Matlab实现的基于麻雀搜索算法的无线传感器网络3D-Dvhop定位算法：三维空间最小误差寻找未知节点位置并对比原始与SSA版本,基于麻雀搜索算法的无线传感器网络3D-Dvhop定位算法优化与实践：
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Matlab实现的基于麻雀搜索算法的无线传感器网络3D-Dvhop定位算法：三维空间最小误差寻找未知节点位置并对比原始与SSA版本,基于麻雀搜索算法的无线传感器网络3D-Dvhop定位算法优化与实践：寻找最小误差实现精准定位,matlab代码：基于麻雀搜索算法的无线传感器网络3D-Dvhop定位算法- 在三维空间中，利用麻雀搜索算法寻找未知节点到锚节点的实际距离和估计距离之间的最小误差，完成对未知节点位置的估计- 进行了原始3D-Dvhop定位算法和SSA-3D-Dvhop定位算法的对比- 注释很详细,基于麻雀搜索算法; 3D-Dvhop定位算法; 距离误差; 节点位置估计; 对比实验; 详细注释。,麻雀搜索算法优化的3D-Dvhop无线传感器网络定位方法 <link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90425907/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90425907/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">探索麻雀搜索算法在无线传感器网络<span class="_ _0"> </span></span>3D-Dvhop<span class="_ _0"> </span><span class="ff2">定位中的实践</span>**</div><div class="t m0 x1 h2 y2 ff2 fs0 fc0 sc0 ls0 ws0">在无线<span class="_ _1"></span>传感器<span class="_ _1"></span>网络（<span class="_ _1"></span><span class="ff1">WSN</span>）中<span class="_ _1"></span>，节点<span class="_ _1"></span>的定位<span class="_ _1"></span>是一个<span class="_ _1"></span>至关重<span class="_ _1"></span>要的环<span class="_ _1"></span>节。今<span class="_ _1"></span>天，我<span class="_ _1"></span>们将聚<span class="_ _1"></span>焦于</div><div class="t m0 x1 h2 y3 ff2 fs0 fc0 sc0 ls0 ws0">一种<span class="_ _1"></span>新兴<span class="_ _1"></span>的定<span class="_ _1"></span>位算<span class="_ _1"></span>法<span class="ff1">——</span>基于<span class="_ _1"></span>麻雀<span class="_ _1"></span>搜索<span class="_ _1"></span>算法<span class="_ _1"></span>的<span class="_ _0"> </span><span class="ff1">3D-Dvhop<span class="_"> </span></span>定位<span class="_ _1"></span>算法<span class="_ _1"></span>。这<span class="_ _1"></span>种算<span class="_ _1"></span>法在<span class="_ _1"></span>三维<span class="_ _1"></span>空间</div><div class="t m0 x1 h2 y4 ff2 fs0 fc0 sc0 ls0 ws0">中，<span class="_ _2"></span>通过麻雀搜索算法寻找未知节点到锚节点的实际距离和估计距离之间的最小误差，<span class="_ _2"></span>从而</div><div class="t m0 x1 h2 y5 ff2 fs0 fc0 sc0 ls0 ws0">实现节点位置的精确估计。</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">---</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">一、背景介绍</span>**</div><div class="t m0 x1 h2 y8 ff2 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff1">WSN<span class="_ _0"> </span></span>中，<span class="_ _3"></span>节点常常部署在复杂的三维环境中。<span class="_ _3"></span>对于这些节点来说，<span class="_ _3"></span>能够准确知道自己所</div><div class="t m0 x1 h2 y9 ff2 fs0 fc0 sc0 ls0 ws0">处的<span class="_ _1"></span>位置<span class="_ _1"></span>是进行<span class="_ _1"></span>数据<span class="_ _1"></span>传输<span class="_ _1"></span>、协<span class="_ _1"></span>同工作<span class="_ _1"></span>等任<span class="_ _1"></span>务的<span class="_ _1"></span>前提<span class="_ _1"></span>。传统<span class="_ _1"></span>的<span class="_ _0"> </span><span class="ff1">3D-Dvhop<span class="_"> </span></span>定位<span class="_ _1"></span>算法<span class="_ _1"></span>虽然<span class="_ _1"></span>能够</div><div class="t m0 x1 h2 ya ff2 fs0 fc0 sc0 ls0 ws0">提供一定的定位服务，<span class="_ _4"></span>但在复杂环境中往往难以达到理想的定位精度。<span class="_ _4"></span>因此，<span class="_ _4"></span>我们引入了麻</div><div class="t m0 x1 h2 yb ff2 fs0 fc0 sc0 ls0 ws0">雀搜索算法，希望通过其优秀的寻优能力，提升<span class="_ _0"> </span><span class="ff1">WSN<span class="_ _0"> </span></span>节点的定位精度。</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">---</div><div class="t m0 x1 h2 yd ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">二、麻雀搜索算法简介</span>**</div><div class="t m0 x1 h2 ye ff2 fs0 fc0 sc0 ls0 ws0">麻雀搜索算法是一种仿生优化算法，<span class="_ _2"></span>其灵感来源于麻雀在寻找食物过程中的智慧行为。<span class="_ _2"></span>该算</div><div class="t m0 x1 h2 yf ff2 fs0 fc0 sc0 ls0 ws0">法能够在多维空间中，<span class="_ _5"></span>通过模拟麻雀的觅食行为，<span class="_ _5"></span>快速寻找到最优解。