Table 1 CCJDE-MDT algorithm

(a) Initialization

Conditioned on each hypothesis \(H^{j}\), initialize the state \({\hat{X}}_{0}^{(j)}\) and its MSE \(P_{0}^{(j)}\)

Initialize the hypothesis probability \(P\{H^{j} \}=1/N\).

(b) Update

At time k, with data \(Z_{k}\) available:

Update the state estimate \({\hat{X}}_{k}^{(j)}\) and its MSE \(P_{k}^{(j)}\) under each hypothesis \(H^{j}\);

Update the posterior probability of each \(P\{H^{j}|Z^{k}\}\) according to (6)

(c) Further

Compute the expected estimation cost \(\varepsilon _{ij}^{k}\) according to (3);

computation

For each candidate \(D^{i}\), compute the estimate \({\check{X}}_{k}^{(i)}\) according to (5)

(d) Output

The optimal CCJDE-MDT detection result is \(D_{k}^{i}\): \(C^{i}(Z^{k})\le C^{l}(Z^{k}),\forall l\);

The corresponding CCJDE-MDT tracking result is \({\check{X}}_{k}^{(i)}\)