A PMBM Filter for Tracking Coexisting Point and Group Targets with Target Spawning and Generalized Measurement Models

TY - JOUR

T1 - A PMBM Filter for Tracking Coexisting Point and Group Targets with Target Spawning and Generalized Measurement Models

AU - Zhang, Jichuan

AU - Jiang, Qi

AU - Jiao, Longxiang

AU - Li, Weidong

AU - Hu, Cheng

N1 - Publisher Copyright: © 2026 by the authors.

PY - 2026/3

Y1 - 2026/3

N2 - Highlights: What are the main findings? A modified PMBM prediction framework is developed by incorporating a group-dependent target spawning model, enabling unified tracking of coexisting point and group targets in complex group-dynamic scenarios. A generalized PMBM update strategy is proposed to support point-target density updates with arbitrary measurement cardinality, thereby overcoming the limitations of the standard point-target measurement assumption. What are the implications of the main findings? The proposed prediction model enables timely detection of newly spawned targets in dynamic group scenarios, such as drone swarms with member separation. The generalized update mechanism improves state estimation accuracy and target-type inference under non-ideal measurement conditions, where a single point target may generate multiple measurements within one scan. Accurate multi-target filtering is crucial for low-altitude surveillance, where point and group targets often coexist. Poisson multi-Bernoulli mixture (PMBM) filters provide a unified Bayesian framework for the joint filtering of point and group targets under the assumptions of independent target dynamics and standard measurement models. However, in practical scenarios, group targets may generate new targets through member separation, while point targets may produce multiple measurements due to multi-beam sensing and micro-Doppler signatures. These phenomena violate the assumptions of existing PMBM filters and lead to degraded state estimation and target-type inference. To address these challenges, this paper proposes a modified PMBM filter with group target spawning and generalized measurement models for coexisting point and group targets. Specifically, a group-dependent spawning model is incorporated into the prediction step to enable timely detection of newly spawned targets. In addition, a generalized update function is developed to support point-target density updates with measurement sets of arbitrary cardinality, and a measurement-rate-based correction factor is introduced to improve target-type estimation under nonstandard measurement conditions. Furthermore, an efficient Poisson multi-Bernoulli approximation is derived to reduce computational complexity. The effectiveness of the proposed filter is verified through simulation and experimental results.

AB - Highlights: What are the main findings? A modified PMBM prediction framework is developed by incorporating a group-dependent target spawning model, enabling unified tracking of coexisting point and group targets in complex group-dynamic scenarios. A generalized PMBM update strategy is proposed to support point-target density updates with arbitrary measurement cardinality, thereby overcoming the limitations of the standard point-target measurement assumption. What are the implications of the main findings? The proposed prediction model enables timely detection of newly spawned targets in dynamic group scenarios, such as drone swarms with member separation. The generalized update mechanism improves state estimation accuracy and target-type inference under non-ideal measurement conditions, where a single point target may generate multiple measurements within one scan. Accurate multi-target filtering is crucial for low-altitude surveillance, where point and group targets often coexist. Poisson multi-Bernoulli mixture (PMBM) filters provide a unified Bayesian framework for the joint filtering of point and group targets under the assumptions of independent target dynamics and standard measurement models. However, in practical scenarios, group targets may generate new targets through member separation, while point targets may produce multiple measurements due to multi-beam sensing and micro-Doppler signatures. These phenomena violate the assumptions of existing PMBM filters and lead to degraded state estimation and target-type inference. To address these challenges, this paper proposes a modified PMBM filter with group target spawning and generalized measurement models for coexisting point and group targets. Specifically, a group-dependent spawning model is incorporated into the prediction step to enable timely detection of newly spawned targets. In addition, a generalized update function is developed to support point-target density updates with measurement sets of arbitrary cardinality, and a measurement-rate-based correction factor is introduced to improve target-type estimation under nonstandard measurement conditions. Furthermore, an efficient Poisson multi-Bernoulli approximation is derived to reduce computational complexity. The effectiveness of the proposed filter is verified through simulation and experimental results.

KW - PMBM filter

KW - generalized measurement model

KW - group target spawning

KW - multi-target filtering

UR - https://www.scopus.com/pages/publications/105032632455

U2 - 10.3390/rs18050769

DO - 10.3390/rs18050769

M3 - Article

AN - SCOPUS:105032632455

SN - 2072-4292

VL - 18

JO - Remote Sensing

JF - Remote Sensing

IS - 5

M1 - 769

ER -