are deployed reasonably. Compared to a clock-synchronization-based localization scheme, CSILS improves localization accuracy with a proper level of localization coverage and communication overhead in terms of mobility model of water currents.

Passive location technology has been applied broadly these years.Taylor expansion method was commonly used by the traditional multi-station passive location system.A new method of time difference of arrival（TDOA） passive location with multi-station based on weighted matrix was presented.When the stable carrier being included in a target＇s emission signal,the noise characteristics equations was analyzed using the conventional time-domain correlation method after the carrier was tracked.The model was constructed with the weighted matrix of the distance difference observations among a number of stations,thus the high accuracy location being obtained by employing the weighted matrix.From the simulation,it can be proved that under the same condition,the position results using presented method are more accurate than the traditional Taylor expansion method,improved by about 30% in best cases.This method is an effective method for multi-station passive location,particularly for mobile observation stations.

Abstract： Due to the limited energy and bandwidth resources of Underwater Wireless Sensor Networks (UWSNs), only a small part of nodes are allowed to track underwater target at each time step. As far as we know, almost all the existing node selection schemes assume the true locations of nodes are known. However, such assumption is hard to satisfy due to the mobility of the nodes. This paper presents a new node selection scheme which only depends on the estimated locations of nodes rather than the true ones. Firstly, the uncertainty of node's location is approximated the additional measurement noise via the first-order Taylor series expansion. Then, in order to select nodes under node's location uncertainty, the relation between posterior Cramer-Rao lower bound (PCRLB) and estimated locations of nodes is derived. Then, our tracking scheme which consists of multi-sensor particle filter (PF) and the new node selection scheme is designed to track the underwater target under the uncertainty of node's location. Finally, a simulation is presented to illustrate the tracking improvements obtained by estimating target's state using our scheme.