Autonomous Sensor Manager Agents (ASMA)

Transcription

1 Autonomous Sensor Manager Agents (ASMA) Lisa Ann Osadciw Department of Electrical Engineering and Computer Science Syracuse University Syracuse, NY (315) (office)/(315) (fax) ABSTRACT Autonomous sensor manager agents are presented as an algorithm to perform sensor management within a multisensor fusion network. The design of the hybrid ant system/particle swarm agents is described in detail with some insight into their performance. Although the algorithm is designed for the general sensor management problem, a simulation example involving 2 radar systems is presented. Algorithmic parameters are determined by the size of the region covered by the sensor network, the number of sensors, and the number of parameters to be selected. With straight forward modifications, this algorithm can be adapted for most sensor management problems. Keywords: sensor fusion, sensor management, swarms, independent agents 1. INTRODUCTION This paper describes the design of autonomous sensor manager agents that when combined become a sensor manager, which is able to react to the dynamic characteristics of the environment and sensors. This bottom up approach for sensor adaptation is based on swarm algorithms. The hybrid ant system/particle swarm agents can be easily adapted to any sensor management problem. The general sensor management problem involving multiple sensors consists of the observation space, which surrounds the sensors, and information space, which are the measurements representing the observations. Sensor performance is measured and shared between the agents. As networks are created of numerous, heterogeneous sensors, the optimizing of the global system-of-systems performance involves the agents changing the sensor s operating parameters and then reassessing performance. This problem is prevented from overwhelming the processors by distributing the processing among the multiple autonomous sensor manager agents. The outputs of these agents may determine sensor placement, replacement, and operating parameters adapting the sensor network to meet changing mission and environmental requirements. The uniqueness of this algorithm lies in its ability to autonomously react to changing mission needs, environmental conditions, or sensor status in an effective and independent manner. The multi-sensor network problem can be thought of consisting of three information spaces and four systems. A test database contains the data necessary to calibrate the systems. The four processing systems include the sensor suite, the ASMAs, the data fusion processors, and the performance monitoring processors as shown in Figure 1. The sensor suite observes the surrounding space recording measurements. Sensor status is sent to the IO-ASMA, and the measurements are used by the data fusion processor. The data fusion processor makes estimates of events by combining measurements from multiple sensors and storing the information. The performance monitor assesses the quality of the estimates and communications determine the status of the sensor network. Finally, the ASMA processor manages the sensors and network.

2 Performance Monitoring Sensor Status Controls ASMA Information Sensors Measurements Data Fusion Process Estimates Observation Space Information Space Test Database Fig. 1.Sensor Managment Problem The ASMAs are designed for the general sensor management problem and can be easily modified for any type of sensor network. A scalable and very adaptable sensor management algorithm emerges from these ASMAs. As the number of sensors and their complexity increases, the sensor management algorithm only requires new sensor models and more agents. The sensor manager agents then make sensor operating parameter decisions based on the sensors performance in the network to achieve a goal. Thus, the operator s job has evolved from making all the sensor operating parameter decisions to simply setting the goal. This paper describes agents designed to control a suite of intelligent surveillance radar systems covering hundreds of nautical miles. This provides a focus for this discussion and supports quantitative results. 2. ALGORITHM DESCRIPTION The ASMAs and supporting system primarily consists of 6 functional blocks with the communications handled through a 7th block in terms of performance monitoring. The other sensor managers may or may not be ASMA systems as long as the interfaces are similar to the ASMA system. The performance monitoring block triggers the ASMAs into action when it computes a change in the performance parameters described in the next section. The operator is left to decide if system s goals must change as a result of detected events. The ASMAs try to eliminate the performance change by re optimizing performance through a different selection of sensor operating parameters. Figure 2 contains a functional block diagram of the IO-ASM system. The next section gives some background information on swarms. This is followed by a discussion of the performance parameters and then a detailed description of the ASMAs.

