CAJ | 학술논문

针对雷达目标一维距离像(HRRP)识别问题,结合支持向量机(SVM)和 DS 证据理论提出一种多极化HRRP 分类方法—–SDHRRP.该方法通过混淆矩阵获取基分类器之间的距离,从而根据基分类器对不同目标类的分类能力给其赋予不同的可信度.将该可信度值与 SVM 后验概率结合到 DS 证据理论的基本概率赋值(BPA)中,以实现 SVM 和 DS 证据理论在目标识别中的有效结合.对实测目标数据的实验结果表明,基于分类器可信度得到的 BPA 能够有效避免证据冲突, SDHRRP 方法可以有效降低融合分类的误差率.
침대뢰체목표일유거리상(HRRP)식별문제,결합지지향량궤(SVM)화 DS 증거이론제출일충다겁화HRRP 분류방법—–SDHRRP.해방법통과혼효구진획취기분류기지간적거리,종이근거기분류기대불동목표류적분류능력급기부여불동적가신도.장해가신도치여 SVM 후험개솔결합도 DS 증거이론적기본개솔부치(BPA)중,이실현 SVM 화 DS 증거이론재목표식별중적유효결합.대실측목표수거적실험결과표명,기우분류기가신도득도적 BPA 능구유효피면증거충돌, SDHRRP 방법가이유효강저융합분류적오차솔.
@@@@For the hotspot of high resolution range profile(HRRP) usage in radar target recognition, a multi-polarized HRRP classification approach combing SVM and DS evidence theory‘SDHRRP’is presented. The method defines different confidence for the classifiers based on the distance between each other given by confusion matrix. Then the value and the posterior probability of SVM are integrated into the BPA(basic probability assignment), which achieves the combination of SVM and the evidence theory in target recognition. The gained BPA based on classifier reliability can avoid the evidence conflict efficiently. The experimental results based on the measured data show the effectiveness of the proposed approach.