Algorithm 1: MD-LM-IPDA Track Update Process

1: for each track $\tau$ find 2:    The track state prediction $p\left(x_k^{\tau }|{\chi }_k^{\tau },Z^{k-1}\right)$ and the probability of target existence prediction $P\left({\chi }_k^{\tau }|Z^{k-1}\right)$ 3:    The measurement selection (the gating method), the measurement cell generation (the measurement partition) and the path pattern combination 4:    The modulated clutter measurement density ${\tilde{\rho }}_{〈z_{{\phi }_{\tau },n_{{\phi }_{\tau }}}\left(k\right)|A_j\left(z_{{\phi }_{\tau },n_{{\phi }_{\tau }}}\left(k\right)\right)〉}^{\tau }$ for each path pattern combined measurement cell 5:    A posteriori data association probabilities ${\beta }_{〈z_{{\phi }_{\tau },n_{{\phi }_{\tau }}}\left(k\right)|A_j\left(z_{{\phi }_{\tau },n_{{\phi }_{\tau }}}\left(k\right)\right)〉}^{\tau }$ in (44) and ${\beta }_{k,0}^{\tau }$ in (45) 6:    The updated probability of target existence $P\left({\chi }_k^{\tau }|Z^k\right)$ in (47) 7:    The updated track state $p\left(x_k^{\tau }|{\chi }_k^{\tau },Z^k\right)$ generated by a Gaussian mixture based on all the data association events 8: end for