利用伯努利濾波的多目標(biāo)機(jī)動(dòng)雷達(dá)誤差配準(zhǔn)方法

鄧洪高; 余潤(rùn)華; 紀(jì)元法; 吳孫勇; 孫希延

doi:10.11999/JEIT240013

利用伯努利濾波的多目標(biāo)機(jī)動(dòng)雷達(dá)誤差配準(zhǔn)方法

doi: 10.11999/JEIT240013

鄧洪高^{1, 4},
余潤(rùn)華^{1, 2},
紀(jì)元法^{1, 2, 4},
吳孫勇^{2, 3, ,},
孫希延^{1, 2, 4, 5}

1.
桂林電子科技大學(xué)信息與通信學(xué)院桂林 541004
2.
桂林電子科技大學(xué)廣西精密導(dǎo)航技術(shù)與應(yīng)用重點(diǎn)實(shí)驗(yàn)室桂林 541004
3.
桂林電子科技大學(xué)數(shù)學(xué)與計(jì)算科學(xué)學(xué)院桂林 541004
4.
衛(wèi)星導(dǎo)航定位與位置服務(wù)國(guó)家地方聯(lián)合工程研究中心桂林 541004
5.
南寧桂電電子科技研究院有限公司南寧 530031

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(U23A20280, 62061010, 62161007)，廣西科技廳項(xiàng)目(AB23026120)

詳細(xì)信息

作者簡(jiǎn)介:
鄧洪高：男，研究員，研究方向?yàn)槔走_(dá)信號(hào)處理和衛(wèi)星導(dǎo)航

余潤(rùn)華：男，碩士生，研究方向?yàn)槔走_(dá)多目標(biāo)跟蹤、空間誤差配準(zhǔn)和多源信息融合

紀(jì)元法：男，教授，研究方向?yàn)樾l(wèi)星導(dǎo)航和信號(hào)處理

吳孫勇：男，教授，研究方向?yàn)槔走_(dá)信號(hào)處理、多目標(biāo)檢測(cè)與跟蹤和多源信息融合

孫希延：女，研究員，研究方向?yàn)樾l(wèi)星導(dǎo)航和遙感

通訊作者:
吳孫勇　wusunyong121991@163.com

中圖分類號(hào): TN959.1; V243.2
計(jì)量
- 文章訪問數(shù): 159
- HTML全文瀏覽量: 53
- PDF下載量: 19
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-01-16
- 修回日期: 2024-09-05
- 網(wǎng)絡(luò)出版日期: 2024-09-11
- 刊出日期: 2024-10-30

Mobile Radar Registration with Multiple Targets Based on Bernoulli Filter

DENG Honggao^{1, 4},
YU Runhua^{1, 2},
JI Yuanfa^{1, 2, 4},
WU Sunyong^{2, 3
, ,},
SUN Xiyan^{1, 2, 4, 5}

1.
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
2.
Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Science and Technology, Guilin 541004, China
3.
School of Mathematics and Computing Science, Guilin University of Electronic Science and Technology, Guilin 541004, China
4.
National & Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service, Guilin 541004, China
5.
GUET-Nanning E-Tech Research Institute Co., Ltd., Nanning 530031, China

Funds: The National Natural Science Foundation of China (U23A20280, 62061010, 62161007), Guangxi Science and Technology Department Project (AB23026120)

