融合泰勒补偿和投影相消的PHD波达方向跟踪方法

doi:10.7527/S1000-6893.2026.33190

Abstract

Abstract:

In multi-domain collaborative sensing and target situation awareness tasks， traditional high-accuracy Direction-of-Arrival （DOA） estimation methods typically rely on a known and fixed number of sources. However， during continuous tracking， the number of targets may vary over time， causing fixed-source-number models to become invalid. Meanwhile， in passive array sensing， multi-target signals often appear in a superimposed form， making it difficult to construct effective single-target likelihood functions and thus limiting the applicability of Random Finite Set （RFS）-based filters in DOA tracking. To address these challenges， this paper proposes a continuous DOA tracking method based on a Taylor-expanded projection-cancellation model. By performing a Taylor expansion of the steering vector around the predicted angle， an extended signal subspace is constructed， and interference from other sources is eliminated using sequential projection operators， resulting in a single-source-equivalent pseudo-spectrum. This enables robust decomposition of multi-source superimposed observations and reliable construction of single-target likelihoods. Furthermore， based on the energy characteristics of the projection-cancellation spectrum and the particle-weight distribution， a birth-death detection mechanism is introduced， which adaptively adjusts the likelihood dimension in the PHD filter without requiring prior source-number estimation. Simulation results demonstrate that the proposed method achieves superior DOA tracking performance compared with conventional approaches， particularly under low SNR conditions and dynamically varying source scenarios.

Key words: DOA tracking, multi-source superimposed measurements, Taylor expansion, projection cancellation, random finite set, pseudo-likelihood function

CLC Number:

V247

Jinke CAO, Xiaofei ZHANG, Qihui WU, De BEN. PHD-based DOA tracking via Taylor compensation and projection cancellation[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(16): 333190.

Figures/Tables 17

Fig.1

Table 1

Reference parameter configuration

参数	符号	取值
阵元数	$M$	8
阵元间距	$d$	$λ / 2$
阵列结构		均匀阵列
快照数	$J$	200
检测概率	$p D, t (·)$	0.98
存活概率	$p S, t (·)$	0.99
新生强度	$r t (·)$	0.02
新生粒子数	$K B, t$	1 000
新生信源能量谱比率	$η S, t$	4
消亡信源能量谱比率	$η N, t$	4
跟踪时长	$T$	50
采样间隔	$Δ T$	1
蒙特卡洛仿真次数	$M c$	200

Table 1

Fig.2

Table 2

Fig.3

Fig.4

Table 3

Source parameters

信源编号	初始状态	出生时刻/s	消亡时刻/s
1	$[- 80,1] T$	1	50
2	$[30, - 1.5] T$	10	35
3	$[50, - 0.5] T$	25	45

Table 3

Fig.5

Fig.6

Table 4

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

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算法名称	运行时间/s
PASTd	0.253
MUSIC	2.183
PF	0.386
MUSIC-PHD	0.417

算法名称	运行时间/s
MUSIC-PHD	7.844
MS-PHD	7.676
PC-PHD	1.817
TPC-PHD	1.843

PHD-based DOA tracking via Taylor compensation and projection cancellation

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