一種幅度信息輔助多伯努利濾波算法
發(fā)布時間：2018-11-13 09:13
【摘要】：在許多多目標(biāo)跟蹤場景中,目標(biāo)返回的幅度通常強于虛警雜波返回的幅度。通過建立更加準(zhǔn)確的包含幅度信息的目標(biāo)和虛警雜波似然函數(shù),可提高多目標(biāo)估計精度。該文提出一種基于隨機有限集的幅度信息輔助多伯努利濾波(Amplitude Information Assistant Multi-Bernoulli Filter,AIA-MBer F)算法。該算法通過建立幅度似然函數(shù)將幅度信息引入到多伯努利濾波的更新過程中,并給出針對線性和非線性模型的高斯混合(Gaussian Mixture,GM)和序貫蒙特卡洛(Sequential Monte Carlo,SMC)實現(xiàn)方法。仿真結(jié)果表明,該濾波算法相比于傳統(tǒng)多伯努利濾波(Multi-Bernoulli Filter,MBer F)無論GM還是SMC實現(xiàn)都可獲得更加準(zhǔn)確穩(wěn)定的目標(biāo)數(shù)和對應(yīng)的目標(biāo)狀態(tài)估計。
[Abstract]:In many multi-target tracking scenarios, the return amplitude of target is usually stronger than that of false alarm clutter. The accuracy of multi-target estimation can be improved by establishing a more accurate likelihood function of target and false alarm clutter. In this paper, an amplitude information aided multiple Bernoulli filter (Amplitude Information Assistant Multi-Bernoulli Filter,AIA-MBer F) algorithm based on random finite sets is proposed. In this algorithm, amplitude information is introduced into the updating process of multiple Bernoulli filtering by establishing amplitude likelihood function, and the implementation methods of Gao Si mixture (Gaussian Mixture,GM) and sequential Monte Carlo (Sequential Monte Carlo,SMC (SMC) for linear and nonlinear models are presented. Simulation results show that the proposed algorithm can obtain more accurate and stable target number and corresponding target state estimation than the traditional multi-Bernoulli filter (Multi-Bernoulli Filter,MBer F), regardless of the GM or SMC implementation.
【作者單位】： 北京航空航天大學(xué)電子信息工程學(xué)院;
【基金】：國家自然科學(xué)基金(61171122;61201318;61471019;61501011) 中央高�；究蒲袠I(yè)務(wù)費專項資金(YWF-15-GJSYS-068)~~
【分類號】：TN713
[Abstract]:In many multi-target tracking scenarios, the return amplitude of target is usually stronger than that of false alarm clutter. The accuracy of multi-target estimation can be improved by establishing a more accurate likelihood function of target and false alarm clutter. In this paper, an amplitude information aided multiple Bernoulli filter (Amplitude Information Assistant Multi-Bernoulli Filter,AIA-MBer F) algorithm based on random finite sets is proposed. In this algorithm, amplitude information is introduced into the updating process of multiple Bernoulli filtering by establishing amplitude likelihood function, and the implementation methods of Gao Si mixture (Gaussian Mixture,GM) and sequential Monte Carlo (Sequential Monte Carlo,SMC (SMC) for linear and nonlinear models are presented. Simulation results show that the proposed algorithm can obtain more accurate and stable target number and corresponding target state estimation than the traditional multi-Bernoulli filter (Multi-Bernoulli Filter,MBer F), regardless of the GM or SMC implementation.
【作者單位】： 北京航空航天大學(xué)電子信息工程學(xué)院;
【基金】：國家自然科學(xué)基金(61171122;61201318;61471019;61501011) 中央高�；究蒲袠I(yè)務(wù)費專項資金(YWF-15-GJSYS-068)~~
【分類號】：TN713
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