Patent Application: US-45622103-A

Abstract:
a swarming agent architecture provides a distributed , decentralized , agent - based computing environment applicable to ground - based surveillance . the approach , called sensor network integration through pheromone fusion , or “ snipf ,” provides an end - to - end demonstration that integrates self - contained sensor / communication nodes with novel swarming algorithms to detect foot and vehicular movement through a monitored area with minimal configuration and maintenance . a plurality of computational nodes distributed within the environment and , depending upon the way in which they are deployed , the various nodes are operative to sense the local environment , receive a message from a neighboring node , and transmit a message to a neighboring node . given these capabilities , the nodes can collectively determine the presence and / or movement of a target and communicate this information to a user . though not required , the system may include nodes that are capable of collectively determining the speed and heading of a target , and the gathered intelligence may be communicated to users within , and external to , the environment . a particularly useful configuration may include one or more ‘ free ’ nodes having relatively limited communications and computational power , and one or more anchor nodes equipped with gps and / or long - distance communications capabilities .

Description:
as discussed in the background of the invention , snipf is based on swarm intelligence [ 1 , 3 ], a new approach to information processing that uses the interactions among processors as well as computation within processors to detect , analyze , and respond to sensory input . this approach contrasts with other approaches in common use . snipf processes sensory data in situ , not centrally , in contrast to data mining and data warehousing approaches . in contrast to conventional artificial intelligence approaches , processing is numerical , utilizing insect - inspired digital “ pheromones ,” and not symbolic implementations . spatial reasoning draws on each node &# 39 ; s location and the limited neighborhood induced by short radio range , rather than using an explicit representation of space , thus different from traditional signal processing . each node interacts with its environment in three ways . it reads its local sensors , receives messages broadcast by nearby neighbors , and broadcasts messages that can be received by nearby neighbors . such a sensor field can detect the presence of a target , localize it , determine its velocity ( speed and heading ), and communicate this information to users , both external to the sensor field and within it , using only a few thousand bytes of software and requiring only a few hundred bytes per second of bandwidth . fig1 is a block diagram of a single snipf node , the operation of which is explained in the following sections . a snipf network has two kinds of nodes . most nodes (˜ 95 %) arefree , with only limited communications and computational power . the nodes are inexpensive and they can run for a long time on small batteries . a few nodes are anchors , with gps and long - haul communications ( e . g ., satellite ) to transmit the net &# 39 ; s findings to commanders . each node has a unique identifier . both kinds of nodes are distributed randomly over the battlespace or other environment of interest ( fig2 ). their density depends on the range of the sensors deployed , their communications range , the nature of the targets , and the type of tracking required . continuous tracking in snipf depends on the target &# 39 ; s being visible to several nodes at a time . with hardware currently available , densities between { fraction ( 1 / 10 )} m 2 and { fraction ( 1 / 100 )} m 2 are appropriate , and smaller densities are possible when detecting but not tracking targets . these figures are given by way of example and not restriction ; other hardware might permit the use of a wider range of densities without departing from the essence of this invention . once on the ground ( or in water ), the nodes self - configure and construct gradients to the anchors . these activities repeat periodically ( e . g ., daily ) to account for lost nodes . to reduce message size , nodes define locally unique identifiers . in the preferred implementation , 6 bits are adequate for these identifiers . each anchor broadcasts its identity and a hop count ( initially zero ). every receiving node ( fig1 “ gradients ”) checks whether it has heard a higher hop count from the same anchor . if not , it is on the expanding front of messages , so it increments the hop count and broadcasts the modified message . each node remembers its minimum hop count from each anchor . hop counts ( fig2 ) define a gradient to route target reports to the anchor , and locate each node if there are at least three non - colinear anchors . once in place , the nodes interact in a sensing cycle whose frequency depends on the expected rate of change in the environment . for example , to track vehicles moving on the order of 30 km / h = 8 . 3 m / sec , nodes spaced on average 10 m apart need a sensing cycle of 0 . 5 sec to guarantee that they see the target when it is in their zone . for pedestrians , cycles of 10 sec suffice , while monitoring construction of a stationary facility could use cycles of several minutes or even hours . for discussion , we assume that a node &# 39 ; s sensor responds to targets in its environment by generating a signal σε [ 0 , 1 ]. σ may be a primary sensor reading , the output of a “ sensor fusion function ” that combines readings from multiple sensors , the time rate of change of a sensor instead of its absolute reading , or other derivative data that will be obvious to one skilled in the art of sensor processing . in the preferred implementation , σ is normalized to fall in the range ε [ 0 , 1 ], but the method here taught may be applied to other ranges as well . fig3 shows simulated raw sensor readings on a 20 × 20 grid . the eye can detect a bright area just ne of center where the target is located . if all sensor readings were piped to a central processor , conventional signal processing techniques could also detect this region , but such a data pipe is unrealistic under our power constraints . the node translates σ ( a real number ) to a binary detection indication , using a threshold θ that falls in the open interval corresponding to the range of σ ( in the preferred implementation , θε ( 0 , 1 )). if σ ≧ θ , the node concludes that it sees a target ; if σ & lt ; θ , it ignores the signal . fig4 shows ( in white ) nodes whose sensors individually exceed the threshold . local sensor anomalies such as noise result in an obscure picture . snipf uses neighbor corroboration ( fig1 “ detection ”). each node periodically receives a message from each nearby node indicating whether or not the neighbor senses a target . proximity among nodes makes it unlikely that only one node would see a target . a node &# 39 ; s threshold for mapping its sensor signal to a detection decision varies with the percentage of its neighbors that sense a target , according to a sigmoid function . an example of such a function is θ + δ − 2δ / 1 + e − r ( p − 0 . 