<span class="_ _5"></span>其特点是搜索范围广、</div><div class="t m0 x1 h2 y10 ff2 fs0 fc0 sc0 ls0 ws0">收敛速度快，非常适合解决<span class="_ _0"> </span><span class="ff1">WSN<span class="_ _0"> </span></span>节点定位这类优化问题。</div><div class="t m0 x1 h2 y11 ff1 fs0 fc0 sc0 ls0 ws0">---</div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">三、</span>SSA-3D-Dvhop<span class="_ _0"> </span><span class="ff2">算法实现</span>**</div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff1">SSA-3D-Dvhop<span class="_"> </span></span>算法中<span class="_ _1"></span>，我们<span class="_ _1"></span>首先利用<span class="_ _1"></span>麻雀搜<span class="_ _1"></span>索算法<span class="_ _1"></span>在三维<span class="_ _1"></span>空间中<span class="_ _1"></span>寻找未知<span class="_ _1"></span>节点到<span class="_ _1"></span>锚节</div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">点的实际距离和估计距离之间的最小误差。<span class="_ _2"></span>这个过程中，<span class="_ _2"></span>麻雀搜索算法能够快速地找到一组</div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">最优的参数，使得未知节点位置的估计误差最小。</div><div class="t m0 x1 h2 y16 ff2 fs0 fc0 sc0 ls0 ws0">接着<span class="_ _1"></span>，我<span class="_ _1"></span>们利<span class="_ _1"></span>用这<span class="_ _1"></span>组参<span class="_ _1"></span>数，<span class="_ _1"></span>通过<span class="_ _6"> </span><span class="ff1">3D-Dvhop<span class="_"> </span></span>算法计<span class="_ _1"></span>算未<span class="_ _1"></span>知节<span class="_ _1"></span>点的<span class="_ _1"></span>位置<span class="_ _1"></span>。相<span class="_ _1"></span>较于<span class="_ _1"></span>原始<span class="_ _1"></span>的<span class="_ _0"> </span><span class="ff1">3D-</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">Dvhop<span class="_"> </span><span class="ff2">算法，<span class="_ _1"></span></span>SSA-3D-Dvhop<span class="_"> </span><span class="ff2">算法<span class="_ _1"></span>通过引<span class="_ _1"></span>入麻<span class="_ _1"></span>雀搜<span class="_ _1"></span>索算<span class="_ _1"></span>法，<span class="_ _1"></span>能够<span class="_ _1"></span>更准<span class="_ _1"></span>确地<span class="_ _1"></span>估计<span class="_ _1"></span>节点的<span class="_ _1"></span>位置<span class="_ _1"></span>。</span></div><div class="t m0 x1 h2 y18 ff1 fs0 fc0 sc0 ls0 ws0">---</div><div class="t m0 x1 h2 y19 ff1 fs0 fc0 sc0 ls0 ws0">**<span class="ff2">四、实验对比</span>**</div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">为了验<span class="_ _1"></span>证<span class="_ _0"> </span><span class="ff1">SSA-3D-Dvhop<span class="_"> </span></span>算法的<span class="_ _1"></span>有效性，<span class="_ _1"></span>我们进<span class="_ _1"></span>行了大<span class="_ _1"></span>量的实<span class="_ _1"></span>验。实<span class="_ _1"></span>验中，我<span class="_ _1"></span>们分别<span class="_ _1"></span>使用</div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">了原始的<span class="_ _0"> </span><span class="ff1">3D-Dvhop<span class="_ _0"> </span></span>算法和<span class="_ _0"> </span><span class="ff1">SSA-3D-Dvhop<span class="_ _0"> </span></span>算法对<span class="_ _0"> </span><span class="ff1">WSN<span class="_ _0"> </span></span>节点进行定位。<span class="_ _7"></span>通过对比两种算法</div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">的定位<span class="_ _1"></span>精度、<span class="_ _1"></span>收敛速<span class="_ _1"></span>度等指<span class="_ _1"></span>标，我们<span class="_ _1"></span>发现<span class="_ _0"> </span><span class="ff1">SSA-3D-Dvhop<span class="_"> </span></span>算法<span class="_ _1"></span>在各个<span class="_ _1"></span>方面都表<span class="_ _1"></span>现出明<span class="_ _1"></span>显的</div><div class="t m0 x1 h2 y1d ff2 fs0 fc0 sc0 ls0 ws0">优势。</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>