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Target detection, tracking, and sensor fusion are complicated problems, which usually are performed sequentially. First detecting targets, then tracking, then fusing multiple sensors reduces computations. This procedure however is inapplicable to difficult targets which cannot be reliably detected using individual sensors, on individual scans or frames. In such more complicated cases one has to perform functions of fusing, tracking, and detecting concurrently. This often has led to prohibitive combinatorial complexity and, as a consequence, to sub-optimal performance as compared to the information-theoretic content of all the available data. It is well appreciated that in this task the human mind is by far superior qualitatively to existing mathematical methods of sensor fusion, however, the human mind is limited in the amount of information and speed of computation it can cope with. Therefore, research efforts have been devoted toward incorporating “biological lessons” into smart algorithms, yet success has been limited. Why is this so, and how to overcome existing limitations? The fundamental reasons for current limitations are analyzed and a potentially breakthrough research and development effort is outlined. We utilize the way our mind combines emotions and concepts in the thinking process and present the mathematical approach to accomplishing this in the current technology computers. The presentation will summarize the difficulties encountered by intelligent systems over the last 50 years related to combinatorial complexity, analyze the fundamental limitations of existing algorithms and neural networks, and relate it to the type of logic underlying the computational structure: formal, multivalued, and fuzzy logic. A new concept of dynamic logic will be introduced along with algorithms capable of pulling together all the available information from multiple sources. This new mathematical technique, like our brain, combines conceptual understanding with emotional evaluation and overcomes the combinatorial complexity of concurrent fusion, tracking, and detection. The presentation will discuss examples of performance, where computational speedups of many orders of magnitude were attained leading to performance improvements of up to 10 dB (and better).
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