| 摘要: |
| 在复杂的海洋环境中,强海杂波对雷达目标检测产生严重干扰,易引起虚警。对海杂波谱特性参数的准确估计有助于利用白化等手段对海杂波进行抑制,对海上目标检测具有重要意义。针对时变、复杂的海洋环境的杂波谱变化,本文提出一种能够适应环境的多帧贝叶斯迭代感知估计方法,对海杂波谱的中心频率和带宽两个特性参数进行估计。所提方法首先收集待估测区域的海杂波回波,进行先验分布初始参数的估计,随后通过统计海杂波贝叶斯估计值的分布参数,对先验分布的参数进行迭代,进而实现先验分布参数逐渐与环境相适应。在与环境匹配的先验分布条件下对海杂波谱的中心频率和带宽进行贝叶斯估计,能够很大程度降低估计误差。使用四、五级海况下的X波段对海探测数据集的杂波区对所提方法进行实验,对海杂波谱特性参数的估计误差小于单帧估计方法和使用遗忘因子的估计方法,证明了所提方法的有效性。 |
| 关键词: 海杂波 多普勒谱 贝叶斯估计 |
| DOI： |
| 分类号:TN957.54 |
| 基金项目:国家自然科学基金资助项目(62388102,62101583) |
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| Multi-frame Bayesian Iterative Estimation Method for Frequency Center and Bandwidth of Sea Clutter Spectrum |
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| Abstract: |
| In complex marine environments, sea clutter can cause serious interference to radar target detection and easily lead to false alarms. Accurately estimating the spectral characteristics of sea clutter can help suppress it using techniques such as whitening, which is of great significance for maritime target detection. This paper proposes a multi-frame Bayesian iterative perception estimation method that can adapt to the time-varying and complex sea clutter spectrum changes, and estimate the two characteristic parameters of center frequency and bandwidth of the sea clutter spectrum. The proposed method first collects sea clutter echoes from the area to be estimated, estimates the initial parameters of the prior distribution, and then iterates the parameters of the prior distribution by statistically estimating the Bayesian values of sea clutter, gradually adapting the prior distribution parameters to the environment. Bayesian estimation of the center frequency and bandwidth of sea clutter spectra under prior distribution conditions that match the environment can greatly reduce estimation errors. Experiments were conducted on the clutter area of the sea detection dataset using X-bands under fourth and fifth sea states, and the proposed method showed that the estimation error of the sea clutter spectral characteristic parameters was smaller than that of the single frame estimation method and the estimation method using forgetting factor, demonstrating the effectiveness of the proposed method. |
| Key words: sea clutter doppler spectrum key word 3 Bayesian estimation |