3 Networked IO-ASM Systems Sensor Database Performance Montioring Performance parameters Sensor Models Operators Weights and Optimization Function Autonomous Sensor Manager Agents Sensor Interface ASMA System Sensor Control Fig. 2.ASMA Functional Diagram 2.1 Background Research into general sensor management strategies that are not single point, ad hoc solutions began in the late 1980s [ ]. Hintz noted a lack of effective sensor control algorithms and proposed a blending of mathematical techniques to build a cohesive sensor management algorithm. The focus up until that point was on formulated information theoretic metrics by which to manage the sensors and optimize target detection, estimation, and classification [4-9]. SWARM is a distributed optimization algorithm that is very useful in multi-dimensional problems. It is inspired by swarming organisms such as ants in nature, slime mold, termites, and even in the formation of city neighborhoods [ , 16]. Ants and termites comprise 30% of the Amazonian rain forest with 10,000 species [16]. These swarm species have meager vocabularies based on pheromones and have minimal cognitive skills. These organisms display emergent intelligence by creating higher level sophistication based on low level rules. Harvester ant colonies prioritize food source based shortness of route and ease of access. In slime mold, cell aggregation is controlled by communication between cells and not pacemaker cells altering the amounts of cyclic AMP as Harvard s B.M. Shafer proposed in a 1962 paper. Keller and Segel argued that slime mold communicates rather than using pacemaker cells in a 1969 paper [16]. Ants colonies forage and build nests. City neighborhoods, as discovered by Jane Jacobs, follow swarm algorithms through the development of class lines [16]. There are now many popular computer games based loosely on Keller s and Segel s slime mold equations of 30 years ago [16]. Bottom-up software has been designed to organize lively virtual communities. All these systems solve problems by drawing on masses of somewhat stupid elements rather than relying on a single, very intelligent node. These are bottom-up systems displaying emergent behavior.

4 Other algorithms that demonstrate emergent behavior include simulated annealing, genetic algorithms, and evolutionary computation. The distributed, complex, and dynamic nature of the sensor management problem lends itself to the emergent intelligence nature of these multi-agent approaches. The swarm algorithms, in particular the Particle Swarm Optimization algorithm (PSO), was originally introduced in terms of social and cognitive behavior by Kennedy and Eberhart in 1995 [ ]. This approach has come to be widely used as a problem-solving method in engineering and computer science because it has been proven to be a powerful competitor to other evolutionary algorithms. A hybrid swarm algorithm has been the approach chosen in this project as a result of reviewing the work of several researchers. Eberhart demonstrated the effectiveness of swarm algorithms in comparison to genetic algorithms in benchmark tests in [ 22 ]. Several other researchers have analyzed the performance of the PSO and its variants with different settings, e.g., neighborhood settings [ 20 ], hybrid PSO with breeding and sub populations [ 21 ]. Work presented in [ 22 ] describes the complex task of parameter selection in a PSO model. Comparisons between PSO and the standard GA were done analytically and also with regards to performance in [ 23 ]. The swarm approach differs from other multi-agent approaches by the fact that agents are not eliminated and the motion synchronizes as the evolution progresses [ 9 ]. The swarm thinks and acts locally but its collective behavior results in global patterns. Emergent behavior sensors follow local performance rules and will create higher-level behavior better suited for the current environment as the sensors communicate their performance to one another. This emergent complexity grows smarter over time and responds to the specific and changing needs of the situation and environment. These differences make the swarm approach more effective than other emergent behavior algorithms. The sensor manager uses a hybrid Ant System-Particle Swarm Optimization, AS-PSO, algorithm by combining the two basic types: particle swarm and ant systems. This supports a more stable evolutionary programming solution. The ant system is responsible for combining the sensors and templates. Then the particle swarm searches for points in the defined regions that do not meet performance requirements. The performance requirements and optimization function are received from the mission manager. 2.2 Global Performance Parameters The global sensor suite performance measures must be meaningful to both the scenario and sensors. One of the unique characteristics of this algorithm is its ability to relate a single total performance measure to a set of goals developed by the operator. The operator defines the performance value as a weighted sum of multiple global performance parameters. This approach generates one dynamic and adaptive performance parameter that can support comparisons across scenarios and sensor networks. The global performance parameters are functions of single sensor performance. Performance monitoring consists of computing the total global performance from measuring current single sensor performance. ASMAs choose sensor parameters by predicting the total global performance from single sensor performance predictions from sensor models. The total global performance includes a required value for each global parameter as well as a weighting. This allows different types of performance values to be made unitless and hence comparable. The weights are used by the operator to establish performance parameter priority. The weights must sum to 1 or where N is the number of global performance parameters and The global performance is computed from N W k = 1 k = 1 W k (1) is the weighting corresponding to the kth parameter.

