Source detection and identification in Software Writer USS Code 128 in Software Source detection and identification

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Source detection and identification generate, create code 128a none for software projects interleaved 25 Figure 5.18 (a) Local and (b) distant sources. estimation aspects of the problem, deferring discussion of practical network formation algorithms until 8. The general detection problem as stated above is probably not solvable. Resort must be made to heuristics concerning the particular signals of interest and the resource costs to make much progress.

This divides the problems into classes for which efficient algorithms can be devised; the designer is then presented with the metaproblem of knowing in advance what problem class the system will work for, or devising a higher-level algorithm that can identify the problem class and thus apply the appropriate fusion strategy. The supposition will be made that bandwidth and energy are finite, while the number of nodes is large. There are then two basic situations: the signal is received at high SNR by a small number of sensors, or it is received by a very large number of sensors.

These situations then both divide into problems of detecting and identifying one or multiple signals. In this section, a hierarchical approach to solving each of these problems is taken. As described in 3, all signals attenuate with distance from the source.

This provides a degree of spatial isolation; sensors that are far enough apart receive information about disjoint sets of sources. Further, if the variation in the distances from the source to the sensors is large compared with the average distance the SNRs at the sensors will display large variations and it will be the case that a small subset of the sensors will contribute most of the information to any detection or identification decision. By contrast if a source is strong but distant, then all sensors will detect it with similar SNR and may potentially usefully contribute to the decision.

Representative situations are depicted in Figure 5.18. In each situation, the sensor node topologies are the same, but in (a) the source is within the sensor field while in (b) the source is far away.

While in each situation there are variations in the received signal strength at each node, in (a) the relative variations are much larger for most distance propagation laws.. Example 5.19 Signal streng Code 128C for None th variations Consider the situation depicted in Figure 5.19.

Four nodes on a square grid detect events at locations 1, 2, and 3. Propagation losses follow the second power of distance. What is the ratio of the maximum to minimum received power at the four nodes for sources at the various positions, and how might this ratio be used to distinguish whether a target is near or far Solution For position 1 clearly the ratio is 1, for position 2 the ratio is (5/4)/(1/4) 5, for position 3 it is (17/4)/(5/4) 17/5, while for position 4 it is 36.

25/25.25. Clearly, the further away the source.

5.4 Hierarchical detection and identification systems a a/2 1 2. Figure 5.19 Signal locations. Increased resource use, performance Remote signal processing C oherent cooperative processing Non-coherent local processing Feature extraction for signal identification Energy detection. Decreased probability of occurrence Figure 5.20 Signal process ing hierarchy. the more the ratio tends towards 1.

While generally close signals display larger variations in strength between the four sensors, a source in position 1 is equidistant from all of them. To distinguish this case from more distant but powerful sources, a second ring of sensor nodes could be queried, or the time series analyzed if the source is in motion..

The basic design objective Software Code 128B with a resource constraint such as energy or bandwidth is to achieve the desired detection or identification probability constraints with the minimum number of active nodes. Prior probabilities are seldom known in sensor network problems, resulting in missed detection and false alarm probability constraints. A natural approach to such problems is a multilayered detection scheme.

Consider, for example, the use of seismic sensors for detection and identification problems in which there is an energy constraint. The sensor itself may consume no energy, and the A/D conversion and amplification circuitry are low power due to low clock rates. Energy is consumed mainly in radio communications with other nodes and when sophisticated local signal processing is employed.

The average energy consumption at each node can be minimized while meeting missed detection and false alarm probability constraints by pursuing a hierarchical processing approach, as depicted in Figure 5.20. At the bottom of the signal processing hierarchy is detection of events based on received energy.

This is a very simple test that uses little energy, and thus can operate continuously. As most of the time there are no sources of interest, the average power consumption is close to that of this operation. The threshold is set so as to guarantee the missed detection probability.

The next level of processing is some form of feature extraction (e.g., in the frequency or wavelet domains) to determine whether the detected signal is of the desired type.

If the confidence level in the identification decision is high enough, nearby nodes may be alerted to suppress reporting (as this node has made a good enough decision) and the decision is communicated through the.
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