摘要

摘要: 傳統(tǒng)的組網(wǎng)雷達(dá)多目標(biāo)誤差配準(zhǔn)方法通常假設(shè)數(shù)據(jù)關(guān)聯(lián)關(guān)系已知，但在平臺(tái)機(jī)動(dòng)的情況下，系統(tǒng)同時(shí)存在雷達(dá)測(cè)量偏差和平臺(tái)姿態(tài)角偏差，且雷達(dá)觀測(cè)過程中會(huì)受到雜波干擾，導(dǎo)致數(shù)據(jù)關(guān)聯(lián)尤為困難。針對(duì)這一問題，該文提出一種基于伯努利濾波的多目標(biāo)機(jī)動(dòng)雷達(dá)誤差配準(zhǔn)方法。首先建立系統(tǒng)偏差的量測(cè)與狀態(tài)方程，然后將系統(tǒng)偏差建模成伯努利隨機(jī)有限集，利用公共坐標(biāo)系下的原始量測(cè)可實(shí)現(xiàn)系統(tǒng)偏差在伯努利濾波框架下的遞推估計(jì)，有效避免了數(shù)據(jù)關(guān)聯(lián)問題。同時(shí)，為了充分利用多目標(biāo)量測(cè)信息，提出一種修正的貪婪量測(cè)劃分方法，在每個(gè)濾波時(shí)刻挑選出系統(tǒng)偏差對(duì)應(yīng)的最優(yōu)量測(cè)子集，利用量測(cè)子集中的多量測(cè)信息實(shí)現(xiàn)系統(tǒng)偏差的集中式融合估計(jì)，提高了系統(tǒng)偏差的估計(jì)精度和收斂速度。仿真實(shí)驗(yàn)表明，所提方法能夠在數(shù)據(jù)關(guān)聯(lián)未知的多目標(biāo)多雜波場(chǎng)景下對(duì)雷達(dá)測(cè)量偏差和平臺(tái)姿態(tài)角偏差進(jìn)行有效估計(jì)，在平臺(tái)姿態(tài)角變化率較低時(shí)，所提方法具有較強(qiáng)的適應(yīng)性。
- 誤差配準(zhǔn) /
- 數(shù)據(jù)關(guān)聯(lián) /
- 伯努利濾波 /
- 集中式融合 /
- 量測(cè)劃分
Abstract: Traditional methods for multi-target bias registration in networked radar system typically assume that the data association relationship is known. However, in the case of platform maneuvering, there are simultaneously radar measurement biases and platform attitude angle biases, and the radar observation process is prone to clutter interference, resulting in difficulties in data association. To address this issue, a multi-target mobile radar bias registration method based on Bernoulli filter is proposed. Firstly, the measurement and state equations for the system biases are established, and then the system biases are modeled as a Bernoulli random finite set. The recursive estimation of the system biases under the Bernoulli filtering framework is achieved using the original measurements in a common coordinate system, effectively avoiding the data association. Additionally, to fully utilize multi-target measurement information, a modified greedy measurement partitioning method is proposed to select the optimal measurement subset corresponding to the system biases at each filtering time step, and the centralized fusion estimation of the system biases is performed using the multi-measurement information in the measurement subset, improving the estimation accuracy and convergence speed of the system biases. Simulation experiments show that the proposed method can effectively estimate radar measurement biases and platform attitude angle biases in multi-target and cluttered scenarios with unknown data association. Moreover, this method demonstrates strong adaptability when the platform attitude angle variation rate is low.
- Registration /
- Data association /
- Bernoulli filter /
- Centralized fusion /
- Measurement partition

HTML全文

圖 1 場(chǎng)景1中偏差估計(jì)均方根誤差圖

下載: 全尺寸圖片幻燈片

圖 2 場(chǎng)景2中偏差估計(jì)均方根誤差圖

下載: 全尺寸圖片幻燈片

表 1 場(chǎng)景3中偏差收斂步數(shù)比較

量測(cè)劃分前/后	偏差估計(jì)收斂步數(shù)
	雷達(dá)測(cè)量偏差			平臺(tái)姿態(tài)角偏差
	徑向距離	方位角	俯仰角	偏航角	縱搖角	橫搖角
前	161	330	288	362	317	305
后	18	281	194	310	260	242

下載: 導(dǎo)出CSV

表 2 場(chǎng)景3中偏差估計(jì)精度比較(%)

量測(cè)劃分前/后	偏差估計(jì)精度
	雷達(dá)測(cè)量偏差			平臺(tái)姿態(tài)角偏差
	徑向距離	方位角	俯仰角	偏航角	縱搖角	橫搖角
前	98.43	96.52	90.33	93.25	93.34	92.14
后	98.66	98.18	95.04	97.31	96.71	96.53

下載: 導(dǎo)出CSV

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