## Multisensor Fusion (8,14,15)

Research has been conducted on multisensor fusion for target recognition. Some of the motivating factors of such research are increased target illumination, increased coverage, and increased information for recognition. Significant improvement in target recognition performance has been reported (8) when multiple radar sources are utilized using sensor fusion approaches. Tenney and Sandell (14) developed a theory for obtaining the distributed Bayesian decision rules. Chair and Varshney (15) presented an optimal fusion structure given that

Fig. 8. A typical data fusion approach for target recognition. [Adapted from Heuter et al. (8)]. |

detectors are independently designed. The target recognition using multiple sensors is formulated as a two — stage decision problem in Ref. 8. A typical radar target recognition approach using data fusion is illustrated in Fig. 8. After the prescreening, single-source classifications are performed first; then the fusion of decision are performed.

The data fusion problem is treated as an m-hypothesis problem with individual source decisions being the observations. The decision rule for m-hypothesis is written as

Decide wi if g1 (u) > gj(u) for all j ф і і 30 j

For Bayes’ rule, gi(u) is a posterior probability. That is,

Since the prior probability and the distribution of features cannot be estimated accurately, a heuristic function is used (8). It is a direct extension of Bayesian approach introduced by Varshney (16), and the function gi(-) is generalized to include the full threshold range:

where P0 and P1 are prior probabilities; ^1 and are the sets of all i such that {gi(u) > Ti} and {gi(u) < Ti}, respectively, with Ti being the individual source threshold for partitioning decision regions; and the probabilities Pfi and Pdi are false alarm rates and probabilities of detections of each local sensor. The probabilities Pf i and Pdi are defined by the cumulative distribution functions (CDF) for each decision statistic. In practice, the CDFs are quantized and estimated from training on the individual sensor’s classifier error probabilities. In a distributed scenario, the weighting can be computed at each sensor and transmitted to the fusion center, where they will be summed and compared to the decision threshold. In 8, the data fusion approach is applied to multiple polarimetric channels of a SAR image, and substantially improved classification performance is reported.

In radar target recognition, different types of radar are employed for different applications. In this article, radar target recognition approaches for different radar systems are discussed.

J. Webster (ed.), Wiley Encyclopedia of Electrical and Electronics Engineering Copyright © 1999 John Wiley & Sons, Inc.

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