Real-time cross-domain monitoring of multi-UAV-multi-USV systems via efficient block sparse Bayesian learning

Special Topic: Intelligent Materials and Devices

Open Access

Algorithm 1

Multi-UAV-multi-USV monitoring with EBSBL (MUMU-EBSBL)

Input: $\tilde{H}$ : total observation time number; $n$ : number of USVs and UAVs; $k_{max}$ : maximum iterations of EBSBL; $ς$ : stopping threshold for EBSBL.
Output: $u_{j}$ : control input of UAVs.
1:	for $h = 1, \dots, \tilde{H}$ do
2:		Assign UAVs for each USV by Eqs. (4)–(8)
3:		Compute the estimated position and velocity of USVs by Eq. (9)
4:		for $i = 1, \dots, n$ do
5:			for $x$ -axis and $y$ -axis do
6:				Compute data vector $τ$ and dictionary matrix $Ψ$ by Eqs. (10)–(13)
7:				Initialize $ϖ^{(0)}, γ^{(0)}, σ^{(0)}$ , and set $k = 0$
8:				while $k < k_{max}$ do
9:					Compute $Σ_{w}^{(k + 1)}, w^{(k + 1)}, ϖ^{(k + 1)}, ϱ^{(k + 1)}, σ^{(k + 1)}, γ_{g'}^{(k + 1)}$ by Eq. (34)–(39)
10:					Compute: $Γ^{(k + 1)}$ by Eq. (20)
11:					if $∥ w^{(k + 1)} - w^{(k)} ∥ \leq ς$ then break;
12:					else $k = k + 1$ ;
13:					end if
14:				end while
15:				Compute $\hat{θ}$ by Eq. (31)
16:			end for
17:			Identify coordinated dynamics $f_{i} ({\tilde{p}}_{1} (t), \dots, {\tilde{p}}_{n} (t))$ by Eq. (32)
18:			Generate sigma points ${X_{b}}$ by Eq. (37)
19:			Compute predicted state ${\hat{p}}_{t + Δ t \| t}$ by Eq. (38)
20:			Use UKF solving ${\hat{p}}_{t + Δ t \| t + Δ t}$ with ${\hat{z}}_{t + Δ t}$ by Eqs. (39)–(42)
21:			Compute $u_{j}^{init} (t)$ by Eqs. (43) and (44)
22:			Compute $u_{j}^{avoid} (t)$ by Eqs. (45) and (46)
23:			$u_{j} (t) = u_{j}^{init} (t) + u_{j}^{avoid} (t)$
24:		end for
25:	end for

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