声学技术
聲學技術
성학기술
Technical Acousitics
2013年
6期
458-463
,共6页
罗胜男%付广义%贺旭%李宇%尹力
囉勝男%付廣義%賀旭%李宇%尹力
라성남%부엄의%하욱%리우%윤력
分布式MIMO声纳系统%定位精度%修正时延的扩展卡尔曼滤波%目标跟踪
分佈式MIMO聲納繫統%定位精度%脩正時延的擴展卡爾曼濾波%目標跟蹤
분포식MIMO성납계통%정위정도%수정시연적확전잡이만려파%목표근종
distributed MIMO sonar system%accuracy of localization%Modified Extended Kalman Filter (MEKF)%target tracking
分布式多输入多输出(Multiple-Input Multiple-Output, MIMO)声纳是一种通过MIMO技术规划时空信道来提高声纳探测性能的新型主动探测声纳体制。由于分布式MIMO声纳节点分布间隔大,水中声速较小,由各发射节点同步发射的测距信号将经过不同的时延到达目标,因此各接收节点测得的距离值分别对应于目标不同时刻的状态。常规的定位方法并没有考虑传播时延对测量值的影响,因而定位精度受到限制。提出了一种修正时延的扩展卡尔曼滤波方法(Modified Extended Kalman Filter, MEKF)对分布式MIMO声纳系统中的移动目标进行跟踪。仿真结果表明,与常规的目标定位跟踪方法相比,该方法有定位精度高、收敛速度快、跟踪性能稳定的特点。
分佈式多輸入多輸齣(Multiple-Input Multiple-Output, MIMO)聲納是一種通過MIMO技術規劃時空信道來提高聲納探測性能的新型主動探測聲納體製。由于分佈式MIMO聲納節點分佈間隔大,水中聲速較小,由各髮射節點同步髮射的測距信號將經過不同的時延到達目標,因此各接收節點測得的距離值分彆對應于目標不同時刻的狀態。常規的定位方法併沒有攷慮傳播時延對測量值的影響,因而定位精度受到限製。提齣瞭一種脩正時延的擴展卡爾曼濾波方法(Modified Extended Kalman Filter, MEKF)對分佈式MIMO聲納繫統中的移動目標進行跟蹤。倣真結果錶明,與常規的目標定位跟蹤方法相比,該方法有定位精度高、收斂速度快、跟蹤性能穩定的特點。
분포식다수입다수출(Multiple-Input Multiple-Output, MIMO)성납시일충통과MIMO기술규화시공신도래제고성납탐측성능적신형주동탐측성납체제。유우분포식MIMO성납절점분포간격대,수중성속교소,유각발사절점동보발사적측거신호장경과불동적시연도체목표,인차각접수절점측득적거리치분별대응우목표불동시각적상태。상규적정위방법병몰유고필전파시연대측량치적영향,인이정위정도수도한제。제출료일충수정시연적확전잡이만려파방법(Modified Extended Kalman Filter, MEKF)대분포식MIMO성납계통중적이동목표진행근종。방진결과표명,여상규적목표정위근종방법상비,해방법유정위정도고、수렴속도쾌、근종성능은정적특점。
The distributed Multiple-Input Multiple-Output (MIMO) sonar system is new active detection sonar that offers detection improvement by planning space-time channels. The stations in such system are widely separated and the un-derwater sound velocity is small. There are different time delays for the signals launched from different transmitters to reach the target. Thereore, the distance measured at each receiver corresponds to the status of target at different time. The accuracy of common localization methods is limited because the time delay for transmission has not been taken into account. In this paper, a Modified Extended Kalman Filter method (MEKF) based on delay correction was proposed to deal with this target tracking problem. Simulation results showed that the MEKF method could provide higher accuracy, faster convergence and steadier performance.