Risk-Aware Resource Allocation Strategy for Target Tracking in a Cognitive Radar Network

. 2026 May 22;26(11):3299. doi: 10.3390/s26113299

Algorithm 1 Target tracking process in the cognitive radar framework

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Initialization:
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$t_{k} \leftarrow 1$
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${\hat{X}}_{0 | 0} \leftarrow X_{0}$
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$P_{0 | 0} \leftarrow P_{0}$
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while $t_{k} < t_{k_{\max}}$ do ▷Time loop
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Step 1. Estimation
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${\hat{X}}_{k | k - 1} \leftarrow f ({\hat{X}}_{k - 1 | k - 1})$
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$A_{k} \leftarrow$ Jacobian of f at $t_{k}$
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$P_{k | k - 1} \leftarrow A_{k} P_{k - 1 | k - 1} A_{k}^{T} + Q_{k - 1}$
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Step 2. Decision making
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Construct the positional PC-CRLB surrogate $B_{p, k}^{- ↑}$ from the predicted state
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Calculate $λ_{\max} (B_{p, k}^{- ↑})$
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Formulate the optimization problem in (19)
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Obtain $u_{k}$ by solving the SOCP
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Step 3. State update
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${\hat{X}}_{k | k} \leftarrow {\hat{X}}_{k | k - 1}$
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for $i = 1$ to N do
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if $u_{i, k} > 0$ then
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Acquire $z_{i, k} = h_{i} (X_{k}) + w_{i, k} (T_{i, k}^{d})$
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Compute the Jacobian $H_{i, k}$ at ${\hat{X}}_{k | k - 1}$
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Calculate the Kalman gain
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Update ${\hat{X}}_{k | k}$ and $P_{k | k}$
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end if
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end for
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$t_{k} \leftarrow t_{k} + 1$
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end while