Information-sharing and decision-making in networks of radiation detectors

A network of sensors observes a time-inhomo-geneous Poisson signal and within a fixed time interval has to decide between two hypotheses regarding the signal’s intensity. The paper reveals an interplay between network topology, essentially determining the quantity of information available to different sensors, and the quality of individual sensor information as captured by the sensor’s likelihood ratio. Armed with analytic expressions of bounds on the error probabilities associated with the binary hypothesis test regarding the intensity of the observed signal, the insight into the interplay between sensor communication and data quality helps in deciding which sensor is better positioned to make a decision on behalf of the network, and links the analysis to network centrality concepts. The analysis is illustrated on networked radiation detection examples, first in simulation and then on cases utilizing field measurement data available through a U.S. Domestic Nuclear Detection Office (dndo) database.     

Controlled tracking in urban terrain: Closing the loop

We investigate the challenging problem of integrating detection, signal processing, target tracking, and adaptive waveform scheduling with lookahead in urban terrain. We propose a closed‐loop active sensing system to address this problem by exploiting three distinct levels of diversity: (1) spatial diversity through the use of coordinated multistatic radars; (2) waveform diversity by adaptively scheduling the transmitted waveform; and (3) motion model diversity by using a bank of parallel filters matched to different motion models. Specifically, at every radar scan, the waveform that yields the minimum trace of the one‐step‐ahead error covariance matrix is transmitted; the received signal goes through a matched‐filter, and curve fitting is used to extract range and range‐rate measurements that feed the LMIPDA‐VSIMM algorithm for data association and filtering. Monte Carlo simulations demonstrate the effectiveness of the proposed system in an urban scenario contaminated by dense and uneven clutter, strong multipath, and limited line‐of‐sight.     

Including probabilistic target detection attributes into map representations

Range measuring sensors can play an extremely important role in robot navigation. All range measuring devices rely on a ‘detection criterion’ made in the presence of noise, to determine when the transmitted signal is considered detected and hence a range reading is obtained. In commonly used sensors, such as laser range finders and polaroid sonars, the criterion under which successful detection is assumed, is kept hidden from the user. However, ‘detection decisions’ on the presence of noise still take place within the sensor. This paper integrates signal detection probabilities into the map building process which provides the most accurate interpretation of such sensor data. To facilitate range detection analysis, map building with a frequency modulated continuous wave millimetre wave radar (FMCW MMWR), which is able to provide complete received power-range spectra for multiple targets down range is considered. This allows user intervention in the detection process and although not directly applicable to the commonly used ‘black-box’ type range sensors, provides insight as to how not only range values, but received signal strength values should be incorporated into the map building process.This paper presents two separate methods of map building with sensors which return both range and received signal power information. The first is an algorithm which uses received signal-to-noise power to make an estimates of the range to multiple targets down range, without any signal distribution assumptions. We refer to this as feature detection based on target presence probability (TPP). In contrast to the first method, the second method does use assumptions on the statistics of the signal in target presence and absence scenarios to formulate a probabilistic likelihood detector. This allows for an increased rate of convergence to ground truth. Evidence theory is then introduced to model and update successive observations in a recursive fashion. Both methods are then compared using real MMWR data sets from indoor and outdoor experiments.     

Nonlinear filtering in target tracking using cooperative mobile sensors

Collaborative signal processing and sensor deployment have been among the most important research tasks in target tracking using networked sensors. In this paper, the mathematical model is formulated for single target tracking using mobile nonlinear scalar range sensors. Then a sensor deployment strategy is proposed for the mobile sensors and a nonlinear convergent filter is built to estimate the trajectory of the target.     

Coordination of multiple mobile robots with limited communication range in pursuit of single mobile target in cluttered environment

This paper addresses the problem of coordinating multiple mobile robots in searching for and capturing a mobile target, with the aim of reducing the capture time. Compared with the previous algorithms, we assume that the target can be detected by any robot and captured successfully by two or more robots. In this paper, we assume that each robot has a limited communication range. We maintain the robots within a mobile network to guarantee the successful capture. In addition, the motion of the target is modeled and incorporated into directing the motion of the robots to reduce the capture time. A coordination algorithm considering both aspects is proposed. This algorithm can greatly reduce the expected time of capturing the mobile target. Finally, we validate the algorithm by the simulations and experiments.     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

Information-theoretic bounds on target recognition performancebased on degraded image data

This paper derives bounds on the performance of statistical object recognition systems, wherein an image of a target is observed by a remote sensor. Detection and recognition problems are modeled as composite hypothesis testing problems involving nuisance parameters. We develop information-theoretic performance bounds on target recognition based on statistical models for sensors and data, and examine conditions under which these bounds are tight. In particular, we examine the validity of asymptotic approximations to probability of error in such imaging problems. Problems involving Gaussian, Poisson, and multiplicative noise, and random pixel deletions are considered, as well as least-favorable Gaussian clutter. A sixth application involving compressed sensor image data is considered in some detail. This study provides a systematic and computationally attractive framework for analytically characterizing target recognition performance under complicated, non-Gaussian models and optimizing system parameters     

Path planning in GPS-denied environments via collective intelligence of distributed sensor networks

This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin’s car-like robot.     

扩展目标跟踪的PCRLB

在杂波和漏检的环境下针对扩展目标估计的性能评价问题, 提出求扩展目标跟踪估计的后验克拉美罗界(PCRLB) 的方法. 该方法假设单个扩展目标的量测个数服从泊松分布, 杂波的个数也服从泊松分布, 给出在有漏检和杂波环境下求PCRLB 的方法—–信息约减因子法(IRF). 算例展示了不同的检测概率和杂波密度对PCRLB 的影响. 仿真结果表明, 所提出方法能有效反映扩展目标跟踪所能达到的最优性能, 检测概率越高, PCRLB 越小; 杂波密度越大, PCRLB 越大.

     

Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric

Mobile sensors cover more area over a fixed period of time than do the same number of stationary sensors. However, the quality of coverage (QoC) achieved by mobile sensors depends on the velocity, mobility pattern, number of mobile sensors deployed, and the dynamics of the phenomenon being sensed. The gains attained by mobile sensors over static sensors and the optimal motion strategies for mobile sensors are not well understood. In this paper, we consider the following event capture problem: the events of interest arrive at certain points in the sensor field and disappear according to known arrival and departure time distributions. An event is said to be captured if it is sensed by one of the mobile sensors before it fades away. We analyze how the QoC scales with velocity, path, and number of mobile sensors. We characterize cases where the deployment of mobile sensors has no advantage over static sensors, and find the optimal velocity pattern that a mobile sensor should adopt. We also present algorithms for two motion planning problems: 1) for a single sensor, what is the sensor trajectory and the minimum speed required to satisfy a bound on the event loss probability and 2) for sensors with fixed speed, what is the minimum number of sensors required to satisfy a bound on the event loss probability. When the robots are restricted to move along a line or a closed curve, our algorithms return the optimal velocity for the minimum velocity problem. For the minimum sensor problem, the number of sensors used is within a factor of 2 of the optimal solution. For the case where the events occur at arbitrary points on a plane, we present heuristic algorithms for the aforementioned motion planning problems and bound their performance with respect to the optimal.