Abstract
Methods dealing with the problem of Joint Tracking and Classification (JTC) are abundant, among which Simultaneous Tracking and Classification (STC) provides a modularized scheme solving tracking and classification subproblems simultaneously. However, there is no explicit hard decision on the class label but only soft decision (class probability) is provided. This does not fit many practical cases, in which a hard decision is urgently needed. To solve this problem, this paper proposes a Hard decision-based STC (HSTC) method. HSTC takes all the decision error rate, timeliness, and estimation error into account. Specifically, for decision, the sequential probability ratio test is adopted due to its nice properties and also the adaptability to our situation. For estimation, by utilizing the two-way information exchange between the tracker and the classifier, we propose flexible three tracking schemes related to decision. The HSTC tracking result is divided into three parts according to the time of making the hard decision. In general, the proposed HSTC method takes advantage of both SPRT and STC. Finally, two illustrative JTC examples with hard decision verify the effectiveness of the the proposed HSTC method. They show that HSTC can meet the demand of the problem, and also has the performance superiority in both decision and estimation.
Highlights
Target tracking and classification are critical in battlefield surveillance systems [1,2,3,4,5,6,7,8]
In view of the above, we propose to use the well known sequential probability ratio test (SPRT) [29] due to its nice properties: SPRT is optimal in the sense that it minimizes the average sample number (ASN) under both hypotheses simultaneously among all tests of the same allowable error probabilities [30]
Note that this paper considers the military application of the Joint Tracking and Classification (JTC) problem
Summary
Target tracking and classification are critical in battlefield surveillance systems [1,2,3,4,5,6,7,8] They are treated separately using their respective data and techniques: tracking is usually based on kinematic data while classification relies on attribute data. Since they involve continuous and discrete valued uncertainties respectively, their solutions are different. Solving these two problems jointly has attracted much attention. It is easy to realize that for such problems, tracking and classification should be handled jointly, and the performance of both could be improved by effectively utilizing their mutual-effect
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