5 ( actual required ) N i Pglobal = W ψ ψ i i= 1 ψrequired i where ψ i are global performance parameters and W i is the weighting. The actual values in (2) are either computed from collected measurements in performance monitoring or the sensor models in ASMAs. For the ground based sensor scenario, the total global performance consists of two global performance parameters: target detection and measurement accuracy. For the system discussed in this paper, global detection is based on 1 radar return out of 2 possible returns. The accuracy is the cumulative accuracy of the two radars, which happen to not be colocated. Since many more parameters can be added with the appropriate models and performance monitoring, this approach to measuring performance scales well and supports comparisons between sensor suites and scenarios. i (2) Ant System Sensor 1 Sensor 2 Sensor 3 Sensor Parameter Sets Particle Swarm P global V 3D Optimization Space Fig. 3.Picture Illustrating the Hybrid Ant System/ Swarm 2.3 Detailed Algorithm Description This hybrid swarm algorithm is composed of two types of swarm agents: Sensor Operating Point Agent (SOP) and Locate Minimum Likelihood Agent (LML). The agent design reflects the search space as Figure 3 illustrates. The SOP agents, at the top of Figure 3, are designed based on the ant system, and LML agents, on the bottom of Figure 3, are based on particle swarm agents. The SOP agents must search a mixture of discrete and continuous sensor operating parameters. The LML agents search a 3 dimensional, continuous space.

6 The processing performed by this algorithm is best illustrated in the diagram given in Figure 5. The first step is to initialize all the agents. There are M SOP agents as explained at the bottom of Figure 5. Each SOP agent has P LML agents linked to it. Thus, there are MP total LML agents. The SOP agents randomly select a complete set of sensor operating parameters. Each node illustrated in the top half of Figure 2 represents a sensor s single operating parameter set. The SOP agents deposit pheromones along the paths connecting the nodes indicating good selections as time progresses. Initially, all paths have an equal amount of pheromone or τ ik = 1 (3) where i is the path s initial sensor operating parameter set and k is where the path terminates. The probability of selecting path ik is the transition probability or where α is a parameter affecting convergence and τ ij is the pheromone for path ik. Initially, the transition probabilities are M for every node. Π ik = α τ ik M α τ ij j = 1 At first, the LML agents are randomly placed in the 3 dimensional region. Each SOP agent is associated with P LML agents. Each LML agent moves around the space searching for the location of the minimum global performance in the 3-dimensional continuous space. The ith agent is represented by its location in space or X i = ( x i1, x i2, x i3 ) [23]. These agents will move around the space using a 3D velocity vector, V i = ( v i1, v i2, v i3 ), that is based on minimizing the total global performance function, (2). This movement through the space is sometimes referred to as flying with a velocity,. The term velocity is based on considering each iteration an increment in time. V i (4) Each LML agent must maintain a memory of its previous best position, δλ i = ( λ i1, λ i2, λ i3 ) [23]. In each generation, the velocity of each LML agent is updated with an attraction force towards its own previous best position ( Λ i ) and the best position ( Λ g = ( λ g1, λ g2, λ g3 ) ) discovered among all the LML agents. The portion of the adjustment to the velocity influenced by the individual s previous best position is controlled by a uniform random variable between 0 and 1, ψ 1, and the portion influenced by the global best is a second uniform random variable between 0 and 1, ψ 2. The formula for updating the velocity in all dimensions is ( t + 1) () t () t V id = ω V id + 2ψ 1 ( λ id X id ) + 2ψ 2 ( λ gd X id ) for d=1,2,3 and iteration i. The position is updated using (t ) (5) ( t + 1) X id () t X id = + The next update is represented by the integer t+1. A constant, ( t + 1) V id V max. (6), is used to limit the velocities of the LML agents.