5 ) , where p is the percentage of neighbors that detect the target , r adjusts steepness , and δ is the maximum deviation from the base threshold , but one skilled in the art will recognize that other sigmoid functions may be used without departing from the essence of this invention . if all neighbors detect a target , the node &# 39 ; s threshold drops to θ − δ , accepting signals that would otherwise be rejected . if no other neighbors detect a target , the threshold rises to θ + δ , rejecting readings that would otherwise trigger detection . this neighborhood interaction improves the sensitivity of the network while reducing false alarms . [ 0030 ] fig5 shows the detecting neighborhood produced by applying neighbor corroboration to the sensor readings in fig3 . it clearly identifies the bright region in the original data and suppresses spurious readings . in spite of the power of this technique , it requires each node to transmit only one bit per sensing cycle , and to receive only n bits , where n is the number of neighbors within communication range . table 2 compares snipf &# 39 ; s detection algorithm with other common data fusion architectures . once detected , a target must be located geographically . of all the nodes that detect the target , those closest to it must recognize that they are the closest . the percentage p of a node &# 39 ; s neighbors that detect the target will be higher for a node closer 20 to the target than for one far away . an “ edge node ” is one that detects the target and whose p is less than φε ( 0 , 1 ). nodes that detect the target but are not edge nodes must be one step closer to the target , and so forth . nodes already know whether they are edge nodes as a result of target detection . to locate the target , detecting nodes construct a gradient using a small integer ( four bits in the preferred implementation ) indicating their distance from the edge . a detecting node that hears no edge distance greater than its own knows that it is farthest from the edge , and thus that it is a nearest node . fig6 shows the result , with edge nodes ( edge distance 1 ) colored gray and central nodes ( edge distance 2 ) colored white . since sensor location is our main estimator of target location , precision and bias of tactical target location ( ttl ) are directly dependent on sensor density and its relation to sensor range ( that is , the number of sensors that detect a given target ). a node estimates target velocity by maintaining a short history ( ten steps in the preferred implementation ) of its edge distance and measuring how fast it changes . a node in line with the target &# 39 ; s vector sees the highest rate of change in its edge distance , while a node on the edge of the detecting range at the maximum distance from the line of movement sees the slowest . a gradient mechanism identifies the nodes of highest change . their rate of change estimates the target &# 39 ; s speed , and their position estimates its direction . the sensor net must communicate information about the presence , location , and velocity of the target to other blue units . snipf provides two mechanisms for this communication . first , messages can diffuse up gradients to the nearest anchor , whose higher - power radio can pass the information on to a remote headquarters . second , snipf can use target information locally to attract mobile resources , such as sof personnel or unpiloted robotic vehicles . the mobile entity listens to broadcasts from the nodes . a node that determines that it is closest to the target generates a gradient . mobile entities climb this gradient to reach the target . for simplicity , this discussion has assumed a single type of target with a single sensor configuration . it will be apparent to one skilled in the art that snipf can handle multiple target types requiring different sensor configurations , by adding a tag to each message indicating the target type with which the message is concerned . required bandwidth is linear in the number of different sensor configurations . the snipf algorithms are extremely robust and economic of processor and communications resources . an anticipated prototype will use a multi - sensor board that includes an acoustic sensor with tone decoder with a range of 6 m @ 60 db , a magnetometer that can sense a truck at 10 m , and a 2 - axis accelerometer with 2 mg sensitivity . other sensors can be interfaced for additional sensing tasks ( e . g ., chem ./ bio sensors , or the multi - frequency hausdorff antenna shown in fig7 to channelize ambient electromagnetic radiation for elint and sigint . snipf can track any entity for which a sensor exists that can function at a range on the order of the communication range . the prototype &# 39 ; s sensor suite will be sensitive to targets such as light trucks , talking humans , and animal - drawn carts as well as more traditional military forces and equipment . the hw supports any sensor , and snipf ( unlike conventional al or data mining algorithms ) makes no assumptions about the nature of the sensors , so the system can be extended easily . one skilled in the art will appreciate that the hardware details described here are by way of example and not restriction , and that the essence of the invention can be applied to other hardware platforms . the inventive system may optionally repair a net when some nodes die or are removed . if a node detects that the number of its neighbors has dropped , it generates a gradient leading to itself . a lightweight unpiloted robotic vehicle equipped with a new node drops the new node in the area of the attracting node , thus increasing the net &# 39 ; s local density and repairing the breach . refinements of this scheme are possible . for example , if the net detects increased activity in an area previously quiescent the density of nodes in that area can be increased to provide finer resolution of sensing . potential users of snipf include sof ( via handheld computers with snipf - compatible radios ), central command ( using information exported from the network to identify events requiring further attention ), and local commands ( who will be interested in exported information , and in addition may deploy unpiloted vehicles such as uav &# 39 ; s or ugv &# 39 ; s to move within the sensor field in response to detected targets ). snipf can also be used by security personnel for perimeter monitoring in military and civilian applications . 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