Research Article
Bayesian-Based Search Decision Framework and Search Strategy Analysis in Probabilistic Search
Algorithm 2
The searching process of the saccadic strategy.
| (1) | Initialize the belief map M | | (2) | Initialization decision threshold B, B and time t = 0 | | (3) | Calculate the aggregate belief B(0) | | (4) | Initialization parameter i = 1 | | (5) | while B < B(t) < B do | | (6) | Find the cell (xd, yd) with the largest belief on the belief map M | | (7) | if t = 0 then | | (8) | Construct a path P from (xc, yc) to (xd, yd) by the Dijkstra algorithm | | (9) | ath+1 = P(1) | | (10) | else | | (11) | if The (xd, yd) did not change then | | (12) | ath+1 = P(i) | | (13) | else | | (14) | Rebuild the path P | | (15) | ath+1 = P(1) | | (16) | Reset i = 1 | | (17) | end if | | (18) | end if | | (19) | i = i + 1 | | (20) | Check the cell ath+1 and get detection result Da(t) | | (21) | Update M based on Da(t) | | (22) | Calculate B(t) | | (23) | t = t + 1 | | (24) | end while | | (25) | return Search result |
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