7 If the minimum location or performance value for all the LML agents and corresponding SOP agent has been found, new sensor operating parameter combinations are selected by the SOP agents using the transition probabilities associated with the paths in Figure 3. Before choosing new combinations of sensors and parameter sets, the transition probabilities for the paths corresponding to the previous combinations must be updated as shown in Figure 5. The SOP agent s global minimum, P global, is used to compute a change in pheromone level for the agent s path by τ = QP global where Q is an arbitrary constant. The pheromone is updated by (7) ρ ( t + 1) τ ik = () t ρτ ik + τ ik where is the constant representing memory between 0 and 1. Then the path s transition probability in (4) is updated using the new pheromone from (8). The next section contains an overview of the simulation software. (8) 3. SIMULATION AND RESULTS The purpose of this section is to provide some insight into the parameter selection for the autonomous sensor manager agents. A Matlab simulation executing the steps in Figure 5 for a 2 degree by 2 degree region in New York State. Two large radars that are not collocated cover this region. The SOP agents are allowed to choose from 6 parameter sets to achieve the selected cumulative detection and accuracy measure for the entire region. The power and sensitivity gradually increases as the parameter set index increases. There can be no holes in the region that do not meet the minimum performance level selected. The algorithm constants affect the speed of convergence. The specification of high global performance values actually speeds convergence because the LML agents look for holes in the coverage. If there are many holes, the algorithm converges rapidly. For a reliable convergence rate, the memory parameter, ρ, in (8) is found to be.8. The pheromone values converge to 0 as the number of iterations increase from 10 to 100 so an initial Q value of 1 in (7) is suggested. The α in (4) is recommended to be 3. Smaller values slow convergence and higher values stop the searching too quickly. The size of the search space and number of sensor parameter sets directly affects convergence. The agent parameters, number of LML agents per SOP agent, number of SOP agents, and finally number of iterations control whether optimum pararmeters are found or not. A parameter set will always be selected by the SOP agents but the algorithm may converge quickly to the wrong set or not converge at all. The parameters previously specified match the 2 by 2 degree region. The optimum number of iterations seems to be 500 to assure convergence by comparing values in Table 5 and Figure 4, which plots the minimum total global performance as a function of the iteration. In Table 5, a numerical comparison can be made between the optimum SOP agents. All columns have chosen a different optimum set of sensor parameters but performance reliably stabilizes after 500 iterations. The best indication that the algorithm has converged is the transition probabilities. The algorithm settles on the optimum set for sensor 1 first and then proceeds to search for sensor 2 s optimum parameter set. The algorithm is able to meet the specified requirements without using the parameter sets corresponding to the maximum power and sensitivity. 4. FUTURE WORK This paper has demonstrated the effectiveness of this algorithm to select parameter sets to meet performance goals. The algorithms can be improved in the area of efficiency as well as increasing the types of sensors it models. The impact of increasing the complexity of the problem needs to be analyzed in more depth. Parameter selection as the types

8 of sensors increases also requires analysis. The particle swarm algorithm can also be replaced with more efficient swarm algorithms. Finally, testing real-time software on a sensor network needs to be done. Table 5.Numerical Comparison Between Optimum Agents Number of Runs Total Global Performance (Optimum = 0) Sensor Index: Set Index: Selection Probability: Pheromone Level: Sensor Index: Set Index: Selection Probability: Pheromone Level: Iterations = 50 Iterations = 200 Iterations = e e Fig. 4.Minimum Global Performance After 10 Iterations

9 Initialize all combinations to equal probability Randomly scatter PLML* Agents for each SOP** Agent Choose new SOP Agent paths Compute Likelihoods for MxP agents Normalize Likelihoods Compute regional objective function Compute velocity Update ant likelihood and global likelihood Move all agent positions Yes try again? No Update transition probabilty Yes try new ant combinations? No stop Fig. 5.Autonomous Sensor Manager Agent Processing *LML - Locate Minimum Likelihood Agent (P) **SOP - Sensor Operating Point Agent (M)

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